



                    Biopython Tutorial and Cookbook
                    *******************************
      Jeff Chang, Brad Chapman, Iddo Friedberg, Thomas Hamelryck,
      ===========================================================
    Michiel de Hoon, Peter Cock, Tiago Antao, Eric Talevich, Bartek
    ===============================================================
                               Wilczynski
                               ==========
                             Biopython 1.79
                             ==============


Contents
********


   - Chapter 1 Introduction 

      - 1.1 What is Biopython? 
      - 1.2 What can I find in the Biopython package 
      - 1.3 Installing Biopython 
      - 1.4 Frequently Asked Questions (FAQ) 

   - Chapter 2 Quick Start -- What can you do with Biopython? 

      - 2.1 General overview of what Biopython provides 
      - 2.2 Working with sequences 
      - 2.3 A usage example 
      - 2.4 Parsing sequence file formats 

         - 2.4.1 Simple FASTA parsing example 
         - 2.4.2 Simple GenBank parsing example 
         - 2.4.3 I love parsing -- please don't stop talking about it! 

      - 2.5 Connecting with biological databases 
      - 2.6 What to do next 

   - Chapter 3 Sequence objects 

      - 3.1 Sequences act like strings 
      - 3.2 Slicing a sequence 
      - 3.3 Turning Seq objects into strings 
      - 3.4 Concatenating or adding sequences 
      - 3.5 Changing case 
      - 3.6 Nucleotide sequences and (reverse) complements 
      - 3.7 Transcription 
      - 3.8 Translation 
      - 3.9 Translation Tables 
      - 3.10 Comparing Seq objects 
      - 3.11 Sequences with unknown sequence contents 
      - 3.12 MutableSeq objects 
      - 3.13 UnknownSeq objects 
      - 3.14 Working with strings directly 

   - Chapter 4 Sequence annotation objects 

      - 4.1 The SeqRecord object 
      - 4.2 Creating a SeqRecord 

         - 4.2.1 SeqRecord objects from scratch 
         - 4.2.2 SeqRecord objects from FASTA files 
         - 4.2.3 SeqRecord objects from GenBank files 

      - 4.3 Feature, location and position objects 

         - 4.3.1 SeqFeature objects 
         - 4.3.2 Positions and locations 
         - 4.3.3 Sequence described by a feature or location 

      - 4.4 Comparison 
      - 4.5 References 
      - 4.6 The format method 
      - 4.7 Slicing a SeqRecord 
      - 4.8 Adding SeqRecord objects 
      - 4.9 Reverse-complementing SeqRecord objects 

   - Chapter 5 Sequence Input/Output 

      - 5.1 Parsing or Reading Sequences 

         - 5.1.1 Reading Sequence Files 
         - 5.1.2 Iterating over the records in a sequence file 
         - 5.1.3 Getting a list of the records in a sequence file 
         - 5.1.4 Extracting data 
         - 5.1.5 Modifying data 

      - 5.2 Parsing sequences from compressed files 
      - 5.3 Parsing sequences from the net 

         - 5.3.1 Parsing GenBank records from the net 
         - 5.3.2 Parsing SwissProt sequences from the net 

      - 5.4 Sequence files as Dictionaries 

         - 5.4.1 Sequence files as Dictionaries -- In memory 
         - 5.4.2 Sequence files as Dictionaries -- Indexed files 
         - 5.4.3 Sequence files as Dictionaries -- Database indexed
         files 
         - 5.4.4 Indexing compressed files 
         - 5.4.5 Discussion 

      - 5.5 Writing Sequence Files 

         - 5.5.1 Round trips 
         - 5.5.2 Converting between sequence file formats 
         - 5.5.3 Converting a file of sequences to their reverse
         complements 
         - 5.5.4 Getting your SeqRecord objects as formatted strings 

      - 5.6 Low level FASTA and FASTQ parsers 

   - Chapter 6 Multiple Sequence Alignment objects 

      - 6.1 Parsing or Reading Sequence Alignments 

         - 6.1.1 Single Alignments 
         - 6.1.2 Multiple Alignments 
         - 6.1.3 Ambiguous Alignments 

      - 6.2 Writing Alignments 

         - 6.2.1 Converting between sequence alignment file formats 
         - 6.2.2 Getting your alignment objects as formatted strings 

      - 6.3 Manipulating Alignments 

         - 6.3.1 Slicing alignments 
         - 6.3.2 Alignments as arrays 

      - 6.4 Getting information on the alignment 

         - 6.4.1 Substitutions 

      - 6.5 Alignment Tools 

         - 6.5.1 ClustalW 
         - 6.5.2 MUSCLE 
         - 6.5.3 MUSCLE using stdout 
         - 6.5.4 MUSCLE using stdin and stdout 
         - 6.5.5 EMBOSS needle and water 

      - 6.6 Pairwise sequence alignment 

         - 6.6.1 pairwise2 
         - 6.6.2 PairwiseAligner 

      - 6.7 Substitution matrices 

   - Chapter 7 BLAST 

      - 7.1 Running BLAST over the Internet 
      - 7.2 Running BLAST locally 

         - 7.2.1 Introduction 
         - 7.2.2 Standalone NCBI BLAST+ 
         - 7.2.3 Other versions of BLAST 

      - 7.3 Parsing BLAST output 
      - 7.4 The BLAST record class 
      - 7.5 Dealing with PSI-BLAST 
      - 7.6 Dealing with RPS-BLAST 

   - Chapter 8 BLAST and other sequence search tools 

      - 8.1 The SearchIO object model 

         - 8.1.1 QueryResult 
         - 8.1.2 Hit 
         - 8.1.3 HSP 
         - 8.1.4 HSPFragment 

      - 8.2 A note about standards and conventions 
      - 8.3 Reading search output files 
      - 8.4 Dealing with large search output files with indexing 
      - 8.5 Writing and converting search output files 

   - Chapter 9 Accessing NCBI's Entrez databases 

      - 9.1 Entrez Guidelines 
      - 9.2 EInfo: Obtaining information about the Entrez databases 
      - 9.3 ESearch: Searching the Entrez databases 
      - 9.4 EPost: Uploading a list of identifiers 
      - 9.5 ESummary: Retrieving summaries from primary IDs 
      - 9.6 EFetch: Downloading full records from Entrez 
      - 9.7 ELink: Searching for related items in NCBI Entrez 
      - 9.8 EGQuery: Global Query - counts for search terms 
      - 9.9 ESpell: Obtaining spelling suggestions 
      - 9.10 Parsing huge Entrez XML files 
      - 9.11 HTML escape characters 
      - 9.12 Handling errors 
      - 9.13 Specialized parsers 

         - 9.13.1 Parsing Medline records 
         - 9.13.2 Parsing GEO records 
         - 9.13.3 Parsing UniGene records 

      - 9.14 Using a proxy 
      - 9.15 Examples 

         - 9.15.1 PubMed and Medline 
         - 9.15.2 Searching, downloading, and parsing Entrez Nucleotide
         records 
         - 9.15.3 Searching, downloading, and parsing GenBank records 
         - 9.15.4 Finding the lineage of an organism 

      - 9.16 Using the history and WebEnv 

         - 9.16.1 Searching for and downloading sequences using the
         history 
         - 9.16.2 Searching for and downloading abstracts using the
         history 
         - 9.16.3 Searching for citations 


   - Chapter 10 Swiss-Prot and ExPASy 

      - 10.1 Parsing Swiss-Prot files 

         - 10.1.1 Parsing Swiss-Prot records 
         - 10.1.2 Parsing the Swiss-Prot keyword and category list 

      - 10.2 Parsing Prosite records 
      - 10.3 Parsing Prosite documentation records 
      - 10.4 Parsing Enzyme records 
      - 10.5 Accessing the ExPASy server 

         - 10.5.1 Retrieving a Swiss-Prot record 
         - 10.5.2 Searching Swiss-Prot 
         - 10.5.3 Retrieving Prosite and Prosite documentation records 

      - 10.6 Scanning the Prosite database 

   - Chapter 11 Going 3D: The PDB module 

      - 11.1 Reading and writing crystal structure files 

         - 11.1.1 Reading an mmCIF file 
         - 11.1.2 Reading files in the MMTF format 
         - 11.1.3 Reading a PDB file 
         - 11.1.4 Reading a PQR file 
         - 11.1.5 Reading files in the PDB XML format 
         - 11.1.6 Writing mmCIF files 
         - 11.1.7 Writing PDB files 
         - 11.1.8 Writing PQR files 
         - 11.1.9 Writing MMTF files 

      - 11.2 Structure representation 

         - 11.2.1 Structure 
         - 11.2.2 Model 
         - 11.2.3 Chain 
         - 11.2.4 Residue 
         - 11.2.5 Atom 
         - 11.2.6 Extracting a specific Atom/Residue/Chain/Model from a
         Structure 

      - 11.3 Disorder 

         - 11.3.1 General approach 
         - 11.3.2 Disordered atoms 
         - 11.3.3 Disordered residues 

      - 11.4 Hetero residues 

         - 11.4.1 Associated problems 
         - 11.4.2 Water residues 
         - 11.4.3 Other hetero residues 

      - 11.5 Navigating through a Structure object 
      - 11.6 Analyzing structures 

         - 11.6.1 Measuring distances 
         - 11.6.2 Measuring angles 
         - 11.6.3 Measuring torsion angles 
         - 11.6.4 Internal coordinates for standard residues 
         - 11.6.5 Determining atom-atom contacts 
         - 11.6.6 Superimposing two structures 
         - 11.6.7 Mapping the residues of two related structures onto
         each other 
         - 11.6.8 Calculating the Half Sphere Exposure 
         - 11.6.9 Determining the secondary structure 
         - 11.6.10 Calculating the residue depth 

      - 11.7 Common problems in PDB files 

         - 11.7.1 Examples 
         - 11.7.2 Automatic correction 
         - 11.7.3 Fatal errors 

      - 11.8 Accessing the Protein Data Bank 

         - 11.8.1 Downloading structures from the Protein Data Bank 
         - 11.8.2 Downloading the entire PDB 
         - 11.8.3 Keeping a local copy of the PDB up to date 

      - 11.9 General questions 

         - 11.9.1 How well tested is Bio.PDB? 
         - 11.9.2 How fast is it? 
         - 11.9.3 Is there support for molecular graphics? 
         - 11.9.4 Who's using Bio.PDB? 


   - Chapter 12 Bio.PopGen: Population genetics 

      - 12.1 GenePop 

   - Chapter 13 Phylogenetics with Bio.Phylo 

      - 13.1 Demo: What's in a Tree? 

         - 13.1.1 Coloring branches within a tree 

      - 13.2 I/O functions 
      - 13.3 View and export trees 
      - 13.4 Using Tree and Clade objects 

         - 13.4.1 Search and traversal methods 
         - 13.4.2 Information methods 
         - 13.4.3 Modification methods 
         - 13.4.4 Features of PhyloXML trees 

      - 13.5 Running external applications 
      - 13.6 PAML integration 
      - 13.7 Future plans 

   - Chapter 14 Sequence motif analysis using Bio.motifs 

      - 14.1 Motif objects 

         - 14.1.1 Creating a motif from instances 
         - 14.1.2 Creating a sequence logo 

      - 14.2 Reading motifs 

         - 14.2.1 JASPAR 
         - 14.2.2 MEME 
         - 14.2.3 TRANSFAC 

      - 14.3 Writing motifs 
      - 14.4 Position-Weight Matrices 
      - 14.5 Position-Specific Scoring Matrices 
      - 14.6 Searching for instances 

         - 14.6.1 Searching for exact matches 
         - 14.6.2 Searching for matches using the PSSM score 
         - 14.6.3 Selecting a score threshold 

      - 14.7 Each motif object has an associated Position-Specific
      Scoring Matrix 
      - 14.8 Comparing motifs 
      - 14.9 De novo motif finding 

         - 14.9.1 MEME 

      - 14.10 Useful links 

   - Chapter 15 Cluster analysis 

      - 15.1 Distance functions 
      - 15.2 Calculating cluster properties 
      - 15.3 Partitioning algorithms 
      - 15.4 Hierarchical clustering 
      - 15.5 Self-Organizing Maps 
      - 15.6 Principal Component Analysis 
      - 15.7 Handling Cluster/TreeView-type files 
      - 15.8 Example calculation 

   - Chapter 16 Supervised learning methods 

      - 16.1 The Logistic Regression Model 

         - 16.1.1 Background and Purpose 





Chapter 1  Introduction
***********************



1.1 What is Biopython?
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  The Biopython Project is an international association of developers of
freely available Python (https://www.python.org) tools for computational
molecular biology. Python is an object oriented, interpreted, flexible
language that is becoming increasingly popular for scientific computing.
Python is easy to learn, has a very clear syntax and can easily be
extended with modules written in C, C++ or FORTRAN.
  The Biopython web site (http://www.biopython.org) provides an online
resource for modules, scripts, and web links for developers of
Python-based software for bioinformatics use and research. Basically,
the goal of Biopython is to make it as easy as possible to use Python
for bioinformatics by creating high-quality, reusable modules and
classes. Biopython features include parsers for various Bioinformatics
file formats (BLAST, Clustalw, FASTA, Genbank,...), access to online
services (NCBI, Expasy,...), interfaces to common and not-so-common
programs (Clustalw, DSSP, MSMS...), a standard sequence class, various
clustering modules, a KD tree data structure etc. and even
documentation.
  Basically, we just like to program in Python and want to make it as
easy as possible to use Python for bioinformatics by creating
high-quality, reusable modules and scripts.


1.2 What can I find in the Biopython package
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  The main Biopython releases have lots of functionality, including:


   - The ability to parse bioinformatics files into Python utilizable
   data structures, including support for the following formats:


      - Blast output -- both from standalone and WWW Blast 
      - Clustalw 
      - FASTA 
      - GenBank 
      - PubMed and Medline 
      - ExPASy files, like Enzyme and Prosite 
      - SCOP, including `dom' and `lin' files 
      - UniGene 
      - SwissProt 


   - Files in the supported formats can be iterated over record by
   record or indexed and accessed via a Dictionary interface.

   - Code to deal with popular on-line bioinformatics destinations such
   as:


      - NCBI -- Blast, Entrez and PubMed services 
      - ExPASy -- Swiss-Prot and Prosite entries, as well as Prosite
      searches 


   - Interfaces to common bioinformatics programs such as:


      - Standalone Blast from NCBI 
      - Clustalw alignment program 
      - EMBOSS command line tools 


   - A standard sequence class that deals with sequences, ids on
   sequences, and sequence features.

   - Tools for performing common operations on sequences, such as
   translation, transcription and weight calculations.

   - Code to perform classification of data using k Nearest Neighbors,
   Naive Bayes or Support Vector Machines.

   - Code for dealing with alignments, including a standard way to
   create and deal with substitution matrices.

   - Code making it easy to split up parallelizable tasks into separate
   processes.

   - GUI-based programs to do basic sequence manipulations,
   translations, BLASTing, etc.

   - Extensive documentation and help with using the modules, including
   this file, on-line wiki documentation, the web site, and the mailing
   list.

   - Integration with BioSQL, a sequence database schema also supported
   by the BioPerl and BioJava projects.

  We hope this gives you plenty of reasons to download and start using
Biopython!


1.3 Installing Biopython
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  All of the installation information for Biopython was separated from
this document to make it easier to keep updated.
  The short version is use pip install biopython, see the main
README (1) file for other options.


1.4 Frequently Asked Questions (FAQ)
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   1. How do I cite Biopython in a scientific publication?
  Please cite our application note [, Cock et al., 2009] as the main
   Biopython reference. In addition, please cite any publications from
   the following list if appropriate, in particular as a reference for
   specific modules within Biopython (more information can be found on
   our website): 

      - For the official project announcement: [, Chapman and Chang,
      2000]; 
      - For Bio.PDB: [, Hamelryck and Manderick, 2003]; 
      - For Bio.Cluster: [, De Hoon et al., 2004]; 
      - For Bio.Graphics.GenomeDiagram: [, Pritchard et al., 2006]; 
      - For Bio.Phylo and Bio.Phylo.PAML: [, Talevich et al., 2012]; 
      - For the FASTQ file format as supported in Biopython, BioPerl,
      BioRuby, BioJava, and EMBOSS: [, Cock et al., 2010]. 


   2. How should I capitalize "Biopython"? Is "BioPython" OK?
  The correct capitalization is "Biopython", not "BioPython" (even
   though that would have matched BioPerl, BioJava and BioRuby).

   3. How is the Biopython software licensed?
  Biopython is distributed under the Biopython License Agreement.
   However, since the release of Biopython 1.69, some files are
   explicitly dual licensed under your choice of the Biopython License
   Agreement or the BSD 3-Clause License. This is with the intention of
   later offering all of Biopython under this dual licensing approach.

   4. What is the Biopython logo and how is it licensed?
  As of July 2017 and the Biopython 1.70 release, the Biopython logo is
   a yellow and blue snake forming a double helix above the word
   "biopython" in lower case. It was designed by Patrick Kunzmann and
   this logo is dual licensed under your choice of the Biopython License
   Agreement or the BSD 3-Clause License.

 Prior to this, the Biopython logo was two yellow snakes forming a
   double helix around the word "BIOPYTHON", designed by Henrik
   Vestergaard and Thomas Hamelryck in 2003 as part of an open
   competition.


   5. Do you have a change-log listing what's new in each release?
  See the file NEWS.rst included with the source code (originally called
   just NEWS), or read the latest NEWS file on GitHub (2).

   6. What is going wrong with my print commands?
  As of Biopython 1.77, we only support Python 3, so this tutorial uses
   the Python 3 style print function.

   7. How do I find out what version of Biopython I have installed?
  Use this: 
   <<>>> import Bio
     >>> print(Bio.__version__)
     ...
   >>
  If the "import Bio" line fails, Biopython is not installed. Note that
   those are double underscores before and after version. If the second
   line fails, your version is very out of date.
 If the version string ends with a plus like "1.66+", you don't have an
   official release, but an old snapshot of the in development code
   after that version was released. This naming was used until June 2016
   in the run-up to Biopython 1.68.
 If the version string ends with ".dev<number>" like "1.68.dev0", again
   you don't have an official release, but instead a snapshot of the in
   development code before that version was released.

   8. Where is the latest version of this document?
  If you download a Biopython source code archive, it will include the
   relevant version in both HTML and PDF formats. The latest published
   version of this document (updated at each release) is online: 

      - http://biopython.org/DIST/docs/tutorial/Tutorial.html 
      - http://biopython.org/DIST/docs/tutorial/Tutorial.pdf 


   9. What is wrong with my sequence comparisons?
  There was a major change in Biopython 1.65 making the Seq and
   MutableSeq classes (and subclasses) use simple string-based
   comparison which you can do explicitly with str(seq1) == str(seq2).
 Older versions of Biopython would use instance-based comparison for Seq
   objects which you can do explicitly with id(seq1) == id(seq2).
 If you still need to support old versions of Biopython, use these
   explicit forms to avoid problems. See Section ??.

   10. What file formats do Bio.SeqIO and Bio.AlignIO read and write?
  Check the built in docstrings (from Bio import SeqIO, then
   help(SeqIO)), or see http://biopython.org/wiki/SeqIO and
   http://biopython.org/wiki/AlignIO on the wiki for the latest listing.

   11. Why won't the  Bio.SeqIO and Bio.AlignIO functions parse, read
   and write take filenames? They insist on handles!
  You need Biopython 1.54 or later, or just use handles explicitly (see
   Section ??). It is especially important to remember to close output
   handles explicitly after writing your data.

   12. Why won't the  Bio.SeqIO.write() and Bio.AlignIO.write()
   functions accept a single record or alignment? They insist on a list
   or iterator!
  You need Biopython 1.54 or later, or just wrap the item with [...] to
   create a list of one element.

   13. Why doesn't str(...) give me the full sequence of a Seq object?
  You need Biopython 1.45 or later.

   14. Why doesn't Bio.Blast work with the latest plain text NCBI blast
   output?
  The NCBI keep tweaking the plain text output from the BLAST tools, and
   keeping our parser up to date is/was an ongoing struggle. If you
   aren't using the latest version of Biopython, you could try
   upgrading. However, we (and the NCBI) recommend you use the XML
   output instead, which is designed to be read by a computer program.

   15. Why has my script using Bio.Entrez.efetch() stopped working?
  This could be due to NCBI changes in February 2012 introducing EFetch
   2.0. First, they changed the default return modes - you probably want
   to add retmode="text" to your call. Second, they are now stricter
   about how to provide a list of IDs -- Biopython 1.59 onwards turns a
   list into a comma separated string automatically.

   16. Why doesn't Bio.Blast.NCBIWWW.qblast() give the same results as
   the NCBI BLAST website?
  You need to specify the same options -- the NCBI often adjust the
   default settings on the website, and they do not match the QBLAST
   defaults anymore. Check things like the gap penalties and expectation
   threshold.

   17. Why can't I add SeqRecord objects together?
  You need Biopython 1.53 or later.

   18. Why doesn't Bio.SeqIO.index_db() work? The module imports fine
   but there is no index_db function!
  You need Biopython 1.57 or later (and a Python with SQLite3 support).

   19. Where is the MultipleSeqAlignment object? The Bio.Align module
   imports fine but this class isn't there!
  You need Biopython 1.54 or later. Alternatively, the older
   Bio.Align.Generic.Alignment class supports some of its functionality,
   but using this is now discouraged.

   20. Why can't I run command line tools directly from the application
   wrappers?
  You need Biopython 1.55 or later, but these were deprecated in
   Biopython 1.78. Consider using the Python subprocess module directly.

   21. I looked in a directory for code, but I couldn't find the code
   that does something. Where's it hidden?
  One thing to know is that we put code in __init__.py files. If you are
   not used to looking for code in this file this can be confusing. The
   reason we do this is to make the imports easier for users. For
   instance, instead of having to do a "repetitive" import like from
   Bio.GenBank import GenBank, you can just use from Bio import GenBank.

   22. Why doesn't Bio.Fasta work?
  We deprecated the Bio.Fasta module in Biopython 1.51 (August 2009) and
   removed it in Biopython 1.55 (August 2010). There is a brief example
   showing how to convert old code to use Bio.SeqIO instead in the
   DEPRECATED.rst (3) file.

  For more general questions, the Python FAQ pages
https://docs.python.org/3/faq/index.html may be useful.   
-----------------------------------


 (1) https://github.com/biopython/biopython/blob/master/README.rst
 (2) https://github.com/biopython/biopython/blob/master/NEWS.rst
 (3) https://github.com/biopython/biopython/blob/master/DEPRECATED.rst


Chapter 2  Quick Start -- What can you do with Biopython?
*********************************************************

  This section is designed to get you started quickly with Biopython,
and to give a general overview of what is available and how to use it.
All of the examples in this section assume that you have some general
working knowledge of Python, and that you have successfully installed
Biopython on your system. If you think you need to brush up on your
Python, the main Python web site provides quite a bit of free
documentation to get started with (https://docs.python.org/3/).
  Since much biological work on the computer involves connecting with
databases on the internet, some of the examples will also require a
working internet connection in order to run.
  Now that that is all out of the way, let's get into what we can do
with Biopython.


2.1 General overview of what Biopython provides
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  As mentioned in the introduction, Biopython is a set of libraries to
provide the ability to deal with "things" of interest to biologists
working on the computer. In general this means that you will need to
have at least some programming experience (in Python, of course!) or at
least an interest in learning to program. Biopython's job is to make
your job easier as a programmer by supplying reusable libraries so that
you can focus on answering your specific question of interest, instead
of focusing on the internals of parsing a particular file format (of
course, if you want to help by writing a parser that doesn't exist and
contributing it to Biopython, please go ahead!). So Biopython's job is
to make you happy!
  One thing to note about Biopython is that it often provides multiple
ways of "doing the same thing." Things have improved in recent releases,
but this can still be frustrating as in Python there should ideally be
one right way to do something. However, this can also be a real benefit
because it gives you lots of flexibility and control over the libraries.
The tutorial helps to show you the common or easy ways to do things so
that you can just make things work. To learn more about the alternative
possibilities, look in the Cookbook (Chapter ??, this has some cools
tricks and tips), the Advanced section (Chapter ??), the built in
"docstrings" (via the Python help command, or the API documentation (1))
or ultimately the code itself.


2.2 Working with sequences
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  Disputably (of course!), the central object in bioinformatics is the
sequence. Thus, we'll start with a quick introduction to the Biopython
mechanisms for dealing with sequences, the Seq object, which we'll
discuss in more detail in Chapter ??.
  Most of the time when we think about sequences we have in my mind a
string of letters like `AGTACACTGGT'. You can create such Seq object
with this sequence as follows - the ">>>" represents the Python prompt
followed by what you would type in:
<<>>> from Bio.Seq import Seq
  >>> my_seq = Seq("AGTACACTGGT")
  >>> my_seq
  Seq('AGTACACTGGT')
  >>> print(my_seq)
  AGTACACTGGT
>>

  The Seq object differs from the Python string in the methods it
supports. You can't do this with a plain string:
<<>>> my_seq
  Seq('AGTACACTGGT')
  >>> my_seq.complement()
  Seq('TCATGTGACCA')
  >>> my_seq.reverse_complement()
  Seq('ACCAGTGTACT')
>>

  The next most important class is the SeqRecord or Sequence Record.
This holds a sequence (as a Seq object) with additional annotation
including an identifier, name and description. The Bio.SeqIO module for
reading and writing sequence file formats works with SeqRecord objects,
which will be introduced below and covered in more detail by Chapter ??.
  This covers the basic features and uses of the Biopython sequence
class. Now that you've got some idea of what it is like to interact with
the Biopython libraries, it's time to delve into the fun, fun world of
dealing with biological file formats!


2.3 A usage example
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  Before we jump right into parsers and everything else to do with
Biopython, let's set up an example to motivate everything we do and make
life more interesting. After all, if there wasn't any biology in this
tutorial, why would you want you read it?
  Since I love plants, I think we're just going to have to have a plant
based example (sorry to all the fans of other organisms out there!).
Having just completed a recent trip to our local greenhouse, we've
suddenly developed an incredible obsession with Lady Slipper Orchids (if
you wonder why, have a look at some Lady Slipper Orchids photos on
Flickr (2), or try a Google Image Search (3)).
  Of course, orchids are not only beautiful to look at, they are also
extremely interesting for people studying evolution and systematics. So
let's suppose we're thinking about writing a funding proposal to do a
molecular study of Lady Slipper evolution, and would like to see what
kind of research has already been done and how we can add to that.
  After a little bit of reading up we discover that the Lady Slipper
Orchids are in the Orchidaceae family and the Cypripedioideae sub-family
and are made up of 5 genera: Cypripedium, Paphiopedilum, Phragmipedium,
Selenipedium and Mexipedium.
  That gives us enough to get started delving for more information. So,
let's look at how the Biopython tools can help us. We'll start with
sequence parsing in Section ??, but the orchids will be back later on as
well - for example we'll search PubMed for papers about orchids and
extract sequence data from GenBank in Chapter ??, extract data from
Swiss-Prot from certain orchid proteins in Chapter ??, and work with
ClustalW multiple sequence alignments of orchid proteins in Section ??.


2.4 Parsing sequence file formats
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*


  A large part of much bioinformatics work involves dealing with the
many types of file formats designed to hold biological data. These files
are loaded with interesting biological data, and a special challenge is
parsing these files into a format so that you can manipulate them with
some kind of programming language. However the task of parsing these
files can be frustrated by the fact that the formats can change quite
regularly, and that formats may contain small subtleties which can break
even the most well designed parsers.
  We are now going to briefly introduce the Bio.SeqIO module -- you can
find out more in Chapter ??. We'll start with an online search for our
friends, the lady slipper orchids. To keep this introduction simple,
we're just using the NCBI website by hand. Let's just take a look
through the nucleotide databases at NCBI, using an Entrez online search
(https://www.ncbi.nlm.nih.gov/nuccore/?term=Cypripedioideae) for
everything mentioning the text Cypripedioideae (this is the subfamily of
lady slipper orchids).
  When this tutorial was originally written, this search gave us only 94
hits, which we saved as a FASTA formatted text file and as a GenBank
formatted text file (files ls_orchid.fasta (4) and ls_orchid.gbk (5),
also included with the Biopython source code under Doc/examples/).
  If you run the search today, you'll get hundreds of results! When
following the tutorial, if you want to see the same list of genes, just
download the two files above or copy them from docs/examples/ in the
Biopython source code. In Section ?? we will look at how to do a search
like this from within Python.


2.4.1 Simple FASTA parsing example
==================================

  If you open the lady slipper orchids FASTA file ls_orchid.fasta (6) in
your favourite text editor, you'll see that the file starts like this:
<<>gi|2765658|emb|Z78533.1|CIZ78533 C.irapeanum 5.8S rRNA gene and ITS1
and ITS2 DNA
  CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGATGAGACCGTGGAATAAACGATCGAGTG
  AATCCGGAGGACCGGTGTACTCAGCTCACCGGGGGCATTGCTCCCGTGGTGACCCTGATTTGTTGTTGGG
  ...
>>

  It contains 94 records, each has a line starting with ">"
(greater-than symbol) followed by the sequence on one or more lines. Now
try this in Python:
<<>>> from Bio import SeqIO
  >>> for seq_record in SeqIO.parse("ls_orchid.fasta", "fasta"):
  ...     print(seq_record.id)
  ...     print(repr(seq_record.seq))
  ...     print(len(seq_record))
  ...
>>

  You should get something like this on your screen:
<<gi|2765658|emb|Z78533.1|CIZ78533
  Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGATGAGACCGTGG...CGC')
  740
  ...
  gi|2765564|emb|Z78439.1|PBZ78439
  Seq('CATTGTTGAGATCACATAATAATTGATCGAGTTAATCTGGAGGATCTGTTTACT...GCC')
  592
>>



2.4.2 Simple GenBank parsing example
====================================

  Now let's load the GenBank file ls_orchid.gbk (7) instead - notice
that the code to do this is almost identical to the snippet used above
for the FASTA file - the only difference is we change the filename and
the format string:
<<>>> from Bio import SeqIO
  >>> for seq_record in SeqIO.parse("ls_orchid.gbk", "genbank"):
  ...     print(seq_record.id)
  ...     print(repr(seq_record.seq))
  ...     print(len(seq_record))
  ...
>>

  This should give:
<<Z78533.1
  Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGATGAGACCGTGG...CGC')
  740
  ...
  Z78439.1
  Seq('CATTGTTGAGATCACATAATAATTGATCGAGTTAATCTGGAGGATCTGTTTACT...GCC')
  592
>>

  You'll notice that a shorter string has been used as the seq_record.id
in this case.


2.4.3 I love parsing -- please don't stop talking about it!
===========================================================

  Biopython has a lot of parsers, and each has its own little special
niches based on the sequence format it is parsing and all of that.
Chapter ?? covers Bio.SeqIO in more detail, while Chapter ?? introduces
Bio.AlignIO for sequence alignments.
  While the most popular file formats have parsers integrated into
Bio.SeqIO and/or Bio.AlignIO, for some of the rarer and unloved file
formats there is either no parser at all, or an old parser which has not
been linked in yet. Please also check the wiki pages
http://biopython.org/wiki/SeqIO and http://biopython.org/wiki/AlignIO
for the latest information, or ask on the mailing list. The wiki pages
should include an up to date list of supported file types, and some
additional examples.
  The next place to look for information about specific parsers and how
to do cool things with them is in the Cookbook (Chapter ?? of this
Tutorial). If you don't find the information you are looking for, please
consider helping out your poor overworked documentors and submitting a
cookbook entry about it! (once you figure out how to do it, that is!)


2.5 Connecting with biological databases
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=


  One of the very common things that you need to do in bioinformatics is
extract information from biological databases. It can be quite tedious
to access these databases manually, especially if you have a lot of
repetitive work to do. Biopython attempts to save you time and energy by
making some on-line databases available from Python scripts. Currently,
Biopython has code to extract information from the following databases:


   - Entrez (8) (and PubMed (9)) from the NCBI -- See Chapter ??. 
   - ExPASy (10) -- See Chapter ??. 
   - SCOP (11) -- See the Bio.SCOP.search() function. 

  The code in these modules basically makes it easy to write Python code
that interact with the CGI scripts on these pages, so that you can get
results in an easy to deal with format. In some cases, the results can
be tightly integrated with the Biopython parsers to make it even easier
to extract information.


2.6 What to do next
*=*=*=*=*=*=*=*=*=*


  Now that you've made it this far, you hopefully have a good
understanding of the basics of Biopython and are ready to start using it
for doing useful work. The best thing to do now is finish reading this
tutorial, and then if you want start snooping around in the source code,
and looking at the automatically generated documentation.
  Once you get a picture of what you want to do, and what libraries in
Biopython will do it, you should take a peak at the Cookbook
(Chapter ??), which may have example code to do something similar to
what you want to do.
  If you know what you want to do, but can't figure out how to do it,
please feel free to post questions to the main Biopython list (see
http://biopython.org/wiki/Mailing_lists). This will not only help us
answer your question, it will also allow us to improve the documentation
so it can help the next person do what you want to do.
  Enjoy the code!   
-----------------------------------


 (1) http://biopython.org/docs/1.79/api/
 (2) https://www.flickr.com/search/?q=lady+slipper+orchids=intz=t
 (3) https://google.com/search?q=lady slipper orchidstbm=isch
 (4) https://raw.githubusercontent.com/biopython/biopython/master/Doc/ex
   amples/ls_orchid.fasta
 (5) https://raw.githubusercontent.com/biopython/biopython/master/Doc/ex
   amples/ls_orchid.gbk
 (6) https://raw.githubusercontent.com/biopython/biopython/master/Doc/ex
   amples/ls_orchid.fasta
 (7) https://raw.githubusercontent.com/biopython/biopython/master/Doc/ex
   amples/ls_orchid.gbk
 (8) https://www.ncbi.nlm.nih.gov/Web/Search/entrezfs.html
 (9) https://www.ncbi.nlm.nih.gov/PubMed/
 (10) https://www.expasy.org/
 (11) http://scop.mrc-lmb.cam.ac.uk/scop/


Chapter 3  Sequence objects
***************************

  Biological sequences are arguably the central object in
Bioinformatics, and in this chapter we'll introduce the Biopython
mechanism for dealing with sequences, the Seq object. Chapter ?? will
introduce the related SeqRecord object, which combines the sequence
information with any annotation, used again in Chapter ?? for Sequence
Input/Output.
  Sequences are essentially strings of letters like AGTACACTGGT, which
seems very natural since this is the most common way that sequences are
seen in biological file formats.
  The most important difference between Seq objects and standard Python
strings is they have different methods. Although the Seq object supports
many of the same methods as a plain string, its translate() method
differs by doing biological translation, and there are also additional
biologically relevant methods like reverse_complement().


3.1 Sequences act like strings
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=


  In most ways, we can deal with Seq objects as if they were normal
Python strings, for example getting the length, or iterating over the
elements:
<<>>> from Bio.Seq import Seq
  >>> my_seq = Seq("GATCG")
  >>> for index, letter in enumerate(my_seq):
  ...     print("%i %s" % (index, letter))
  0 G
  1 A
  2 T
  3 C
  4 G
  >>> print(len(my_seq))
  5
>>

  You can access elements of the sequence in the same way as for strings
(but remember, Python counts from zero!):
<<>>> print(my_seq[0]) #first letter
  G
  >>> print(my_seq[2]) #third letter
  T
  >>> print(my_seq[-1]) #last letter
  G
>>

  The Seq object has a .count() method, just like a string. Note that
this means that like a Python string, this gives a non-overlapping
count:
<<>>> from Bio.Seq import Seq
  >>> "AAAA".count("AA")
  2
  >>> Seq("AAAA").count("AA")
  2
>>

  For some biological uses, you may actually want an overlapping count
(i.e. 3 in this trivial example). When searching for single letters,
this makes no difference:
<<>>> from Bio.Seq import Seq
  >>> my_seq = Seq("GATCGATGGGCCTATATAGGATCGAAAATCGC")
  >>> len(my_seq)
  32
  >>> my_seq.count("G")
  9
  >>> 100 * float(my_seq.count("G") + my_seq.count("C")) / len(my_seq)
  46.875
>>

  While you could use the above snippet of code to calculate a GC%, note
that the Bio.SeqUtils module has several GC functions already built. For
example:
<<>>> from Bio.Seq import Seq
  >>> from Bio.SeqUtils import GC
  >>> my_seq = Seq("GATCGATGGGCCTATATAGGATCGAAAATCGC")
  >>> GC(my_seq)
  46.875
>>

  Note that using the Bio.SeqUtils.GC() function should automatically
cope with mixed case sequences and the ambiguous nucleotide S which
means G or C.
  Also note that just like a normal Python string, the Seq object is in
some ways "read-only". If you need to edit your sequence, for example
simulating a point mutation, look at the Section ?? below which talks
about the MutableSeq object.


3.2 Slicing a sequence
*=*=*=*=*=*=*=*=*=*=*=


  A more complicated example, let's get a slice of the sequence:
<<>>> from Bio.Seq import Seq
  >>> my_seq = Seq("GATCGATGGGCCTATATAGGATCGAAAATCGC")
  >>> my_seq[4:12]
  Seq('GATGGGCC')
>>

  Note that `Seq` objects follow the usual indexing conventions for
Python strings, with the first element of the sequence numbered 0. When
you do a slice the first item is included (i.e. 4 in this case) and the
last is excluded (12 in this case).
  Also like a Python string, you can do slices with a start, stop and
stride (the step size, which defaults to one). For example, we can get
the first, second and third codon positions of this DNA sequence:
<<>>> my_seq[0::3]
  Seq('GCTGTAGTAAG')
  >>> my_seq[1::3]
  Seq('AGGCATGCATC')
  >>> my_seq[2::3]
  Seq('TAGCTAAGAC')
>>

  Another stride trick you might have seen with a Python string is the
use of a -1 stride to reverse the string. You can do this with a Seq
object too:
<<>>> my_seq[::-1]
  Seq('CGCTAAAAGCTAGGATATATCCGGGTAGCTAG')
>>



3.3 Turning Seq objects into strings
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=


  If you really do just need a plain string, for example to write to a
file, or insert into a database, then this is very easy to get:
<<>>> str(my_seq)
  'GATCGATGGGCCTATATAGGATCGAAAATCGC'
>>

  Since calling str() on a Seq object returns the full sequence as a
string, you often don't actually have to do this conversion explicitly.
Python does this automatically in the print function:
<<>>> print(my_seq)
  GATCGATGGGCCTATATAGGATCGAAAATCGC
>>

  You can also use the Seq object directly with a %s placeholder when
using the Python string formatting or interpolation operator (%):
<<>>> fasta_format_string = ">Name\n%s\n" % my_seq
  >>> print(fasta_format_string)
  >Name
  GATCGATGGGCCTATATAGGATCGAAAATCGC
  <BLANKLINE>
>>

  This line of code constructs a simple FASTA format record (without
worrying about line wrapping). Section ?? describes a neat way to get a
FASTA formatted string from a SeqRecord object, while the more general
topic of reading and writing FASTA format sequence files is covered in
Chapter ??.


3.4 Concatenating or adding sequences
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*


  As of Biopython 1.78, you can add any two Seq objects together.
<<>>> from Bio.Seq import Seq
  >>> protein_seq = Seq("EVRNAK")
  >>> dna_seq = Seq("ACGT")
  >>> protein_seq + dna_seq
  Seq('EVRNAKACGT')
>>

  Deliberately mixing DNA and protein like this is likely a mistake
though...
  You may often have many sequences to add together, which can be done
with a for loop like this:
<<>>> from Bio.Seq import Seq
  >>> list_of_seqs = [Seq("ACGT"), Seq("AACC"), Seq("GGTT")]
  >>> concatenated = Seq("")
  >>> for s in list_of_seqs:
  ...      concatenated += s
  ...
  >>> concatenated
  Seq('ACGTAACCGGTT')
>>

  Like Python strings, Biopython Seq also has a .join method:
<<>>> from Bio.Seq import Seq
  >>> contigs = [Seq("ATG"), Seq("ATCCCG"), Seq("TTGCA")]
  >>> spacer = Seq("N"*10)
  >>> spacer.join(contigs)
  Seq('ATGNNNNNNNNNNATCCCGNNNNNNNNNNTTGCA')
>>



3.5 Changing case
*=*=*=*=*=*=*=*=*


  Python strings have very useful upper and lower methods for changing
the case. For example,
<<>>> from Bio.Seq import Seq
  >>> dna_seq = Seq("acgtACGT")
  >>> dna_seq
  Seq('acgtACGT')
  >>> dna_seq.upper()
  Seq('ACGTACGT')
  >>> dna_seq.lower()
  Seq('acgtacgt')
>>

  These are useful for doing case insensitive matching:
<<>>> "GTAC" in dna_seq
  False
  >>> "GTAC" in dna_seq.upper()
  True
>>



3.6 Nucleotide sequences and (reverse) complements
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=


  For nucleotide sequences, you can easily obtain the complement or
reverse complement of a Seq object using its built-in methods:
<<>>> from Bio.Seq import Seq
  >>> my_seq = Seq("GATCGATGGGCCTATATAGGATCGAAAATCGC")
  >>> my_seq
  Seq('GATCGATGGGCCTATATAGGATCGAAAATCGC')
  >>> my_seq.complement()
  Seq('CTAGCTACCCGGATATATCCTAGCTTTTAGCG')
  >>> my_seq.reverse_complement()
  Seq('GCGATTTTCGATCCTATATAGGCCCATCGATC')
>>

  As mentioned earlier, an easy way to just reverse a Seq object (or a
Python string) is slice it with -1 step:
<<>>> my_seq[::-1]
  Seq('CGCTAAAAGCTAGGATATATCCGGGTAGCTAG')
>>

  If you do accidentally end up trying to do something weird like taking
the (reverse)complement of a protein sequence, the results are
biologically meaningless:
<<>>> from Bio.Seq import Seq
  >>> protein_seq = Seq("EVRNAK")
  >>> protein_seq.complement()
  Seq('EBYNTM')
>>

  Here the letter "E" is not a valid IUPAC ambiguity code for
nucleotides, so was not complemented. However, "V" means "A", "C" or "G"
and has complement "B", and so on.
  The example in Section ?? combines the Seq object's reverse complement
method with Bio.SeqIO for sequence input/output.


3.7 Transcription
*=*=*=*=*=*=*=*=*

   Before talking about transcription, I want to try to clarify the
strand issue. Consider the following (made up) stretch of double
stranded DNA which encodes a short peptide:

     DNA coding strand (aka Crick strand, strand +1)       
 5'      ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG       3'  
         |||||||||||||||||||||||||||||||||||||||           
 3'      TACCGGTAACATTACCCGGCGACTTTCCCACGGGCTATC       5'  
    DNA template strand (aka Watson strand, strand -1)     

                            |                              
                      Transcription                        


 5'      AUGGCCAUUGUAAUGGGCCGCUGAAAGGGUGCCCGAUAG       3'  
              Single stranded messenger RNA                


  The actual biological transcription process works from the template
strand, doing a reverse complement (TCAG -> CUGA) to give the mRNA.
However, in Biopython and bioinformatics in general, we typically work
directly with the coding strand because this means we can get the mRNA
sequence just by switching T -> U.
  Now let's actually get down to doing a transcription in Biopython.
First, let's create Seq objects for the coding and template DNA strands:
<<>>> from Bio.Seq import Seq
  >>> coding_dna = Seq("ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG")
  >>> coding_dna
  Seq('ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG')
  >>> template_dna = coding_dna.reverse_complement()
  >>> template_dna
  Seq('CTATCGGGCACCCTTTCAGCGGCCCATTACAATGGCCAT')
>>
   These should match the figure above - remember by convention
nucleotide sequences are normally read from the 5' to 3' direction,
while in the figure the template strand is shown reversed.
  Now let's transcribe the coding strand into the corresponding mRNA,
using the Seq object's built in transcribe method:
<<>>> coding_dna
  Seq('ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG')
  >>> messenger_rna = coding_dna.transcribe()
  >>> messenger_rna
  Seq('AUGGCCAUUGUAAUGGGCCGCUGAAAGGGUGCCCGAUAG')
>>
   As you can see, all this does is to replace T by U.
  If you do want to do a true biological transcription starting with the
template strand, then this becomes a two-step process:
<<>>> template_dna.reverse_complement().transcribe()
  Seq('AUGGCCAUUGUAAUGGGCCGCUGAAAGGGUGCCCGAUAG')
>>

  The Seq object also includes a back-transcription method for going
from the mRNA to the coding strand of the DNA. Again, this is a simple U
-> T substitution:
<<>>> from Bio.Seq import Seq
  >>> messenger_rna = Seq("AUGGCCAUUGUAAUGGGCCGCUGAAAGGGUGCCCGAUAG")
  >>> messenger_rna
  Seq('AUGGCCAUUGUAAUGGGCCGCUGAAAGGGUGCCCGAUAG')
  >>> messenger_rna.back_transcribe()
  Seq('ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG')
>>

  Note: The Seq object's transcribe and back_transcribe methods were
added in Biopython 1.49. For older releases you would have to use the
Bio.Seq module's functions instead, see Section ??.


3.8 Translation
*=*=*=*=*=*=*=*

    Sticking with the same example discussed in the transcription
section above, now let's translate this mRNA into the corresponding
protein sequence - again taking advantage of one of the Seq object's
biological methods:
<<>>> from Bio.Seq import Seq
  >>> messenger_rna = Seq("AUGGCCAUUGUAAUGGGCCGCUGAAAGGGUGCCCGAUAG")
  >>> messenger_rna
  Seq('AUGGCCAUUGUAAUGGGCCGCUGAAAGGGUGCCCGAUAG')
  >>> messenger_rna.translate()
  Seq('MAIVMGR*KGAR*')
>>

  You can also translate directly from the coding strand DNA sequence:
<<>>> from Bio.Seq import Seq
  >>> coding_dna = Seq("ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG")
  >>> coding_dna
  Seq('ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG')
  >>> coding_dna.translate()
  Seq('MAIVMGR*KGAR*')
>>

  You should notice in the above protein sequences that in addition to
the end stop character, there is an internal stop as well. This was a
deliberate choice of example, as it gives an excuse to talk about some
optional arguments, including different translation tables (Genetic
Codes).
  The translation tables available in Biopython are based on those from
the NCBI (1) (see the next section of this tutorial). By default,
translation will use the standard genetic code (NCBI table id 1).
Suppose we are dealing with a mitochondrial sequence. We need to tell
the translation function to use the relevant genetic code instead:
<<>>> coding_dna.translate(table="Vertebrate Mitochondrial")
  Seq('MAIVMGRWKGAR*')
>>

  You can also specify the table using the NCBI table number which is
shorter, and often included in the feature annotation of GenBank files:
<<>>> coding_dna.translate(table=2)
  Seq('MAIVMGRWKGAR*')
>>

  Now, you may want to translate the nucleotides up to the first in
frame stop codon, and then stop (as happens in nature):
<<>>> coding_dna.translate()
  Seq('MAIVMGR*KGAR*')
  >>> coding_dna.translate(to_stop=True)
  Seq('MAIVMGR')
  >>> coding_dna.translate(table=2)
  Seq('MAIVMGRWKGAR*')
  >>> coding_dna.translate(table=2, to_stop=True)
  Seq('MAIVMGRWKGAR')
>>
   Notice that when you use the to_stop argument, the stop codon itself
is not translated - and the stop symbol is not included at the end of
your protein sequence.
  You can even specify the stop symbol if you don't like the default
asterisk:
<<>>> coding_dna.translate(table=2, stop_symbol="@")
  Seq('MAIVMGRWKGAR@')
>>

  Now, suppose you have a complete coding sequence CDS, which is to say
a nucleotide sequence (e.g. mRNA -- after any splicing) which is a whole
number of codons (i.e. the length is a multiple of three), commences
with a start codon, ends with a stop codon, and has no internal in-frame
stop codons. In general, given a complete CDS, the default translate
method will do what you want (perhaps with the to_stop option). However,
what if your sequence uses a non-standard start codon? This happens a
lot in bacteria -- for example the gene yaaX in E. coli K12:
<<>>> from Bio.Seq import Seq
  >>> gene =
Seq("GTGAAAAAGATGCAATCTATCGTACTCGCACTTTCCCTGGTTCTGGTCGCTCCCATGGCA"
  ...           
"GCACAGGCTGCGGAAATTACGTTAGTCCCGTCAGTAAAATTACAGATAGGCGATCGTGAT"
  ...           
"AATCGTGGCTATTACTGGGATGGAGGTCACTGGCGCGACCACGGCTGGTGGAAACAACAT"
  ...           
"TATGAATGGCGAGGCAATCGCTGGCACCTACACGGACCGCCGCCACCGCCGCGCCACCAT"
  ...           
"AAGAAAGCTCCTCATGATCATCACGGCGGTCATGGTCCAGGCAAACATCACCGCTAA")
  >>> gene.translate(table="Bacterial")
  Seq('VKKMQSIVLALSLVLVAPMAAQAAEITLVPSVKLQIGDRDNRGYYWDGGHWRDH...HR*',
  ProteinAlpabet())
  >>> gene.translate(table="Bacterial", to_stop=True)
  Seq('VKKMQSIVLALSLVLVAPMAAQAAEITLVPSVKLQIGDRDNRGYYWDGGHWRDH...HHR')
>>

  In the bacterial genetic code GTG is a valid start codon, and while it
does normally encode Valine, if used as a start codon it should be
translated as methionine. This happens if you tell Biopython your
sequence is a complete CDS:
<<>>> gene.translate(table="Bacterial", cds=True)
  Seq('MKKMQSIVLALSLVLVAPMAAQAAEITLVPSVKLQIGDRDNRGYYWDGGHWRDH...HHR')
>>

  In addition to telling Biopython to translate an alternative start
codon as methionine, using this option also makes sure your sequence
really is a valid CDS (you'll get an exception if not).
  The example in Section ?? combines the Seq object's translate method
with Bio.SeqIO for sequence input/output.


3.9 Translation Tables
*=*=*=*=*=*=*=*=*=*=*=


  In the previous sections we talked about the Seq object translation
method (and mentioned the equivalent function in the Bio.Seq module --
see Section ??). Internally these use codon table objects derived from
the NCBI information at
ftp://ftp.ncbi.nlm.nih.gov/entrez/misc/data/gc.prt, also shown on
https://www.ncbi.nlm.nih.gov/Taxonomy/Utils/wprintgc.cgi in a much more
readable layout.
  As before, let's just focus on two choices: the Standard translation
table, and the translation table for Vertebrate Mitochondrial DNA.
<<>>> from Bio.Data import CodonTable
  >>> standard_table = CodonTable.unambiguous_dna_by_name["Standard"]
  >>> mito_table = CodonTable.unambiguous_dna_by_name["Vertebrate
Mitochondrial"]
>>

  Alternatively, these tables are labeled with ID numbers 1 and 2,
respectively:
<<>>> from Bio.Data import CodonTable
  >>> standard_table = CodonTable.unambiguous_dna_by_id[1]
  >>> mito_table = CodonTable.unambiguous_dna_by_id[2]
>>

  You can compare the actual tables visually by printing them: 
<<>>> print(standard_table)
  Table 1 Standard, SGC0

    |  T      |  C      |  A      |  G      |
  --+---------+---------+---------+---------+--
  T | TTT F   | TCT S   | TAT Y   | TGT C   | T
  T | TTC F   | TCC S   | TAC Y   | TGC C   | C
  T | TTA L   | TCA S   | TAA Stop| TGA Stop| A
  T | TTG L(s)| TCG S   | TAG Stop| TGG W   | G
  --+---------+---------+---------+---------+--
  C | CTT L   | CCT P   | CAT H   | CGT R   | T
  C | CTC L   | CCC P   | CAC H   | CGC R   | C
  C | CTA L   | CCA P   | CAA Q   | CGA R   | A
  C | CTG L(s)| CCG P   | CAG Q   | CGG R   | G
  --+---------+---------+---------+---------+--
  A | ATT I   | ACT T   | AAT N   | AGT S   | T
  A | ATC I   | ACC T   | AAC N   | AGC S   | C
  A | ATA I   | ACA T   | AAA K   | AGA R   | A
  A | ATG M(s)| ACG T   | AAG K   | AGG R   | G
  --+---------+---------+---------+---------+--
  G | GTT V   | GCT A   | GAT D   | GGT G   | T
  G | GTC V   | GCC A   | GAC D   | GGC G   | C
  G | GTA V   | GCA A   | GAA E   | GGA G   | A
  G | GTG V   | GCG A   | GAG E   | GGG G   | G
  --+---------+---------+---------+---------+--
>>
   and: 
<<>>> print(mito_table)
  Table 2 Vertebrate Mitochondrial, SGC1

    |  T      |  C      |  A      |  G      |
  --+---------+---------+---------+---------+--
  T | TTT F   | TCT S   | TAT Y   | TGT C   | T
  T | TTC F   | TCC S   | TAC Y   | TGC C   | C
  T | TTA L   | TCA S   | TAA Stop| TGA W   | A
  T | TTG L   | TCG S   | TAG Stop| TGG W   | G
  --+---------+---------+---------+---------+--
  C | CTT L   | CCT P   | CAT H   | CGT R   | T
  C | CTC L   | CCC P   | CAC H   | CGC R   | C
  C | CTA L   | CCA P   | CAA Q   | CGA R   | A
  C | CTG L   | CCG P   | CAG Q   | CGG R   | G
  --+---------+---------+---------+---------+--
  A | ATT I(s)| ACT T   | AAT N   | AGT S   | T
  A | ATC I(s)| ACC T   | AAC N   | AGC S   | C
  A | ATA M(s)| ACA T   | AAA K   | AGA Stop| A
  A | ATG M(s)| ACG T   | AAG K   | AGG Stop| G
  --+---------+---------+---------+---------+--
  G | GTT V   | GCT A   | GAT D   | GGT G   | T
  G | GTC V   | GCC A   | GAC D   | GGC G   | C
  G | GTA V   | GCA A   | GAA E   | GGA G   | A
  G | GTG V(s)| GCG A   | GAG E   | GGG G   | G
  --+---------+---------+---------+---------+--
>>

  You may find these following properties useful -- for example if you
are trying to do your own gene finding:
<<>>> mito_table.stop_codons
  ['TAA', 'TAG', 'AGA', 'AGG']
  >>> mito_table.start_codons
  ['ATT', 'ATC', 'ATA', 'ATG', 'GTG']
  >>> mito_table.forward_table["ACG"]
  'T'
>>



3.10 Comparing Seq objects
*=*=*=*=*=*=*=*=*=*=*=*=*=


  Sequence comparison is actually a very complicated topic, and there is
no easy way to decide if two sequences are equal. The basic problem is
the meaning of the letters in a sequence are context dependent - the
letter "A" could be part of a DNA, RNA or protein sequence. Biopython
can track the molecule type, so comparing two Seq objects could mean
considering this too.
  Should a DNA fragment "ACG" and an RNA fragment "ACG" be equal? What
about the peptide "ACG"? Or the Python string "ACG"? In everyday use,
your sequences will generally all be the same type of (all DNA, all RNA,
or all protein). Well, as of Biopython 1.65, sequence comparison only
looks at the sequence and compares like the Python string.
<<>>> from Bio.Seq import Seq
  >>> seq1 = Seq("ACGT")
  >>> "ACGT" == seq1
  True
  >>> seq1 == "ACGT"
  True
>>

  As an extension to this, using sequence objects as keys in a Python
dictionary is equivalent to using the sequence as a plain string for the
key. See also Section ??.


3.11 Sequences with unknown sequence contents
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*


  In some cases, the length of a sequence may be known but not the
actual letters constituting it. For example, GenBank and EMBL files may
represent a genomic DNA sequence only by its config information, without
specifying the sequence contents explicitly. Such sequences can be
represented by creating a Seq object with the argument None, followed by
the sequence length:
<<>>> from Bio.Seq import Seq
  >>> unknown_seq = Seq(None, 10)
>>

  The Seq object thus created has a well-defined length. Any attempt to
access the sequence contents, however, will raise an
UndefinedSequenceError:
<<>>> unknown_seq
  Seq(None, length=10)
  >>> len(unknown_seq)
  10
  >>> print(unknown_seq)
  Traceback (most recent call last):
  ...
  Bio.Seq.UndefinedSequenceError: Sequence content is undefined
  >>>
>>



3.12 MutableSeq objects
*=*=*=*=*=*=*=*=*=*=*=*


  Just like the normal Python string, the Seq object is "read only", or
in Python terminology, immutable. Apart from wanting the Seq object to
act like a string, this is also a useful default since in many
biological applications you want to ensure you are not changing your
sequence data:
<<>>> from Bio.Seq import Seq
  >>> my_seq = Seq("GCCATTGTAATGGGCCGCTGAAAGGGTGCCCGA")
>>

  Observe what happens if you try to edit the sequence: 
<<>>> my_seq[5] = "G"
  Traceback (most recent call last):
  ...
  TypeError: 'Seq' object does not support item assignment
>>

  However, you can convert it into a mutable sequence (a MutableSeq
object) and do pretty much anything you want with it:
<<>>> from Bio.Seq import MutableSeq
  >>> mutable_seq = MutableSeq(my_seq)
  >>> mutable_seq
  MutableSeq('GCCATTGTAATGGGCCGCTGAAAGGGTGCCCGA')
>>

  Alternatively, you can create a MutableSeq object directly from a
string:
<<>>> from Bio.Seq import MutableSeq
  >>> mutable_seq = MutableSeq("GCCATTGTAATGGGCCGCTGAAAGGGTGCCCGA")
>>

  Either way will give you a sequence object which can be changed:
<<>>> mutable_seq
  MutableSeq('GCCATTGTAATGGGCCGCTGAAAGGGTGCCCGA')
  >>> mutable_seq[5] = "C"
  >>> mutable_seq
  MutableSeq('GCCATCGTAATGGGCCGCTGAAAGGGTGCCCGA')
  >>> mutable_seq.remove("T")
  >>> mutable_seq
  MutableSeq('GCCACGTAATGGGCCGCTGAAAGGGTGCCCGA')
  >>> mutable_seq.reverse()
  >>> mutable_seq
  MutableSeq('AGCCCGTGGGAAAGTCGCCGGGTAATGCACCG')
>>

  Do note that unlike the Seq object, the MutableSeq object's methods
like reverse_complement() and reverse() act in-situ!
  An important technical difference between mutable and immutable
objects in Python means that you can't use a MutableSeq object as a
dictionary key, but you can use a Python string or a Seq object in this
way.
  Once you have finished editing your a MutableSeq object, it's easy to
get back to a read-only Seq object should you need to:
<<>>> from Bio.Seq import Seq
  >>> new_seq = Seq(mutable_seq)
  >>> new_seq
  Seq('AGCCCGTGGGAAAGTCGCCGGGTAATGCACCG')
>>

  You can also get a string from a MutableSeq object just like from a
Seq object (Section ??).


3.13 UnknownSeq objects
*=*=*=*=*=*=*=*=*=*=*=*


  Note that UnknownSeq is deprecated. To represent a sequence of known
length but unknown sequence contents, please use  Seq(None, length).
  The UnknownSeq object is a subclass of the basic Seq object and its
purpose is to represent a sequence where we know the length, but not the
actual letters making it up. You could of course use a normal Seq object
in this situation, but it wastes rather a lot of memory to hold a string
of a million "N" characters when you could just store a single letter
"N" and the desired length as an integer.
<<>>> from Bio.Seq import UnknownSeq
  >>> unk = UnknownSeq(20)
  >>> unk
  UnknownSeq(20, character='?')
  >>> print(unk)
  ????????????????????
  >>> len(unk)
  20
>>

  For DNA or RNA sequences, unknown nucleotides are commonly denoted by
the letter "N", while for proteins "X" is commonly used for unknown
amino acids. When creating an `UnknownSeq`, you can specify the
character to be used instead of "?" to represent unknown letters. For
example 
<<>>> from Bio.Seq import UnknownSeq
  >>> unk_dna = UnknownSeq(20, character="N")
  >>> unk_dna
  UnknownSeq(20, character='N')
  >>> print(unk_dna)
  NNNNNNNNNNNNNNNNNNNN
>>

  You can use all the usual Seq object methods too, note these give back
memory saving UnknownSeq objects where appropriate as you might expect:
<<>>> unk_dna
  UnknownSeq(20, character='N')
  >>> unk_dna.complement()
  UnknownSeq(20, character='N')
  >>> unk_dna.reverse_complement()
  UnknownSeq(20, character='N')
  >>> unk_dna.transcribe()
  UnknownSeq(20, character='N')
  >>> unk_protein = unk_dna.translate()
  >>> unk_protein
  UnknownSeq(6, character='X')
  >>> print(unk_protein)
  XXXXXX
  >>> len(unk_protein)
  6
>>

  You may be able to find a use for the UnknownSeq object in your own
code, but it is more likely that you will first come across them in a
SeqRecord object created by Bio.SeqIO (see Chapter ??). Some sequence
file formats don't always include the actual sequence, for example
GenBank and EMBL files may include a list of features but for the
sequence just present the contig information. Alternatively, the QUAL
files used in sequencing work hold quality scores but they never contain
a sequence -- instead there is a partner FASTA file which does have the
sequence.


3.14 Working with strings directly
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=

    To close this chapter, for those you who really don't want to use
the sequence objects (or who prefer a functional programming style to an
object orientated one), there are module level functions in Bio.Seq will
accept plain Python strings, Seq objects (including UnknownSeq objects)
or MutableSeq objects:
<<>>> from Bio.Seq import reverse_complement, transcribe,
back_transcribe, translate
  >>> my_string = "GCTGTTATGGGTCGTTGGAAGGGTGGTCGTGCTGCTGGTTAG"
  >>> reverse_complement(my_string)
  'CTAACCAGCAGCACGACCACCCTTCCAACGACCCATAACAGC'
  >>> transcribe(my_string)
  'GCUGUUAUGGGUCGUUGGAAGGGUGGUCGUGCUGCUGGUUAG'
  >>> back_transcribe(my_string)
  'GCTGTTATGGGTCGTTGGAAGGGTGGTCGTGCTGCTGGTTAG'
  >>> translate(my_string)
  'AVMGRWKGGRAAG*'
>>

  You are, however, encouraged to work with Seq objects by default.   
-----------------------------------


 (1) https://www.ncbi.nlm.nih.gov/Taxonomy/Utils/wprintgc.cgi


Chapter 4  Sequence annotation objects
**************************************

  Chapter ?? introduced the sequence classes. Immediately "above" the
Seq class is the Sequence Record or SeqRecord class, defined in the
Bio.SeqRecord module. This class allows higher level features such as
identifiers and features (as SeqFeature objects) to be associated with
the sequence, and is used throughout the sequence input/output interface
Bio.SeqIO described fully in Chapter ??.
  If you are only going to be working with simple data like FASTA files,
you can probably skip this chapter for now. If on the other hand you are
going to be using richly annotated sequence data, say from GenBank or
EMBL files, this information is quite important.
  While this chapter should cover most things to do with the SeqRecord
and SeqFeature objects in this chapter, you may also want to read the
SeqRecord wiki page (http://biopython.org/wiki/SeqRecord), and the built
in documentation (also online -- SeqRecord (1) and SeqFeature (2)):
<<>>> from Bio.SeqRecord import SeqRecord
  >>> help(SeqRecord)
  ...
>>



4.1 The SeqRecord object
*=*=*=*=*=*=*=*=*=*=*=*=


  The SeqRecord (Sequence Record) class is defined in the Bio.SeqRecord
module. This class allows higher level features such as identifiers and
features to be associated with a sequence (see Chapter ??), and is the
basic data type for the Bio.SeqIO sequence input/output interface (see
Chapter ??).
  The SeqRecord class itself is quite simple, and offers the following
information as attributes:


 .seq  -- The sequence itself, typically a Seq object.

 .id  -- The primary ID used to identify the sequence -- a string. In
   most cases this is something like an accession number.

 .name  -- A "common" name/id for the sequence -- a string. In some
   cases this will be the same as the accession number, but it could
   also be a clone name. I think of this as being analogous to the LOCUS
   id in a GenBank record.

 .description  -- A human readable description or expressive name for
   the sequence -- a string.

 .letter_annotations  -- Holds per-letter-annotations using a
   (restricted) dictionary of additional information about the letters
   in the sequence. The keys are the name of the information, and the
   information is contained in the value as a Python sequence (i.e. a
   list, tuple or string) with the same length as the sequence itself.
   This is often used for quality scores (e.g. Section ??) or secondary
   structure information (e.g. from Stockholm/PFAM alignment files).

 .annotations  -- A dictionary of additional information about the
   sequence. The keys are the name of the information, and the
   information is contained in the value. This allows the addition of
   more "unstructured" information to the sequence.

 .features  -- A list of SeqFeature objects with more structured
   information about the features on a sequence (e.g. position of genes
   on a genome, or domains on a protein sequence). The structure of
   sequence features is described below in Section ??.

 .dbxrefs  - A list of database cross-references as strings. 



4.2 Creating a SeqRecord
*=*=*=*=*=*=*=*=*=*=*=*=


  Using a SeqRecord object is not very complicated, since all of the
information is presented as attributes of the class. Usually you won't
create a SeqRecord "by hand", but instead use Bio.SeqIO to read in a
sequence file for you (see Chapter ?? and the examples below). However,
creating SeqRecord can be quite simple.


4.2.1 SeqRecord objects from scratch
====================================

  To create a SeqRecord at a minimum you just need a Seq object:
<<>>> from Bio.Seq import Seq
  >>> simple_seq = Seq("GATC")
  >>> from Bio.SeqRecord import SeqRecord
  >>> simple_seq_r = SeqRecord(simple_seq)
>>

  Additionally, you can also pass the id, name and description to the
initialization function, but if not they will be set as strings
indicating they are unknown, and can be modified subsequently:
<<>>> simple_seq_r.id
  '<unknown id>'
  >>> simple_seq_r.id = "AC12345"
  >>> simple_seq_r.description = "Made up sequence I wish I could write
a paper about"
  >>> print(simple_seq_r.description)
  Made up sequence I wish I could write a paper about
  >>> simple_seq_r.seq
  Seq('GATC')
>>

  Including an identifier is very important if you want to output your
SeqRecord to a file. You would normally include this when creating the
object:
<<>>> from Bio.Seq import Seq
  >>> simple_seq = Seq("GATC")
  >>> from Bio.SeqRecord import SeqRecord
  >>> simple_seq_r = SeqRecord(simple_seq, id="AC12345")
>>

  As mentioned above, the SeqRecord has an dictionary attribute
annotations. This is used for any miscellaneous annotations that doesn't
fit under one of the other more specific attributes. Adding annotations
is easy, and just involves dealing directly with the annotation
dictionary:
<<>>> simple_seq_r.annotations["evidence"] = "None. I just made it up."
  >>> print(simple_seq_r.annotations)
  {'evidence': 'None. I just made it up.'}
  >>> print(simple_seq_r.annotations["evidence"])
  None. I just made it up.
>>

  Working with per-letter-annotations is similar, letter_annotations is
a dictionary like attribute which will let you assign any Python
sequence (i.e. a string, list or tuple) which has the same length as the
sequence:
<<>>> simple_seq_r.letter_annotations["phred_quality"] = [40, 40, 38,
30]
  >>> print(simple_seq_r.letter_annotations)
  {'phred_quality': [40, 40, 38, 30]}
  >>> print(simple_seq_r.letter_annotations["phred_quality"])
  [40, 40, 38, 30]
>>

  The dbxrefs and features attributes are just Python lists, and should
be used to store strings and SeqFeature objects (discussed later in this
chapter) respectively.


4.2.2 SeqRecord objects from FASTA files
========================================

  This example uses a fairly large FASTA file containing the whole
sequence for Yersinia pestis biovar Microtus str. 91001 plasmid pPCP1,
originally downloaded from the NCBI. This file is included with the
Biopython unit tests under the GenBank folder, or online
NC_005816.fna (3) from our website.
  The file starts like this - and you can check there is only one record
present (i.e. only one line starting with a greater than symbol):
<<>gi|45478711|ref|NC_005816.1| Yersinia pestis biovar Microtus ...
pPCP1, complete sequence
  TGTAACGAACGGTGCAATAGTGATCCACACCCAACGCCTGAAATCAGATCCAGGGGGTAATCTGCTCTCC
  ...
>>

  Back in Chapter ?? you will have seen the function
Bio.SeqIO.parse(...) used to loop over all the records in a file as
SeqRecord objects. The Bio.SeqIO module has a sister function for use on
files which contain just one record which we'll use here (see Chapter ??
for details):
<<breaklines=true,breaksymbolleft=]{pycon}
  >>> from Bio import SeqIO
  >>> record = SeqIO.read("NC_005816.fna", "fasta")
  >>> record
  SeqRecord(seq=Seq('TGTAACGAACGGTGCAATAGTGATCCACACCCAACGCCTGAAATCAGATCC
AGG...CTG'), id='gi|45478711|ref|NC_005816.1|',
name='gi|45478711|ref|NC_005816.1|',
description='gi|45478711|ref|NC_005816.1| Yersinia pestis biovar
Microtus str. 91001 plasmid pPCP1, complete sequence', dbxrefs=[])
>>

  Now, let's have a look at the key attributes of this SeqRecord
individually -- starting with the seq attribute which gives you a Seq
object:
<<>>> record.seq
  Seq('TGTAACGAACGGTGCAATAGTGATCCACACCCAACGCCTGAAATCAGATCCAGG...CTG')
>>

  Next, the identifiers and description:
<<breaklines=true,breaksymbolleft=]{pycon}
  >>> record.id
  'gi|45478711|ref|NC_005816.1|'
  >>> record.name
  'gi|45478711|ref|NC_005816.1|'
  >>> record.description
  'gi|45478711|ref|NC_005816.1| Yersinia pestis biovar Microtus str.
91001 plasmid pPCP1, complete sequence'
>>

  As you can see above, the first word of the FASTA record's title line
(after removing the greater than symbol) is used for both the id and
name attributes. The whole title line (after removing the greater than
symbol) is used for the record description. This is deliberate, partly
for backwards compatibility reasons, but it also makes sense if you have
a FASTA file like this:
<<>Yersinia pestis biovar Microtus str. 91001 plasmid pPCP1
  TGTAACGAACGGTGCAATAGTGATCCACACCCAACGCCTGAAATCAGATCCAGGGGGTAATCTGCTCTCC
  ...
>>

  Note that none of the other annotation attributes get populated when
reading a FASTA file:
<<>>> record.dbxrefs
  []
  >>> record.annotations
  {}
  >>> record.letter_annotations
  {}
  >>> record.features
  []
>>

  In this case our example FASTA file was from the NCBI, and they have a
fairly well defined set of conventions for formatting their FASTA lines.
This means it would be possible to parse this information and extract
the GI number and accession for example. However, FASTA files from other
sources vary, so this isn't possible in general.


4.2.3 SeqRecord objects from GenBank files
==========================================

  As in the previous example, we're going to look at the whole sequence
for Yersinia pestis biovar Microtus str. 91001 plasmid pPCP1, originally
downloaded from the NCBI, but this time as a GenBank file. Again, this
file is included with the Biopython unit tests under the GenBank folder,
or online NC_005816.gb (4) from our website.
  This file contains a single record (i.e. only one LOCUS line) and
starts:
<<LOCUS       NC_005816               9609 bp    DNA     circular BCT
21-JUL-2008
  DEFINITION  Yersinia pestis biovar Microtus str. 91001 plasmid pPCP1,
complete
              sequence.
  ACCESSION   NC_005816
  VERSION     NC_005816.1  GI:45478711
  PROJECT     GenomeProject:10638
  ...
>>

  Again, we'll use Bio.SeqIO to read this file in, and the code is
almost identical to that for used above for the FASTA file (see
Chapter ?? for details):
<<breaklines=true,breaksymbolleft=]{pycon}
  >>> from Bio import SeqIO
  >>> record = SeqIO.read("NC_005816.gb", "genbank")
  >>> record
  SeqRecord(seq=Seq('TGTAACGAACGGTGCAATAGTGATCCACACCCAACGCCTGAAATCAGATCC
AGG...CTG'), id='NC_005816.1', name='NC_005816', description='Yersinia
pestis biovar Microtus str. 91001 plasmid pPCP1, complete sequence',
dbxrefs=['Project:58037'])
>>

<<>>> record.seq
  Seq('TGTAACGAACGGTGCAATAGTGATCCACACCCAACGCCTGAAATCAGATCCAGG...CTG')
>>

  The name comes from the LOCUS line, while the id includes the version
suffix. The description comes from the DEFINITION line:
<<>>> record.id
  'NC_005816.1'
  >>> record.name
  'NC_005816'
  >>> record.description
  'Yersinia pestis biovar Microtus str. 91001 plasmid pPCP1, complete
sequence'
>>

  GenBank files don't have any per-letter annotations:
<<>>> record.letter_annotations
  {}
>>

  Most of the annotations information gets recorded in the annotations
dictionary, for example:
<<>>> len(record.annotations)
  13
  >>> record.annotations["source"]
  'Yersinia pestis biovar Microtus str. 91001'
>>

  The dbxrefs list gets populated from any PROJECT or DBLINK lines:
<<>>> record.dbxrefs
  ['Project:58037']
>>

  Finally, and perhaps most interestingly, all the entries in the
features table (e.g. the genes or CDS features) get recorded as
SeqFeature objects in the features list.
<<>>> len(record.features)
  41
>>

  We'll talk about SeqFeature objects next, in Section ??.


4.3 Feature, location and position objects
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=




4.3.1 SeqFeature objects
========================

  Sequence features are an essential part of describing a sequence. Once
you get beyond the sequence itself, you need some way to organize and
easily get at the more "abstract" information that is known about the
sequence. While it is probably impossible to develop a general sequence
feature class that will cover everything, the Biopython SeqFeature class
attempts to encapsulate as much of the information about the sequence as
possible. The design is heavily based on the GenBank/EMBL feature
tables, so if you understand how they look, you'll probably have an
easier time grasping the structure of the Biopython classes.
  The key idea about each SeqFeature object is to describe a region on a
parent sequence, typically a SeqRecord object. That region is described
with a location object, typically a range between two positions (see
Section ?? below).
  The SeqFeature class has a number of attributes, so first we'll list
them and their general features, and then later in the chapter work
through examples to show how this applies to a real life example. The
attributes of a SeqFeature are:


 .type  -- This is a textual description of the type of feature (for
   instance, this will be something like `CDS' or `gene').

 .location  -- The location of the SeqFeature on the sequence that you
   are dealing with, see Section ?? below. The SeqFeature delegates much
   of its functionality to the location object, and includes a number of
   shortcut attributes for properties of the location:


    .ref  -- shorthand for .location.ref -- any (different) reference
      sequence the location is referring to. Usually just None.

    .ref_db  -- shorthand for .location.ref_db -- specifies the database
      any identifier in .ref refers to. Usually just None.

    .strand  -- shorthand for .location.strand -- the strand on the
      sequence that the feature is located on. For double stranded
      nucleotide sequence this may either be 1 for the top strand, -1
      for the bottom strand, 0 if the strand is important but is
      unknown, or None if it doesn't matter. This is None for proteins,
      or single stranded sequences. 


 .qualifiers  -- This is a Python dictionary of additional information
   about the feature. The key is some kind of terse one-word description
   of what the information contained in the value is about, and the
   value is the actual information. For example, a common key for a
   qualifier might be "evidence" and the value might be "computational
   (non-experimental)." This is just a way to let the person who is
   looking at the feature know that it has not be experimentally
   (i. e. in a wet lab) confirmed. Note that other the value will be a
   list of strings (even when there is only one string). This is a
   reflection of the feature tables in GenBank/EMBL files.

 .sub_features  -- This used to be used to represent features with
   complicated locations like `joins' in GenBank/EMBL files. This has
   been deprecated with the introduction of the CompoundLocation object,
   and should now be ignored.



4.3.2 Positions and locations
=============================

  The key idea about each SeqFeature object is to describe a region on a
parent sequence, for which we use a location object, typically
describing a range between two positions. Two try to clarify the
terminology we're using:


 position  -- This refers to a single position on a sequence, which may
   be fuzzy or not. For instance, 5, 20, <100 and >200 are all
   positions.

 location  -- A location is region of sequence bounded by some
   positions. For instance 5..20 (i. e. 5 to 20) is a location. 

  I just mention this because sometimes I get confused between the two.


4.3.2.1 FeatureLocation object
------------------------------

  Unless you work with eukaryotic genes, most SeqFeature locations are
extremely simple - you just need start and end coordinates and a strand.
That's essentially all the basic FeatureLocation object does.
  In practise of course, things can be more complicated. First of all we
have to handle compound locations made up of several regions. Secondly,
the positions themselves may be fuzzy (inexact).


4.3.2.2 CompoundLocation object
-------------------------------

  Biopython 1.62 introduced the CompoundLocation as part of a
restructuring of how complex locations made up of multiple regions are
represented. The main usage is for handling `join' locations in
EMBL/GenBank files.


4.3.2.3 Fuzzy Positions
-----------------------

  So far we've only used simple positions. One complication in dealing
with feature locations comes in the positions themselves. In biology
many times things aren't entirely certain (as much as us wet lab
biologists try to make them certain!). For instance, you might do a
dinucleotide priming experiment and discover that the start of mRNA
transcript starts at one of two sites. This is very useful information,
but the complication comes in how to represent this as a position. To
help us deal with this, we have the concept of fuzzy positions.
Basically there are several types of fuzzy positions, so we have five
classes do deal with them:


 ExactPosition  -- As its name suggests, this class represents a
   position which is specified as exact along the sequence. This is
   represented as just a number, and you can get the position by looking
   at the position attribute of the object.

 BeforePosition  -- This class represents a fuzzy position that occurs
   prior to some specified site. In GenBank/EMBL notation, this is
   represented as something like `<13', signifying that the real
   position is located somewhere less than 13. To get the specified
   upper boundary, look at the position attribute of the object.

 AfterPosition  -- Contrary to BeforePosition, this class represents a
   position that occurs after some specified site. This is represented
   in GenBank as `>13', and like BeforePosition, you get the boundary
   number by looking at the position attribute of the object.

 WithinPosition  -- Occasionally used for GenBank/EMBL locations, this
   class models a position which occurs somewhere between two specified
   nucleotides. In GenBank/EMBL notation, this would be represented as
   `(1.5)', to represent that the position is somewhere within the range
   1 to 5. To get the information in this class you have to look at two
   attributes. The position attribute specifies the lower boundary of
   the range we are looking at, so in our example case this would be
   one. The extension attribute specifies the range to the higher
   boundary, so in this case it would be 4. So object.position is the
   lower boundary and object.position + object.extension is the upper
   boundary.

 OneOfPosition  -- Occasionally used for GenBank/EMBL locations, this
   class deals with a position where several possible values exist, for
   instance you could use this if the start codon was unclear and there
   where two candidates for the start of the gene. Alternatively, that
   might be handled explicitly as two related gene features.

 UnknownPosition  -- This class deals with a position of unknown
   location. This is not used in GenBank/EMBL, but corresponds to the
   `?' feature coordinate used in UniProt.

  Here's an example where we create a location with fuzzy end points:
<<>>> from Bio import SeqFeature
  >>> start_pos = SeqFeature.AfterPosition(5)
  >>> end_pos = SeqFeature.BetweenPosition(9, left=8, right=9)
  >>> my_location = SeqFeature.FeatureLocation(start_pos, end_pos)
>>

  Note that the details of some of the fuzzy-locations changed in
Biopython 1.59, in particular for BetweenPosition and WithinPosition you
must now make it explicit which integer position should be used for
slicing etc. For a start position this is generally the lower (left)
value, while for an end position this would generally be the higher
(right) value.
  If you print out a FeatureLocation object, you can get a nice
representation of the information:
<<>>> print(my_location)
  [>5:(8^9)]
>>

  We can access the fuzzy start and end positions using the start and
end attributes of the location:
<<>>> my_location.start
  AfterPosition(5)
  >>> print(my_location.start)
  >5
  >>> my_location.end
  BetweenPosition(9, left=8, right=9)
  >>> print(my_location.end)
  (8^9)
>>

  If you don't want to deal with fuzzy positions and just want numbers,
they are actually subclasses of integers so should work like integers:
<<>>> int(my_location.start)
  5
  >>> int(my_location.end)
  9
>>

  For compatibility with older versions of Biopython you can ask for the
nofuzzy_start and nofuzzy_end attributes of the location which are plain
integers:
<<>>> my_location.nofuzzy_start
  5
  >>> my_location.nofuzzy_end
  9
>>

  Notice that this just gives you back the position attributes of the
fuzzy locations.
  Similarly, to make it easy to create a position without worrying about
fuzzy positions, you can just pass in numbers to the FeaturePosition
constructors, and you'll get back out ExactPosition objects:
<<>>> exact_location = SeqFeature.FeatureLocation(5, 9)
  >>> print(exact_location)
  [5:9]
  >>> exact_location.start
  ExactPosition(5)
  >>> int(exact_location.start)
  5
  >>> exact_location.nofuzzy_start
  5
>>

  That is most of the nitty gritty about dealing with fuzzy positions in
Biopython. It has been designed so that dealing with fuzziness is not
that much more complicated than dealing with exact positions, and
hopefully you find that true!


4.3.2.4 Location testing
------------------------

  You can use the Python keyword in with a SeqFeature or location object
to see if the base/residue for a parent coordinate is within the
feature/location or not.
  For example, suppose you have a SNP of interest and you want to know
which features this SNP is within, and lets suppose this SNP is at index
4350 (Python counting!). Here is a simple brute force solution where we
just check all the features one by one in a loop:
<<>>> from Bio import SeqIO
  >>> my_snp = 4350
  >>> record = SeqIO.read("NC_005816.gb", "genbank")
  >>> for feature in record.features:
  ...     if my_snp in feature:
  ...         print("%s %s" % (feature.type,
feature.qualifiers.get("db_xref")))
  ...
  source ['taxon:229193']
  gene ['GeneID:2767712']
  CDS ['GI:45478716', 'GeneID:2767712']
>>

  Note that gene and CDS features from GenBank or EMBL files defined
with joins are the union of the exons -- they do not cover any introns.


4.3.3 Sequence described by a feature or location
=================================================

  A SeqFeature or location object doesn't directly contain a sequence,
instead the location (see Section ??) describes how to get this from the
parent sequence. For example consider a (short) gene sequence with
location 5:18 on the reverse strand, which in GenBank/EMBL notation
using 1-based counting would be complement(6..18), like this:
<<>>> from Bio.Seq import Seq
  >>> from Bio.SeqFeature import SeqFeature, FeatureLocation
  >>> seq =
Seq("ACCGAGACGGCAAAGGCTAGCATAGGTATGAGACTTCCTTCCTGCCAGTGCTGAGGAACTGGGAGCC
TAC")
  >>> feature = SeqFeature(FeatureLocation(5, 18), type="gene",
strand=-1)
>>

  You could take the parent sequence, slice it to extract 5:18, and then
take the reverse complement. If you are using Biopython 1.59 or later,
the feature location's start and end are integer like so this works:
<<>>> feature_seq =
seq[feature.location.start:feature.location.end].reverse_complement()
  >>> print(feature_seq)
  AGCCTTTGCCGTC
>>

  This is a simple example so this isn't too bad -- however once you
have to deal with compound features (joins) this is rather messy.
Instead, the SeqFeature object has an extract method to take care of all
this (and since Biopython 1.78 can handle trans-splicing by supplying a
dictionary of referenced sequences):
<<>>> feature_seq = feature.extract(seq)
  >>> print(feature_seq)
  AGCCTTTGCCGTC
>>

  The length of a SeqFeature or location matches that of the region of
sequence it describes.
<<>>> print(len(feature_seq))
  13
  >>> print(len(feature))
  13
  >>> print(len(feature.location))
  13
>>

  For simple FeatureLocation objects the length is just the difference
between the start and end positions. However, for a CompoundLocation the
length is the sum of the constituent regions.


4.4 Comparison
*=*=*=*=*=*=*=


  The SeqRecord objects can be very complex, but here's a simple
example:
<<>>> from Bio.Seq import Seq
  >>> from Bio.SeqRecord import SeqRecord
  >>> record1 = SeqRecord(Seq("ACGT"), id="test")
  >>> record2 = SeqRecord(Seq("ACGT"), id="test")
>>

  What happens when you try to compare these "identical" records?
<<>>> record1 == record2
  ...
>>

  Perhaps surprisingly older versions of Biopython would use Python's
default object comparison for the SeqRecord, meaning record1 == record2
would only return True if these variables pointed at the same object in
memory. In this example, record1 == record2 would have returned False
here!
<<>>> record1 == record2  # on old versions of Biopython!
  False
>>

  As of Biopython 1.67, SeqRecord comparison like record1 == record2
will instead raise an explicit error to avoid people being caught out by
this:
<<breaklines=true,breaksymbolleft=]{pycon}
  >>> record1 == record2
  Traceback (most recent call last):
  ...
  NotImplementedError: SeqRecord comparison is deliberately not
implemented. Explicitly compare the attributes of interest.
>>

  Instead you should check the attributes you are interested in, for
example the identifier and the sequence:
<<>>> record1.id == record2.id
  True
  >>> record1.seq == record2.seq
  True
>>

  Beware that comparing complex objects quickly gets complicated (see
also Section ??).


4.5 References
*=*=*=*=*=*=*=


  Another common annotation related to a sequence is a reference to a
journal or other published work dealing with the sequence. We have a
fairly simple way of representing a Reference in Biopython -- we have a
Bio.SeqFeature.Reference class that stores the relevant information
about a reference as attributes of an object.
  The attributes include things that you would expect to see in a
reference like journal, title and authors. Additionally, it also can
hold the medline_id and pubmed_id and a comment about the reference.
These are all accessed simply as attributes of the object.
  A reference also has a location object so that it can specify a
particular location on the sequence that the reference refers to. For
instance, you might have a journal that is dealing with a particular
gene located on a BAC, and want to specify that it only refers to this
position exactly. The location is a potentially fuzzy location, as
described in section ??.
  Any reference objects are stored as a list in the SeqRecord object's
annotations dictionary under the key "references". That's all there is
too it. References are meant to be easy to deal with, and hopefully
general enough to cover lots of usage cases.


4.6 The format method
*=*=*=*=*=*=*=*=*=*=*


  The format() method of the SeqRecord class gives a string containing
your record formatted using one of the output file formats supported by
Bio.SeqIO, such as FASTA:
<<>>> from Bio.Seq import Seq
  >>> from Bio.SeqRecord import SeqRecord
  >>> record = SeqRecord(
  ...     Seq(
  ...        
"MMYQQGCFAGGTVLRLAKDLAENNRGARVLVVCSEITAVTFRGPSETHLDSMVGQALFGD"
  ...        
"GAGAVIVGSDPDLSVERPLYELVWTGATLLPDSEGAIDGHLREVGLTFHLLKDVPGLISK"
  ...        
"NIEKSLKEAFTPLGISDWNSTFWIAHPGGPAILDQVEAKLGLKEEKMRATREVLSEYGNM"
  ...         "SSAC"
  ...     ),
  ...     id="gi|14150838|gb|AAK54648.1|AF376133_1",
  ...     description="chalcone synthase [Cucumis sativus]",
  ... )
  >>> print(record.format("fasta"))
>>
   which should give: 
<<>gi|14150838|gb|AAK54648.1|AF376133_1 chalcone synthase [Cucumis
sativus]
  MMYQQGCFAGGTVLRLAKDLAENNRGARVLVVCSEITAVTFRGPSETHLDSMVGQALFGD
  GAGAVIVGSDPDLSVERPLYELVWTGATLLPDSEGAIDGHLREVGLTFHLLKDVPGLISK
  NIEKSLKEAFTPLGISDWNSTFWIAHPGGPAILDQVEAKLGLKEEKMRATREVLSEYGNM
  SSAC
  <BLANKLINE>
>>

  This format method takes a single mandatory argument, a lower case
string which is supported by Bio.SeqIO as an output format (see
Chapter ??). However, some of the file formats Bio.SeqIO can write to
require more than one record (typically the case for multiple sequence
alignment formats), and thus won't work via this format() method. See
also Section ??.


4.7 Slicing a SeqRecord
*=*=*=*=*=*=*=*=*=*=*=*


  You can slice a SeqRecord, to give you a new SeqRecord covering just
part of the sequence. What is important here is that any per-letter
annotations are also sliced, and any features which fall completely
within the new sequence are preserved (with their locations adjusted).
  For example, taking the same GenBank file used earlier:
<<breaklines=true,breaksymbolleft=]{pycon}
  >>> from Bio import SeqIO
  >>> record = SeqIO.read("NC_005816.gb", "genbank")
  >>> record
  SeqRecord(seq=Seq('TGTAACGAACGGTGCAATAGTGATCCACACCCAACGCCTGAAATCAGATCC
AGG...CTG'), id='NC_005816.1', name='NC_005816', description='Yersinia
pestis biovar Microtus str. 91001 plasmid pPCP1, complete sequence',
dbxrefs=['Project:58037'])
  >>> len(record)
  9609
  >>> len(record.features)
  41
>>

  For this example we're going to focus in on the pim gene, YP_pPCP05.
If you have a look at the GenBank file directly you'll find this
gene/CDS has location string 4343..4780, or in Python counting
4342:4780. From looking at the file you can work out that these are the
twelfth and thirteenth entries in the file, so in Python zero-based
counting they are entries 11 and 12 in the features list:
<<breaklines=true,breakanywhere=true,breaksymbolleft=,breakanywheresymbo
lpre=]{pycon}
  >>> print(record.features[20])
  type: gene
  location: [4342:4780](+)
  qualifiers:
      Key: db_xref, Value: ['GeneID:2767712']
      Key: gene, Value: ['pim']
      Key: locus_tag, Value: ['YP_pPCP05']
  <BLANKLINE>
  >>> print(record.features[21])
  type: CDS
  location: [4342:4780](+)
  qualifiers:
      Key: codon_start, Value: ['1']
      Key: db_xref, Value: ['GI:45478716', 'GeneID:2767712']
      Key: gene, Value: ['pim']
      Key: locus_tag, Value: ['YP_pPCP05']
      Key: note, Value: ['similar to many previously sequenced pesticin
immunity protein entries of Yersinia pestis plasmid pPCP, e.g. gi|
16082683|,ref|NP_395230.1| (NC_003132) , gi|1200166|emb|CAA90861.1|
(Z54145 ) , gi|1488655| emb|CAA63439.1| (X92856) ,
gi|2996219|gb|AAC62543.1| (AF053945) , and gi|5763814|emb|CAB531 67.1|
(AL109969)']
      Key: product, Value: ['pesticin immunity protein']
      Key: protein_id, Value: ['NP_995571.1']
      Key: transl_table, Value: ['11']
      Key: translation, Value:
['MGGGMISKLFCLALIFLSSSGLAEKNTYTAKDILQNLELNTFGNSLSHGIYGKQTTFKQTEFTNIKSNTK
KHIALINKDNSWMISLKILGIKRDEYTVCFEDFSLIRPPTYVAIHPLLIKKVKSGNFIVVKEIKKSIPGCTV
YYH']
  <BLANKLINE>
>>

  Let's slice this parent record from 4300 to 4800 (enough to include
the pim gene/CDS), and see how many features we get:
<<breaklines=true,breaksymbolleft=]{pycon}
  >>> sub_record = record[4300:4800]
  >>> sub_record
  SeqRecord(seq=Seq('ATAAATAGATTATTCCAAATAATTTATTTATGTAAGAACAGGATGGGAGGG
GGA...TTA'), id='NC_005816.1', name='NC_005816', description='Yersinia
pestis biovar Microtus str. 91001 plasmid pPCP1, complete sequence',
dbxrefs=[])
  >>> len(sub_record)
  500
  >>> len(sub_record.features)
  2
>>

  Our sub-record just has two features, the gene and CDS entries for
YP_pPCP05:
<<breaklines=true,breakanywhere=true,breaksymbolleft=,breakanywheresymbo
lpre=]{pycon}
  >>> print(sub_record.features[0])
  type: gene
  location: [42:480](+)
  qualifiers:
      Key: db_xref, Value: ['GeneID:2767712']
      Key: gene, Value: ['pim']
      Key: locus_tag, Value: ['YP_pPCP05']
  <BLANKLINE>
  >>> print(sub_record.features[1])
  type: CDS
  location: [42:480](+)
  qualifiers:
      Key: codon_start, Value: ['1']
      Key: db_xref, Value: ['GI:45478716', 'GeneID:2767712']
      Key: gene, Value: ['pim']
      Key: locus_tag, Value: ['YP_pPCP05']
      Key: note, Value: ['similar to many previously sequenced pesticin
immunity protein entries of Yersinia pestis plasmid pPCP, e.g. gi|
16082683|,ref|NP_395230.1| (NC_003132) , gi|1200166|emb|CAA90861.1|
(Z54145 ) , gi|1488655| emb|CAA63439.1| (X92856) ,
gi|2996219|gb|AAC62543.1| (AF053945) , and gi|5763814|emb|CAB531 67.1|
(AL109969)']
      Key: product, Value: ['pesticin immunity protein']
      Key: protein_id, Value: ['NP_995571.1']
      Key: transl_table, Value: ['11']
      Key: translation, Value:
['MGGGMISKLFCLALIFLSSSGLAEKNTYTAKDILQNLELNTFGNSLSHGIYGKQTTFKQTEFTNIKSNTK
KHIALINKDNSWMISLKILGIKRDEYTVCFEDFSLIRPPTYVAIHPLLIKKVKSGNFIVVKEIKKSIPGCTV
YYH']
  <BLANKLINE>
>>

  Notice that their locations have been adjusted to reflect the new
parent sequence!
  While Biopython has done something sensible and hopefully intuitive
with the features (and any per-letter annotation), for the other
annotation it is impossible to know if this still applies to the
sub-sequence or not. To avoid guessing, with the exception of the
molecule type, the .annotations and .dbxrefs are omitted from the
sub-record, and it is up to you to transfer any relevant information as
appropriate.
<<>>> sub_record.annotations
  {'molecule_type': 'DNA'}
  >>> sub_record.dbxrefs
  []
>>

  You may wish to preserve other entries like the organism? Beware of
copying the entire annotations dictionary as in this case your partial
sequence is no longer circular DNA - it is now linear:
<<>>> sub_record.annotations['topology'] = 'linear'
>>

  The same point could be made about the record id, name and
description, but for practicality these are preserved:
<<>>> sub_record.id
  'NC_005816.1'
  >>> sub_record.name
  'NC_005816'
  >>> sub_record.description
  'Yersinia pestis biovar Microtus str. 91001 plasmid pPCP1, complete
sequence'
>>

  This illustrates the problem nicely though, our new sub-record is not
the complete sequence of the plasmid, so the description is wrong! Let's
fix this and then view the sub-record as a reduced GenBank file using
the format method described above in Section ??:
<<breaklines=true,breaksymbolleft=]{pycon}
  >>> sub_record.description = "Yersinia pestis biovar Microtus str.
91001 plasmid pPCP1, partial"
  >>> print(sub_record.format("genbank")[:200] + "...")
  LOCUS       NC_005816                500 bp    DNA     linear   UNK
01-JAN-1980
  DEFINITION  Yersinia pestis biovar Microtus str. 91001 plasmid pPCP1,
partial.
  ACCESSION   NC_005816
  VERSION     NC_0058...
>>

  See Sections ?? and ?? for some FASTQ examples where the per-letter
annotations (the read quality scores) are also sliced.


4.8 Adding SeqRecord objects
*=*=*=*=*=*=*=*=*=*=*=*=*=*=


  You can add SeqRecord objects together, giving a new SeqRecord. What
is important here is that any common per-letter annotations are also
added, all the features are preserved (with their locations adjusted),
and any other common annotation is also kept (like the id, name and
description).
  For an example with per-letter annotation, we'll use the first record
in a FASTQ file. Chapter ?? will explain the SeqIO functions:
<<breaklines=true,breaksymbolleft=]{pycon}
  >>> from Bio import SeqIO
  >>> record = next(SeqIO.parse("example.fastq", "fastq"))
  >>> len(record)
  25
  >>> print(record.seq)
  CCCTTCTTGTCTTCAGCGTTTCTCC
  >>> print(record.letter_annotations["phred_quality"])
  [26, 26, 18, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 22, 26,
26, 26, 26, 26, 26, 26, 23, 23]
>>

  Let's suppose this was Roche 454 data, and that from other information
you think the TTT should be only TT. We can make a new edited record by
first slicing the SeqRecord before and after the "extra" third T:
<<breaklines=true,breaksymbolleft=]{pycon}
  >>> left = record[:20]
  >>> print(left.seq)
  CCCTTCTTGTCTTCAGCGTT
  >>> print(left.letter_annotations["phred_quality"])
  [26, 26, 18, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 22, 26,
26, 26, 26]
  >>> right = record[21:]
  >>> print(right.seq)
  CTCC
  >>> print(right.letter_annotations["phred_quality"])
  [26, 26, 23, 23]
>>

  Now add the two parts together:
<<breaklines=true,breaksymbolleft=]{pycon}
  >>> edited = left + right
  >>> len(edited)
  24
  >>> print(edited.seq)
  CCCTTCTTGTCTTCAGCGTTCTCC
  >>> print(edited.letter_annotations["phred_quality"])
  [26, 26, 18, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 22, 26,
26, 26, 26, 26, 26, 23, 23]
>>

  Easy and intuitive? We hope so! You can make this shorter with just:
<<>>> edited = record[:20] + record[21:]
>>

  Now, for an example with features, we'll use a GenBank file. Suppose
you have a circular genome:
<<breaklines=true,breaksymbolleft=]{pycon}
  >>> from Bio import SeqIO
  >>> record = SeqIO.read("NC_005816.gb", "genbank")
  >>> record
  SeqRecord(seq=Seq('TGTAACGAACGGTGCAATAGTGATCCACACCCAACGCCTGAAATCAGATCC
AGG...CTG'), id='NC_005816.1', name='NC_005816', description='Yersinia
pestis biovar Microtus str. 91001 plasmid pPCP1, complete sequence',
dbxrefs=['Project:58037'])
  >>> len(record)
  9609
  >>> len(record.features)
  41
  >>> record.dbxrefs
  ['Project:58037']
  >>> record.annotations.keys()
  dict_keys(['molecule_type', 'topology', 'data_file_division', 'date',
'accessions', 'sequence_version', 'gi', 'keywords', 'source',
'organism', 'taxonomy', 'references', 'comment'])
>>

  You can shift the origin like this:
<<breaklines=true,breaksymbolleft=]{pycon}
  >>> shifted = record[2000:] + record[:2000]
  >>> shifted
  SeqRecord(seq=Seq('GATACGCAGTCATATTTTTTACACAATTCTCTAATCCCGACAAGGTCGTAG
GTC...GGA'), id='NC_005816.1', name='NC_005816', description='Yersinia
pestis biovar Microtus str. 91001 plasmid pPCP1, complete sequence',
dbxrefs=[])
  >>> len(shifted)
  9609
>>

  Note that this isn't perfect in that some annotation like the database
cross references, all the annotations except molecule type, and one of
the features (the source feature) have been lost:
<<>>> len(shifted.features)
  40
  >>> shifted.dbxrefs
  []
  >>> shifted.annotations.keys()
  dict_keys(['molecule_type'])
>>

  This is because the SeqRecord slicing step is cautious in what
annotation it preserves (erroneously propagating annotation can cause
major problems). If you want to keep the database cross references or
the annotations dictionary, this must be done explicitly:
<<breaklines=true,breaksymbolleft=]{pycon}
  >>> shifted.dbxrefs = record.dbxrefs[:]
  >>> shifted.annotations = record.annotations.copy()
  >>> shifted.dbxrefs
  ['Project:58037']
  >>> shifted.annotations.keys()
  dict_keys(['molecule_type', 'topology', 'data_file_division', 'date',
'accessions', 'sequence_version', 'gi', 'keywords', 'source',
'organism', 'taxonomy', 'references', 'comment'])
>>

  Also note that in an example like this, you should probably change the
record identifiers since the NCBI references refer to the original
unmodified sequence.


4.9 Reverse-complementing SeqRecord objects
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*


  One of the new features in Biopython 1.57 was the SeqRecord object's
reverse_complement method. This tries to balance easy of use with
worries about what to do with the annotation in the reverse complemented
record.
  For the sequence, this uses the Seq object's reverse complement
method. Any features are transferred with the location and strand
recalculated. Likewise any per-letter-annotation is also copied but
reversed (which makes sense for typical examples like quality scores).
However, transfer of most annotation is problematical.
  For instance, if the record ID was an accession, that accession should
not really apply to the reverse complemented sequence, and transferring
the identifier by default could easily cause subtle data corruption in
downstream analysis. Therefore by default, the SeqRecord's id, name,
description, annotations and database cross references are all not
transferred by default.
  The SeqRecord object's reverse_complement method takes a number of
optional arguments corresponding to properties of the record. Setting
these arguments to True means copy the old values, while False means
drop the old values and use the default value. You can alternatively
provide the new desired value instead.
  Consider this example record:
<<breaklines=true,breaksymbolleft=]{pycon}
  >>> from Bio import SeqIO
  >>> record = SeqIO.read("NC_005816.gb", "genbank")
  >>> print("%s %i %i %i %i" % (record.id, len(record),
len(record.features), len(record.dbxrefs), len(record.annotations)))
  NC_005816.1 9609 41 1 13
>>

  Here we take the reverse complement and specify a new identifier --
but notice how most of the annotation is dropped (but not the features):
<<breaklines=true,breaksymbolleft=]{pycon}
  >>> rc = record.reverse_complement(id="TESTING")
  >>> print("%s %i %i %i %i" % (rc.id, len(rc), len(rc.features),
len(rc.dbxrefs), len(rc.annotations)))
  TESTING 9609 41 0 0
>>

-----------------------------------


 (1) http://biopython.org/docs/1.79/api/Bio.SeqRecord.html
 (2) http://biopython.org/docs/1.79/api/Bio.SeqFeature.html
 (3) https://raw.githubusercontent.com/biopython/biopython/master/Tests/
   GenBank/NC_005816.fna
 (4) https://raw.githubusercontent.com/biopython/biopython/master/Tests/
   GenBank/NC_005816.gb


Chapter 5  Sequence Input/Output
********************************

  In this chapter we'll discuss in more detail the Bio.SeqIO module,
which was briefly introduced in Chapter ?? and also used in Chapter ??.
This aims to provide a simple interface for working with assorted
sequence file formats in a uniform way. See also the Bio.SeqIO wiki page
(http://biopython.org/wiki/SeqIO), and the built in documentation (also
online (1)):
<<>>> from Bio import SeqIO
  >>> help(SeqIO)
  ...
>>

  The "catch" is that you have to work with SeqRecord objects (see
Chapter ??), which contain a Seq object (see Chapter ??) plus annotation
like an identifier and description. Note that when dealing with very
large FASTA or FASTQ files, the overhead of working with all these
objects can make scripts too slow. In this case consider the low-level
SimpleFastaParser and FastqGeneralIterator parsers which return just a
tuple of strings for each record (see Section ??).


5.1 Parsing or Reading Sequences
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=


  The workhorse function Bio.SeqIO.parse() is used to read in sequence
data as SeqRecord objects. This function expects two arguments:


   1. The first argument is a handle to read the data from, or a
   filename. A handle is typically a file opened for reading, but could
   be the output from a command line program, or data downloaded from
   the internet (see Section ??). See Section ?? for more about handles.

   2. The second argument is a lower case string specifying sequence
   format -- we don't try and guess the file format for you! See
   http://biopython.org/wiki/SeqIO for a full listing of supported
   formats. 

  The Bio.SeqIO.parse() function returns an iterator which gives
SeqRecord objects. Iterators are typically used in a for loop as shown
below.
  Sometimes you'll find yourself dealing with files which contain only a
single record. For this situation use the function Bio.SeqIO.read()
which takes the same arguments. Provided there is one and only one
record in the file, this is returned as a SeqRecord object. Otherwise an
exception is raised.


5.1.1 Reading Sequence Files
============================

  In general Bio.SeqIO.parse() is used to read in sequence files as
SeqRecord objects, and is typically used with a for loop like this:
<<from Bio import SeqIO

  for seq_record in SeqIO.parse("ls_orchid.fasta", "fasta"):
      print(seq_record.id)
      print(repr(seq_record.seq))
      print(len(seq_record))
>>

  The above example is repeated from the introduction in Section ??, and
will load the orchid DNA sequences in the FASTA format file
ls_orchid.fasta (2). If instead you wanted to load a GenBank format file
like ls_orchid.gbk (3) then all you need to do is change the filename
and the format string:
<<from Bio import SeqIO

  for seq_record in SeqIO.parse("ls_orchid.gbk", "genbank"):
      print(seq_record.id)
      print(repr(seq_record.seq))
      print(len(seq_record))
>>

  Similarly, if you wanted to read in a file in another file format,
then assuming Bio.SeqIO.parse() supports it you would just need to
change the format string as appropriate, for example "swiss" for
SwissProt files or "embl" for EMBL text files. There is a full listing
on the wiki page (http://biopython.org/wiki/SeqIO) and in the built in
documentation (also online (4)).
  Another very common way to use a Python iterator is within a list
comprehension (or a generator expression). For example, if all you
wanted to extract from the file was a list of the record identifiers we
can easily do this with the following list comprehension:
<<>>> from Bio import SeqIO
  >>> identifiers = [seq_record.id for seq_record in
SeqIO.parse("ls_orchid.gbk", "genbank")]
  >>> identifiers
  ['Z78533.1', 'Z78532.1', 'Z78531.1', 'Z78530.1', 'Z78529.1',
'Z78527.1', ..., 'Z78439.1']
>>

  There are more examples using SeqIO.parse() in a list comprehension
like this in Section ?? (e.g. for plotting sequence lengths or GC%).


5.1.2 Iterating over the records in a sequence file
===================================================

  In the above examples, we have usually used a for loop to iterate over
all the records one by one. You can use the for loop with all sorts of
Python objects (including lists, tuples and strings) which support the
iteration interface.
  The object returned by Bio.SeqIO is actually an iterator which returns
SeqRecord objects. You get to see each record in turn, but once and only
once. The plus point is that an iterator can save you memory when
dealing with large files.
  Instead of using a for loop, can also use the next() function on an
iterator to step through the entries, like this:
<<from Bio import SeqIO

  record_iterator = SeqIO.parse("ls_orchid.fasta", "fasta")

  first_record = next(record_iterator)
  print(first_record.id)
  print(first_record.description)

  second_record = next(record_iterator)
  print(second_record.id)
  print(second_record.description)
>>

  Note that if you try to use next() and there are no more results,
you'll get the special StopIteration exception.
  One special case to consider is when your sequence files have multiple
records, but you only want the first one. In this situation the
following code is very concise:
<<from Bio import SeqIO

  first_record = next(SeqIO.parse("ls_orchid.gbk", "genbank"))
>>

  A word of warning here -- using the next() function like this will
silently ignore any additional records in the file. If your files have
one and only one record, like some of the online examples later in this
chapter, or a GenBank file for a single chromosome, then use the new
Bio.SeqIO.read() function instead. This will check there are no extra
unexpected records present.


5.1.3 Getting a list of the records in a sequence file
======================================================

  In the previous section we talked about the fact that
Bio.SeqIO.parse() gives you a SeqRecord iterator, and that you get the
records one by one. Very often you need to be able to access the records
in any order. The Python list data type is perfect for this, and we can
turn the record iterator into a list of SeqRecord objects using the
built-in Python function list() like so:
<<from Bio import SeqIO

  records = list(SeqIO.parse("ls_orchid.gbk", "genbank"))

  print("Found %i records" % len(records))

  print("The last record")
  last_record = records[-1]  # using Python's list tricks
  print(last_record.id)
  print(repr(last_record.seq))
  print(len(last_record))

  print("The first record")
  first_record = records[0]  # remember, Python counts from zero
  print(first_record.id)
  print(repr(first_record.seq))
  print(len(first_record))
>>

  Giving:
<<Found 94 records
  The last record
  Z78439.1
  Seq('CATTGTTGAGATCACATAATAATTGATCGAGTTAATCTGGAGGATCTGTTTACT...GCC')
  592
  The first record
  Z78533.1
  Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGATGAGACCGTGG...CGC')
  740
>>

  You can of course still use a for loop with a list of SeqRecord
objects. Using a list is much more flexible than an iterator (for
example, you can determine the number of records from the length of the
list), but does need more memory because it will hold all the records in
memory at once.


5.1.4 Extracting data
=====================

  The SeqRecord object and its annotation structures are described more
fully in Chapter ??. As an example of how annotations are stored, we'll
look at the output from parsing the first record in the GenBank file
ls_orchid.gbk (5).
<<from Bio import SeqIO

  record_iterator = SeqIO.parse("ls_orchid.gbk", "genbank")
  first_record = next(record_iterator)
  print(first_record)
>>

  That should give something like this:
<<ID: Z78533.1
  Name: Z78533
  Description: C.irapeanum 5.8S rRNA gene and ITS1 and ITS2 DNA.
  Number of features: 5
  /sequence_version=1
  /source=Cypripedium irapeanum
  /taxonomy=['Eukaryota', 'Viridiplantae', 'Streptophyta', ...,
'Cypripedium']
  /keywords=['5.8S ribosomal RNA', '5.8S rRNA gene', ..., 'ITS1',
'ITS2']
  /references=[...]
  /accessions=['Z78533']
  /data_file_division=PLN
  /date=30-NOV-2006
  /organism=Cypripedium irapeanum
  /gi=2765658
  Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGATGAGACCGTGG...CGC')
>>

  This gives a human readable summary of most of the annotation data for
the SeqRecord. For this example we're going to use the .annotations
attribute which is just a Python dictionary. The contents of this
annotations dictionary were shown when we printed the record above. You
can also print them out directly: 
<<print(first_record.annotations)
>>
   Like any Python dictionary, you can easily get a list of the keys: 
<<print(first_record.annotations.keys())
>>
   or values: 
<<print(first_record.annotations.values())
>>

  In general, the annotation values are strings, or lists of strings.
One special case is any references in the file get stored as reference
objects.
  Suppose you wanted to extract a list of the species from the
ls_orchid.gbk (6) GenBank file. The information we want, Cypripedium
irapeanum, is held in the annotations dictionary under `source' and
`organism', which we can access like this:
<<>>> print(first_record.annotations["source"])
  Cypripedium irapeanum
>>

  or:
<<>>> print(first_record.annotations["organism"])
  Cypripedium irapeanum
>>

  In general, `organism' is used for the scientific name (in Latin, e.g.
Arabidopsis thaliana), while `source' will often be the common name
(e.g. thale cress). In this example, as is often the case, the two
fields are identical.
  Now let's go through all the records, building up a list of the
species each orchid sequence is from:
<<from Bio import SeqIO

  all_species = []
  for seq_record in SeqIO.parse("ls_orchid.gbk", "genbank"):
      all_species.append(seq_record.annotations["organism"])
  print(all_species)
>>

  Another way of writing this code is to use a list comprehension:
<<from Bio import SeqIO

  all_species = [
      seq_record.annotations["organism"]
      for seq_record in SeqIO.parse("ls_orchid.gbk", "genbank")
  ]
  print(all_species)
>>

  In either case, the result is:
<<['Cypripedium irapeanum', 'Cypripedium californicum', ...,
'Paphiopedilum barbatum']
>>

  Great. That was pretty easy because GenBank files are annotated in a
standardised way.
  Now, let's suppose you wanted to extract a list of the species from a
FASTA file, rather than the GenBank file. The bad news is you will have
to write some code to extract the data you want from the record's
description line - if the information is in the file in the first place!
Our example FASTA format file ls_orchid.fasta (7) starts like this:
<<>gi|2765658|emb|Z78533.1|CIZ78533 C.irapeanum 5.8S rRNA gene and ITS1
and ITS2 DNA
  CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGATGAGACCGTGGAATAAACGATCGAGTG
  AATCCGGAGGACCGGTGTACTCAGCTCACCGGGGGCATTGCTCCCGTGGTGACCCTGATTTGTTGTTGGG
  ...
>>

  You can check by hand, but for every record the species name is in the
description line as the second word. This means if we break up each
record's .description at the spaces, then the species is there as field
number one (field zero is the record identifier). That means we can do
this:
<<from Bio import SeqIO

  all_species = []
  for seq_record in SeqIO.parse("ls_orchid.fasta", "fasta"):
      all_species.append(seq_record.description.split()[1])
  print(all_species)
>>

  This gives:
<<['C.irapeanum', 'C.californicum', 'C.fasciculatum', 'C.margaritaceum',
..., 'P.barbatum']
>>

  The concise alternative using list comprehensions would be:
<<from Bio import SeqIO

  all_species == [
      seq_record.description.split()[1]
      for seq_record in SeqIO.parse("ls_orchid.fasta", "fasta")
  ]
  print(all_species)
>>

  In general, extracting information from the FASTA description line is
not very nice. If you can get your sequences in a well annotated file
format like GenBank or EMBL, then this sort of annotation information is
much easier to deal with.


5.1.5 Modifying data
====================

  In the previous section, we demonstrated how to extract data from a
SeqRecord. Another common task is to alter this data. The attributes of
a SeqRecord can be modified directly, for example:
<<>>> from Bio import SeqIO
  >>> record_iterator = SeqIO.parse("ls_orchid.fasta", "fasta")
  >>> first_record = next(record_iterator)
  >>> first_record.id
  'gi|2765658|emb|Z78533.1|CIZ78533'
  >>> first_record.id = "new_id"
  >>> first_record.id
  'new_id'
>>

  Note, if you want to change the way FASTA is output when written to a
file (see Section ??), then you should modify both the id and
description attributes. To ensure the correct behaviour, it is best to
include the id plus a space at the start of the desired description:
<<>>> from Bio import SeqIO
  >>> record_iterator = SeqIO.parse("ls_orchid.fasta", "fasta")
  >>> first_record = next(record_iterator)
  >>> first_record.id = "new_id"
  >>> first_record.description = first_record.id + " " + "desired new
description"
  >>> print(first_record.format("fasta")[:200])
  >new_id desired new description
  CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGATGAGACCGTGGAATAAA
  CGATCGAGTGAATCCGGAGGACCGGTGTACTCAGCTCACCGGGGGCATTGCTCCCGTGGT
  GACCCTGATTTGTTGTTGGGCCGCCTCGGGAGCGTCCATGGCGGGT
>>



5.2 Parsing sequences from compressed files
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*

    In the previous section, we looked at parsing sequence data from a
file. Instead of using a filename, you can give Bio.SeqIO a handle (see
Section ??), and in this section we'll use handles to parse sequence
from compressed files.
  As you'll have seen above, we can use Bio.SeqIO.read() or
Bio.SeqIO.parse() with a filename - for instance this quick example
calculates the total length of the sequences in a multiple record
GenBank file using a generator expression:
<<>>> from Bio import SeqIO
  >>> print(sum(len(r) for r in SeqIO.parse("ls_orchid.gbk", "gb")))
  67518
>>

  Here we use a file handle instead, using the with statement to close
the handle automatically:
<<>>> from Bio import SeqIO
  >>> with open("ls_orchid.gbk") as handle:
  ...     print(sum(len(r) for r in SeqIO.parse(handle, "gb")))
  67518
>>

  Or, the old fashioned way where you manually close the handle:
<<>>> from Bio import SeqIO
  >>> handle = open("ls_orchid.gbk")
  >>> print(sum(len(r) for r in SeqIO.parse(handle, "gb")))
  67518
  >>> handle.close()
>>

  Now, suppose we have a gzip compressed file instead? These are very
commonly used on Linux. We can use Python's gzip module to open the
compressed file for reading - which gives us a handle object:
<<>>> import gzip
  >>> from Bio import SeqIO
  >>> with gzip.open("ls_orchid.gbk.gz", "rt") as handle:
  ...     print(sum(len(r) for r in SeqIO.parse(handle, "gb")))
  ...
  67518
>>

  Similarly if we had a bzip2 compressed file:
<<>>> import bz2
  >>> from Bio import SeqIO
  >>> with bz2.open("ls_orchid.gbk.bz2", "rt") as handle:
  ...     print(sum(len(r) for r in SeqIO.parse(handle, "gb")))
  ...
  67518
>>

  There is a gzip (GNU Zip) variant called BGZF (Blocked GNU Zip
Format), which can be treated like an ordinary gzip file for reading,
but has advantages for random access later which we'll talk about later
in Section ??.


5.3 Parsing sequences from the net
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=

    In the previous sections, we looked at parsing sequence data from a
file (using a filename or handle), and from compressed files (using a
handle). Here we'll use Bio.SeqIO with another type of handle, a network
connection, to download and parse sequences from the internet.
  Note that just because you can download sequence data and parse it
into a SeqRecord object in one go doesn't mean this is a good idea. In
general, you should probably download sequences once and save them to a
file for reuse.


5.3.1 Parsing GenBank records from the net
==========================================
    Section ?? talks about the Entrez EFetch interface in more detail,
but for now let's just connect to the NCBI and get a few Opuntia
(prickly-pear) sequences from GenBank using their GI numbers.
  First of all, let's fetch just one record. If you don't care about the
annotations and features downloading a FASTA file is a good choice as
these are compact. Now remember, when you expect the handle to contain
one and only one record, use the Bio.SeqIO.read() function:
<<from Bio import Entrez
  from Bio import SeqIO

  Entrez.email = "A.N.Other@example.com"
  with Entrez.efetch(
      db="nucleotide", rettype="fasta", retmode="text", id="6273291"
  ) as handle:
      seq_record = SeqIO.read(handle, "fasta")
  print("%s with %i features" % (seq_record.id,
len(seq_record.features)))
>>

  Expected output:
<<gi|6273291|gb|AF191665.1|AF191665 with 0 features
>>

  The NCBI will also let you ask for the file in other formats, in
particular as a GenBank file. Until Easter 2009, the Entrez EFetch API
let you use "genbank" as the return type, however the NCBI now insist on
using the official return types of "gb" (or "gp" for proteins) as
described on EFetch for Sequence and other Molecular Biology
Databases (8). As a result, in Biopython 1.50 onwards, we support "gb"
as an alias for "genbank" in Bio.SeqIO.
<<from Bio import Entrez
  from Bio import SeqIO

  Entrez.email = "A.N.Other@example.com"
  with Entrez.efetch(
      db="nucleotide", rettype="gb", retmode="text", id="6273291"
  ) as handle:
      seq_record = SeqIO.read(handle, "gb")  # using "gb" as an alias
for "genbank"
  print("%s with %i features" % (seq_record.id,
len(seq_record.features)))
>>

  The expected output of this example is:
<<AF191665.1 with 3 features
>>

  Notice this time we have three features.
  Now let's fetch several records. This time the handle contains
multiple records, so we must use the Bio.SeqIO.parse() function:
<<from Bio import Entrez
  from Bio import SeqIO

  Entrez.email = "A.N.Other@example.com"
  with Entrez.efetch(
      db="nucleotide", rettype="gb", retmode="text",
id="6273291,6273290,6273289"
  ) as handle:
      for seq_record in SeqIO.parse(handle, "gb"):
          print("%s %s..." % (seq_record.id,
seq_record.description[:50]))
          print(
              "Sequence length %i, %i features, from: %s"
              % (
                  len(seq_record),
                  len(seq_record.features),
                  seq_record.annotations["source"],
              )
          )
>>

  That should give the following output:
<<AF191665.1 Opuntia marenae rpl16 gene; chloroplast gene for c...
  Sequence length 902, 3 features, from: chloroplast Opuntia marenae
  AF191664.1 Opuntia clavata rpl16 gene; chloroplast gene for c...
  Sequence length 899, 3 features, from: chloroplast Grusonia clavata
  AF191663.1 Opuntia bradtiana rpl16 gene; chloroplast gene for...
  Sequence length 899, 3 features, from: chloroplast Opuntia bradtianaa
>>

  See Chapter ?? for more about the Bio.Entrez module, and make sure to
read about the NCBI guidelines for using Entrez (Section ??).


5.3.2 Parsing SwissProt sequences from the net
==============================================
    Now let's use a handle to download a SwissProt file from ExPASy,
something covered in more depth in Chapter ??. As mentioned above, when
you expect the handle to contain one and only one record, use the
Bio.SeqIO.read() function:
<<from Bio import ExPASy
  from Bio import SeqIO

  with ExPASy.get_sprot_raw("O23729") as handle:
      seq_record = SeqIO.read(handle, "swiss")
  print(seq_record.id)
  print(seq_record.name)
  print(seq_record.description)
  print(repr(seq_record.seq))
  print("Length %i" % len(seq_record))
  print(seq_record.annotations["keywords"])
>>

  Assuming your network connection is OK, you should get back:
<<O23729
  CHS3_BROFI
  RecName: Full=Chalcone synthase 3; EC=2.3.1.74; AltName:
Full=Naringenin-chalcone synthase 3;
  Seq('MAPAMEEIRQAQRAEGPAAVLAIGTSTPPNALYQADYPDYYFRITKSEHLTELK...GAE')
  Length 394
  ['Acyltransferase', 'Flavonoid biosynthesis', 'Transferase']
>>



5.4 Sequence files as Dictionaries
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=


  Looping over the iterator returned by SeqIO.parse once will exhaust
the file. For self-indexed files, such as files in the twoBit format,
the return value of SeqIO.parse can also be used as a dictionary,
allowing random access to the sequence contents. As in this case parsing
is done on demand, the file must remain open as long as the sequence
data is being accessed:
<<>>> from Bio import SeqIO
  >>> handle = open("sequence.bigendian.2bit", "rb")
  >>> records = SeqIO.parse(handle, "twobit")
  >>> records.keys()
  dict_keys(['seq11111', 'seq222', 'seq3333', 'seq4', 'seq555', 'seq6'])
  >>> records['seq222']
  SeqRecord(seq=Seq('TTGATCGGTGACAAATTTTTTACAAAGAACTGTAGGACTTGCTACTTCTCC
CTC...ACA'), id='seq222', name='<unknown name>', description='<unknown
description>', dbxrefs=[])
  >>> records['seq222'].seq
  Seq('TTGATCGGTGACAAATTTTTTACAAAGAACTGTAGGACTTGCTACTTCTCCCTC...ACA')
  >>> handle.close()
  >>> records['seq222'].seq
  Traceback (most recent call last):
  ...
  ValueError: cannot retrieve sequence: file is closed
>>

  For other file formats, Bio.SeqIO provides three related functions
module which allow dictionary like random access to a multi-sequence
file. There is a trade off here between flexibility and memory usage. In
summary: 

   - Bio.SeqIO.to_dict() is the most flexible but also the most memory
   demanding option (see Section ??). This is basically a helper
   function to build a normal Python dictionary with each entry held as
   a SeqRecord object in memory, allowing you to modify the records. 
   - Bio.SeqIO.index() is a useful middle ground, acting like a read
   only dictionary and parsing sequences into SeqRecord objects on
   demand (see Section ??). 
   - Bio.SeqIO.index_db() also acts like a read only dictionary but
   stores the identifiers and file offsets in a file on disk (as an
   SQLite3 database), meaning it has very low memory requirements (see
   Section ??), but will be a little bit slower. 
   See the discussion for an broad overview (Section ??).


5.4.1 Sequence files as Dictionaries -- In memory
=================================================

  The next thing that we'll do with our ubiquitous orchid files is to
show how to index them and access them like a database using the Python
dictionary data type (like a hash in Perl). This is very useful for
moderately large files where you only need to access certain elements of
the file, and makes for a nice quick 'n dirty database. For dealing with
larger files where memory becomes a problem, see Section ?? below.
  You can use the function Bio.SeqIO.to_dict() to make a SeqRecord
dictionary (in memory). By default this will use each record's
identifier (i.e. the .id attribute) as the key. Let's try this using our
GenBank file:
<<>>> from Bio import SeqIO
  >>> orchid_dict = SeqIO.to_dict(SeqIO.parse("ls_orchid.gbk",
"genbank"))
>>

  There is just one required argument for Bio.SeqIO.to_dict(), a list or
generator giving SeqRecord objects. Here we have just used the output
from the SeqIO.parse function. As the name suggests, this returns a
Python dictionary.
  Since this variable orchid_dict is an ordinary Python dictionary, we
can look at all of the keys we have available:
<<>>> len(orchid_dict)
  94
>>

<<>>> list(orchid_dict.keys())
  ['Z78484.1', 'Z78464.1', 'Z78455.1', 'Z78442.1', 'Z78532.1',
'Z78453.1', ..., 'Z78471.1']
>>

  Under Python 3 the dictionary methods like ".keys()" and ".values()"
are iterators rather than lists.
  If you really want to, you can even look at all the records at once: 
<<>>> list(orchid_dict.values()) #lots of output!
  ...
>>

  We can access a single SeqRecord object via the keys and manipulate
the object as normal:
<<>>> seq_record = orchid_dict["Z78475.1"]
  >>> print(seq_record.description)
  P.supardii 5.8S rRNA gene and ITS1 and ITS2 DNA
  >>> seq_record.seq
  Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...GGT')
>>

  So, it is very easy to create an in memory "database" of our GenBank
records. Next we'll try this for the FASTA file instead.
  Note that those of you with prior Python experience should all be able
to construct a dictionary like this "by hand". However, typical
dictionary construction methods will not deal with the case of repeated
keys very nicely. Using the Bio.SeqIO.to_dict() will explicitly check
for duplicate keys, and raise an exception if any are found.


5.4.1.1 Specifying the dictionary keys
--------------------------------------

  Using the same code as above, but for the FASTA file instead:
<<from Bio import SeqIO

  orchid_dict = SeqIO.to_dict(SeqIO.parse("ls_orchid.fasta", "fasta"))
  print(orchid_dict.keys())
>>

  This time the keys are:
<<['gi|2765596|emb|Z78471.1|PDZ78471',
'gi|2765646|emb|Z78521.1|CCZ78521', ...
   ..., 'gi|2765613|emb|Z78488.1|PTZ78488',
'gi|2765583|emb|Z78458.1|PHZ78458']
>>

  You should recognise these strings from when we parsed the FASTA file
earlier in Section ??. Suppose you would rather have something else as
the keys - like the accession numbers. This brings us nicely to
SeqIO.to_dict()'s optional argument key_function, which lets you define
what to use as the dictionary key for your records.
  First you must write your own function to return the key you want (as
a string) when given a SeqRecord object. In general, the details of
function will depend on the sort of input records you are dealing with.
But for our orchids, we can just split up the record's identifier using
the "pipe" character (the vertical line) and return the fourth entry
(field three):
<<def get_accession(record):
      """"Given a SeqRecord, return the accession number as a string.

      e.g. "gi|2765613|emb|Z78488.1|PTZ78488" -> "Z78488.1"
      """
      parts = record.id.split("|")
      assert len(parts) == 5 and parts[0] == "gi" and parts[2] == "emb"
      return parts[3]
>>

  Then we can give this function to the SeqIO.to_dict() function to use
in building the dictionary:
<<from Bio import SeqIO

  orchid_dict = SeqIO.to_dict(
      SeqIO.parse("ls_orchid.fasta", "fasta"),
key_function=get_accession
  )
  print(orchid_dict.keys())
>>

  Finally, as desired, the new dictionary keys:
<<>>> print(orchid_dict.keys())
  ['Z78484.1', 'Z78464.1', 'Z78455.1', 'Z78442.1', 'Z78532.1',
'Z78453.1', ..., 'Z78471.1']
>>

  Not too complicated, I hope!


5.4.1.2 Indexing a dictionary using the SEGUID checksum
-------------------------------------------------------

  To give another example of working with dictionaries of SeqRecord
objects, we'll use the SEGUID checksum function. This is a relatively
recent checksum, and collisions should be very rare (i.e. two different
sequences with the same checksum), an improvement on the CRC64 checksum.
  Once again, working with the orchids GenBank file:
<<from Bio import SeqIO
  from Bio.SeqUtils.CheckSum import seguid

  for record in SeqIO.parse("ls_orchid.gbk", "genbank"):
      print(record.id, seguid(record.seq))
>>

  This should give:
<<Z78533.1 JUEoWn6DPhgZ9nAyowsgtoD9TTo
  Z78532.1 MN/s0q9zDoCVEEc+k/IFwCNF2pY
  ...
  Z78439.1 H+JfaShya/4yyAj7IbMqgNkxdxQ
>>

  Now, recall the Bio.SeqIO.to_dict() function's key_function argument
expects a function which turns a SeqRecord into a string. We can't use
the seguid() function directly because it expects to be given a Seq
object (or a string). However, we can use Python's lambda feature to
create a "one off" function to give to Bio.SeqIO.to_dict() instead:
<<>>> from Bio import SeqIO
  >>> from Bio.SeqUtils.CheckSum import seguid
  >>> seguid_dict = SeqIO.to_dict(SeqIO.parse("ls_orchid.gbk",
"genbank"),
  ...                             lambda rec : seguid(rec.seq))
  >>> record = seguid_dict["MN/s0q9zDoCVEEc+k/IFwCNF2pY"]
  >>> print(record.id)
  Z78532.1
  >>> print(record.description)
  C.californicum 5.8S rRNA gene and ITS1 and ITS2 DNA
>>

  That should have retrieved the record Z78532.1, the second entry in
the file.


5.4.2 Sequence files as Dictionaries -- Indexed files
=====================================================

  As the previous couple of examples tried to illustrate, using
Bio.SeqIO.to_dict() is very flexible. However, because it holds
everything in memory, the size of file you can work with is limited by
your computer's RAM. In general, this will only work on small to medium
files.
  For larger files you should consider Bio.SeqIO.index(), which works a
little differently. Although it still returns a dictionary like object,
this does not keep everything in memory. Instead, it just records where
each record is within the file -- when you ask for a particular record,
it then parses it on demand.
  As an example, let's use the same GenBank file as before:
<<>>> from Bio import SeqIO
  >>> orchid_dict = SeqIO.index("ls_orchid.gbk", "genbank")
  >>> len(orchid_dict)
  94
>>

<<>>> orchid_dict.keys()
  ['Z78484.1', 'Z78464.1', 'Z78455.1', 'Z78442.1', 'Z78532.1',
'Z78453.1', ..., 'Z78471.1']
>>

<<>>> seq_record = orchid_dict["Z78475.1"]
  >>> print(seq_record.description)
  P.supardii 5.8S rRNA gene and ITS1 and ITS2 DNA
  >>> seq_record.seq
  Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...GGT')
  >>> orchid_dict.close()
>>

  Note that Bio.SeqIO.index() won't take a handle, but only a filename.
There are good reasons for this, but it is a little technical. The
second argument is the file format (a lower case string as used in the
other Bio.SeqIO functions). You can use many other simple file formats,
including FASTA and FASTQ files (see the example in Section ??).
However, alignment formats like PHYLIP or Clustal are not supported.
Finally as an optional argument you can supply a key function.
  Here is the same example using the FASTA file - all we change is the
filename and the format name:
<<>>> from Bio import SeqIO
  >>> orchid_dict = SeqIO.index("ls_orchid.fasta", "fasta")
  >>> len(orchid_dict)
  94
  >>> orchid_dict.keys()
  ['gi|2765596|emb|Z78471.1|PDZ78471',
'gi|2765646|emb|Z78521.1|CCZ78521', ...
   ..., 'gi|2765613|emb|Z78488.1|PTZ78488',
'gi|2765583|emb|Z78458.1|PHZ78458']
>>



5.4.2.1 Specifying the dictionary keys
--------------------------------------

  Suppose you want to use the same keys as before? Much like with the
Bio.SeqIO.to_dict() example in Section ??, you'll need to write a tiny
function to map from the FASTA identifier (as a string) to the key you
want:
<<def get_acc(identifier):
      """"Given a SeqRecord identifier string, return the accession
number as a string.

      e.g. "gi|2765613|emb|Z78488.1|PTZ78488" -> "Z78488.1"
      """
      parts = identifier.split("|")
      assert len(parts) == 5 and parts[0] == "gi" and parts[2] == "emb"
      return parts[3]
>>

  Then we can give this function to the Bio.SeqIO.index() function to
use in building the dictionary:
<<>>> from Bio import SeqIO
  >>> orchid_dict = SeqIO.index("ls_orchid.fasta", "fasta",
key_function=get_acc)
  >>> print(orchid_dict.keys())
  ['Z78484.1', 'Z78464.1', 'Z78455.1', 'Z78442.1', 'Z78532.1',
'Z78453.1', ..., 'Z78471.1']
>>

  Easy when you know how?


5.4.2.2 Getting the raw data for a record
-----------------------------------------

  The dictionary-like object from Bio.SeqIO.index() gives you each entry
as a SeqRecord object. However, it is sometimes useful to be able to get
the original raw data straight from the file. For this use the get_raw()
method which takes a single argument (the record identifier) and returns
a bytes string (extracted from the file without modification).
  A motivating example is extracting a subset of a records from a large
file where either Bio.SeqIO.write() does not (yet) support the output
file format (e.g. the plain text SwissProt file format) or where you
need to preserve the text exactly (e.g. GenBank or EMBL output from
Biopython does not yet preserve every last bit of annotation).
  Let's suppose you have download the whole of UniProt in the plain text
SwissPort file format from their FTP site
(ftp://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgeba
se/complete/uniprot_sprot.dat.gz) and uncompressed it as the file
uniprot_sprot.dat, and you want to extract just a few records from it:
<<>>> from Bio import SeqIO
  >>> uniprot = SeqIO.index("uniprot_sprot.dat", "swiss")
  >>> with open("selected.dat", "wb") as out_handle:
  ...     for acc in ["P33487", "P19801", "P13689", "Q8JZQ5", "Q9TRC7"]:
  ...         out_handle.write(uniprot.get_raw(acc))
  ...
>>

  Note with Python 3 onwards, we have to open the file for writing in
binary mode because the get_raw() method returns bytes strings.
  There is a longer example in Section ?? using the SeqIO.index()
function to sort a large sequence file (without loading everything into
memory at once).


5.4.3 Sequence files as Dictionaries -- Database indexed files
==============================================================

  Biopython 1.57 introduced an alternative, Bio.SeqIO.index_db(), which
can work on even extremely large files since it stores the record
information as a file on disk (using an SQLite3 database) rather than in
memory. Also, you can index multiple files together (providing all the
record identifiers are unique).
  The Bio.SeqIO.index() function takes three required arguments: 

   - Index filename, we suggest using something ending .idx. This index
   file is actually an SQLite3 database. 
   - List of sequence filenames to index (or a single filename) 
   - File format (lower case string as used in the rest of the SeqIO
   module). 

  As an example, consider the GenBank flat file releases from the NCBI
FTP site, ftp://ftp.ncbi.nih.gov/genbank/, which are gzip compressed
GenBank files.
  As of GenBank release 210, there are 38 files making up the viral
sequences, gbvrl1.seq, ..., gbvrl38.seq, taking about 8GB on disk once
decompressed, and containing in total nearly two million records.
  If you were interested in the viruses, you could download all the
virus files from the command line very easily with the rsync command,
and then decompress them with gunzip:
<<# For illustration only, see reduced example below
  $ rsync -avP "ftp.ncbi.nih.gov::genbank/gbvrl*.seq.gz" .
  $ gunzip gbvrl*.seq.gz
>>

  Unless you care about viruses, that's a lot of data to download just
for this example - so let's download just the first four chunks (about
25MB each compressed), and decompress them (taking in all about 1GB of
space):
<<# Reduced example, download only the first four chunks
  $ curl -O ftp://ftp.ncbi.nih.gov/genbank/gbvrl1.seq.gz
  $ curl -O ftp://ftp.ncbi.nih.gov/genbank/gbvrl2.seq.gz
  $ curl -O ftp://ftp.ncbi.nih.gov/genbank/gbvrl3.seq.gz
  $ curl -O ftp://ftp.ncbi.nih.gov/genbank/gbvrl4.seq.gz
  $ gunzip gbvrl*.seq.gz
>>

  Now, in Python, index these GenBank files as follows:
<<>>> import glob
  >>> from Bio import SeqIO
  >>> files = glob.glob("gbvrl*.seq")
  >>> print("%i files to index" % len(files))
  4
  >>> gb_vrl = SeqIO.index_db("gbvrl.idx", files, "genbank")
  >>> print("%i sequences indexed" % len(gb_vrl))
  272960 sequences indexed
>>

  Indexing the full set of virus GenBank files took about ten minutes on
my machine, just the first four files took about a minute or so.
  However, once done, repeating this will reload the index file
gbvrl.idx in a fraction of a second.
  You can use the index as a read only Python dictionary - without
having to worry about which file the sequence comes from, e.g.
<<>>> print(gb_vrl["AB811634.1"].description)
  Equine encephalosis virus NS3 gene, complete cds, isolate: Kimron1.
>>



5.4.3.1 Getting the raw data for a record
-----------------------------------------

  Just as with the Bio.SeqIO.index() function discussed above in
Section ??, the dictionary like object also lets you get at the raw
bytes of each record:
<<>>> print(gb_vrl.get_raw("AB811634.1"))
  LOCUS       AB811634                 723 bp    RNA     linear   VRL
17-JUN-2015
  DEFINITION  Equine encephalosis virus NS3 gene, complete cds, isolate:
Kimron1.
  ACCESSION   AB811634
  ...
  //
>>



5.4.4 Indexing compressed files
===============================

  Very often when you are indexing a sequence file it can be quite large
-- so you may want to compress it on disk. Unfortunately efficient
random access is difficult with the more common file formats like gzip
and bzip2. In this setting, BGZF (Blocked GNU Zip Format) can be very
helpful. This is a variant of gzip (and can be decompressed using
standard gzip tools) popularised by the BAM file format, samtools (9),
and tabix (10).
  To create a BGZF compressed file you can use the command line tool
bgzip which comes with samtools. In our examples we use a filename
extension *.bgz, so they can be distinguished from normal gzipped files
(named *.gz). You can also use the Bio.bgzf module to read and write
BGZF files from within Python.
  The Bio.SeqIO.index() and Bio.SeqIO.index_db() can both be used with
BGZF compressed files. For example, if you started with an uncompressed
GenBank file:
<<>>> from Bio import SeqIO
  >>> orchid_dict = SeqIO.index("ls_orchid.gbk", "genbank")
  >>> len(orchid_dict)
  94
  >>> orchid_dict.close()
>>

  You could compress this (while keeping the original file) at the
command line using the following command -- but don't worry, the
compressed file is already included with the other example files:
<<$ bgzip -c ls_orchid.gbk > ls_orchid.gbk.bgz
>>

  You can use the compressed file in exactly the same way:
<<>>> from Bio import SeqIO
  >>> orchid_dict = SeqIO.index("ls_orchid.gbk.bgz", "genbank")
  >>> len(orchid_dict)
  94
  >>> orchid_dict.close()
>>

  or:
<<>>> from Bio import SeqIO
  >>> orchid_dict = SeqIO.index_db("ls_orchid.gbk.bgz.idx",
"ls_orchid.gbk.bgz", "genbank")
  >>> len(orchid_dict)
  94
  >>> orchid_dict.close()
>>

  The SeqIO indexing automatically detects the BGZF compression. Note
that you can't use the same index file for the uncompressed and
compressed files.


5.4.5 Discussion
================

  So, which of these methods should you use and why? It depends on what
you are trying to do (and how much data you are dealing with). However,
in general picking Bio.SeqIO.index() is a good starting point. If you
are dealing with millions of records, multiple files, or repeated
analyses, then look at Bio.SeqIO.index_db().
  Reasons to choose Bio.SeqIO.to_dict() over either Bio.SeqIO.index() or
Bio.SeqIO.index_db() boil down to a need for flexibility despite its
high memory needs. The advantage of storing the SeqRecord objects in
memory is they can be changed, added to, or removed at will. In addition
to the downside of high memory consumption, indexing can also take
longer because all the records must be fully parsed.
  Both Bio.SeqIO.index() and Bio.SeqIO.index_db() only parse records on
demand. When indexing, they scan the file once looking for the start of
each record and do as little work as possible to extract the identifier.
  Reasons to choose Bio.SeqIO.index() over Bio.SeqIO.index_db() include:


   - Faster to build the index (more noticeable in simple file formats) 
   - Slightly faster access as SeqRecord objects (but the difference is
   only really noticeable for simple to parse file formats). 
   - Can use any immutable Python object as the dictionary keys (e.g. a
   tuple of strings, or a frozen set) not just strings. 
   - Don't need to worry about the index database being out of date if
   the sequence file being indexed has changed. 

  Reasons to choose Bio.SeqIO.index_db() over Bio.SeqIO.index() include:


   - Not memory limited -- this is already important with files from
   second generation sequencing where 10s of millions of sequences are
   common, and using Bio.SeqIO.index() can require more than 4GB of RAM
   and therefore a 64bit version of Python. 
   - Because the index is kept on disk, it can be reused. Although
   building the index database file takes longer, if you have a script
   which will be rerun on the same datafiles in future, this could save
   time in the long run. 
   - Indexing multiple files together 
   - The get_raw() method can be much faster, since for most file
   formats the length of each record is stored as well as its offset. 



5.5 Writing Sequence Files
*=*=*=*=*=*=*=*=*=*=*=*=*=


  We've talked about using Bio.SeqIO.parse() for sequence input (reading
files), and now we'll look at Bio.SeqIO.write() which is for sequence
output (writing files). This is a function taking three arguments: some
SeqRecord objects, a handle or filename to write to, and a sequence
format.
  Here is an example, where we start by creating a few SeqRecord objects
the hard way (by hand, rather than by loading them from a file):
<<from Bio.Seq import Seq
  from Bio.SeqRecord import SeqRecord

  rec1 = SeqRecord(
      Seq(
          "MMYQQGCFAGGTVLRLAKDLAENNRGARVLVVCSEITAVTFRGPSETHLDSMVGQALFGD"
          "GAGAVIVGSDPDLSVERPLYELVWTGATLLPDSEGAIDGHLREVGLTFHLLKDVPGLISK"
          "NIEKSLKEAFTPLGISDWNSTFWIAHPGGPAILDQVEAKLGLKEEKMRATREVLSEYGNM"
          "SSAC",
      ),
      id="gi|14150838|gb|AAK54648.1|AF376133_1",
      description="chalcone synthase [Cucumis sativus]",
  )

  rec2 = SeqRecord(
      Seq(
          "YPDYYFRITNREHKAELKEKFQRMCDKSMIKKRYMYLTEEILKENPSMCEYMAPSLDARQ"
          "DMVVVEIPKLGKEAAVKAIKEWGQ",
      ),
      id="gi|13919613|gb|AAK33142.1|",
      description="chalcone synthase [Fragaria vesca subsp. bracteata]",
  )

  rec3 = SeqRecord(
      Seq(
          "MVTVEEFRRAQCAEGPATVMAIGTATPSNCVDQSTYPDYYFRITNSEHKVELKEKFKRMC"
          "EKSMIKKRYMHLTEEILKENPNICAYMAPSLDARQDIVVVEVPKLGKEAAQKAIKEWGQP"
          "KSKITHLVFCTTSGVDMPGCDYQLTKLLGLRPSVKRFMMYQQGCFAGGTVLRMAKDLAEN"
          "NKGARVLVVCSEITAVTFRGPNDTHLDSLVGQALFGDGAAAVIIGSDPIPEVERPLFELV"
          "SAAQTLLPDSEGAIDGHLREVGLTFHLLKDVPGLISKNIEKSLVEAFQPLGISDWNSLFW"
          "IAHPGGPAILDQVELKLGLKQEKLKATRKVLSNYGNMSSACVLFILDEMRKASAKEGLGT"
          "TGEGLEWGVLFGFGPGLTVETVVLHSVAT",
      ),
      id="gi|13925890|gb|AAK49457.1|",
      description="chalcone synthase [Nicotiana tabacum]",
  )

  my_records = [rec1, rec2, rec3]
>>

  Now we have a list of SeqRecord objects, we'll write them to a FASTA
format file:
<<from Bio import SeqIO

  SeqIO.write(my_records, "my_example.faa", "fasta")
>>

  And if you open this file in your favourite text editor it should look
like this:
<<>gi|14150838|gb|AAK54648.1|AF376133_1 chalcone synthase [Cucumis
sativus]
  MMYQQGCFAGGTVLRLAKDLAENNRGARVLVVCSEITAVTFRGPSETHLDSMVGQALFGD
  GAGAVIVGSDPDLSVERPLYELVWTGATLLPDSEGAIDGHLREVGLTFHLLKDVPGLISK
  NIEKSLKEAFTPLGISDWNSTFWIAHPGGPAILDQVEAKLGLKEEKMRATREVLSEYGNM
  SSAC
  >gi|13919613|gb|AAK33142.1| chalcone synthase [Fragaria vesca subsp.
bracteata]
  YPDYYFRITNREHKAELKEKFQRMCDKSMIKKRYMYLTEEILKENPSMCEYMAPSLDARQ
  DMVVVEIPKLGKEAAVKAIKEWGQ
  >gi|13925890|gb|AAK49457.1| chalcone synthase [Nicotiana tabacum]
  MVTVEEFRRAQCAEGPATVMAIGTATPSNCVDQSTYPDYYFRITNSEHKVELKEKFKRMC
  EKSMIKKRYMHLTEEILKENPNICAYMAPSLDARQDIVVVEVPKLGKEAAQKAIKEWGQP
  KSKITHLVFCTTSGVDMPGCDYQLTKLLGLRPSVKRFMMYQQGCFAGGTVLRMAKDLAEN
  NKGARVLVVCSEITAVTFRGPNDTHLDSLVGQALFGDGAAAVIIGSDPIPEVERPLFELV
  SAAQTLLPDSEGAIDGHLREVGLTFHLLKDVPGLISKNIEKSLVEAFQPLGISDWNSLFW
  IAHPGGPAILDQVELKLGLKQEKLKATRKVLSNYGNMSSACVLFILDEMRKASAKEGLGT
  TGEGLEWGVLFGFGPGLTVETVVLHSVAT
>>

  Suppose you wanted to know how many records the Bio.SeqIO.write()
function wrote to the handle? If your records were in a list you could
just use len(my_records), however you can't do that when your records
come from a generator/iterator. The Bio.SeqIO.write() function returns
the number of SeqRecord objects written to the file.
  Note - If you tell the Bio.SeqIO.write() function to write to a file
that already exists, the old file will be overwritten without any
warning.


5.5.1 Round trips
=================

  Some people like their parsers to be "round-tripable", meaning if you
read in a file and write it back out again it is unchanged. This
requires that the parser must extract enough information to reproduce
the original file exactly. Bio.SeqIO does not aim to do this.
  As a trivial example, any line wrapping of the sequence data in FASTA
files is allowed. An identical SeqRecord would be given from parsing the
following two examples which differ only in their line breaks:
<<>YAL068C-7235.2170 Putative promoter sequence
  TACGAGAATAATTTCTCATCATCCAGCTTTAACACAAAATTCGCACAGTTTTCGTTAAGA
  GAACTTAACATTTTCTTATGACGTAAATGAAGTTTATATATAAATTTCCTTTTTATTGGA

  >YAL068C-7235.2170 Putative promoter sequence
  TACGAGAATAATTTCTCATCATCCAGCTTTAACACAAAATTCGCA
  CAGTTTTCGTTAAGAGAACTTAACATTTTCTTATGACGTAAATGA
  AGTTTATATATAAATTTCCTTTTTATTGGA
>>

  To make a round-tripable FASTA parser you would need to keep track of
where the sequence line breaks occurred, and this extra information is
usually pointless. Instead Biopython uses a default line wrapping of 60
characters on output. The same problem with white space applies in many
other file formats too. Another issue in some cases is that Biopython
does not (yet) preserve every last bit of annotation (e.g. GenBank and
EMBL).
  Occasionally preserving the original layout (with any quirks it may
have) is important. See Section ?? about the get_raw() method of the
Bio.SeqIO.index() dictionary-like object for one potential solution.


5.5.2 Converting between sequence file formats
==============================================

  In previous example we used a list of SeqRecord objects as input to
the Bio.SeqIO.write() function, but it will also accept a SeqRecord
iterator like we get from Bio.SeqIO.parse() -- this lets us do file
conversion by combining these two functions.
  For this example we'll read in the GenBank format file
ls_orchid.gbk (11) and write it out in FASTA format:
<<from Bio import SeqIO

  records = SeqIO.parse("ls_orchid.gbk", "genbank")
  count = SeqIO.write(records, "my_example.fasta", "fasta")
  print("Converted %i records" % count)
>>

  Still, that is a little bit complicated. So, because file conversion
is such a common task, there is a helper function letting you replace
that with just:
<<from Bio import SeqIO

  count = SeqIO.convert("ls_orchid.gbk", "genbank", "my_example.fasta",
"fasta")
  print("Converted %i records" % count)
>>

  The Bio.SeqIO.convert() function will take handles or filenames. Watch
out though -- if the output file already exists, it will overwrite it!
To find out more, see the built in help:
<<>>> from Bio import SeqIO
  >>> help(SeqIO.convert)
  ...
>>

  In principle, just by changing the filenames and the format names,
this code could be used to convert between any file formats available in
Biopython. However, writing some formats requires information (e.g.
quality scores) which other files formats don't contain. For example,
while you can turn a FASTQ file into a FASTA file, you can't do the
reverse. See also Sections ?? and ?? in the cookbook chapter which looks
at inter-converting between different FASTQ formats.
  Finally, as an added incentive for using the Bio.SeqIO.convert()
function (on top of the fact your code will be shorter), doing it this
way may also be faster! The reason for this is the convert function can
take advantage of several file format specific optimisations and tricks.


5.5.3 Converting a file of sequences to their reverse complements
=================================================================

  Suppose you had a file of nucleotide sequences, and you wanted to turn
it into a file containing their reverse complement sequences. This time
a little bit of work is required to transform the SeqRecord objects we
get from our input file into something suitable for saving to our output
file.
  To start with, we'll use Bio.SeqIO.parse() to load some nucleotide
sequences from a file, then print out their reverse complements using
the Seq object's built in .reverse_complement() method (see Section ??):
<<>>> from Bio import SeqIO
  >>> for record in SeqIO.parse("ls_orchid.gbk", "genbank"):
  ...     print(record.id)
  ...     print(record.seq.reverse_complement())
>>

  Now, if we want to save these reverse complements to a file, we'll
need to make SeqRecord objects. We can use the SeqRecord object's built
in .reverse_complement() method (see Section ??) but we must decide how
to name our new records.
  This is an excellent place to demonstrate the power of list
comprehensions which make a list in memory:
<<>>> from Bio import SeqIO
  >>> records = [rec.reverse_complement(id="rc_"+rec.id, description =
"reverse complement") \
  ...            for rec in SeqIO.parse("ls_orchid.fasta", "fasta")]
  >>> len(records)
  94
>>

  Now list comprehensions have a nice trick up their sleeves, you can
add a conditional statement:
<<>>> records = [rec.reverse_complement(id="rc_"+rec.id, description =
"reverse complement") \
  ...            for rec in SeqIO.parse("ls_orchid.fasta", "fasta") if
len(rec)<700]
  >>> len(records)
  18
>>

  That would create an in memory list of reverse complement records
where the sequence length was under 700 base pairs. However, we can do
exactly the same with a generator expression - but with the advantage
that this does not create a list of all the records in memory at once:
<<>>> records = (rec.reverse_complement(id="rc_"+rec.id, description =
"reverse complement") \
  ...           for rec in SeqIO.parse("ls_orchid.fasta", "fasta") if
len(rec)<700)
>>

  As a complete example:
<<>>> from Bio import SeqIO
  >>> records = (rec.reverse_complement(id="rc_"+rec.id, description =
"reverse complement") \
  ...            for rec in SeqIO.parse("ls_orchid.fasta", "fasta") if
len(rec)<700)
  >>> SeqIO.write(records, "rev_comp.fasta", "fasta")
  18
>>

  There is a related example in Section ??, translating each record in a
FASTA file from nucleotides to amino acids.


5.5.4 Getting your SeqRecord objects as formatted strings
=========================================================
    Suppose that you don't really want to write your records to a file
or handle -- instead you want a string containing the records in a
particular file format. The Bio.SeqIO interface is based on handles, but
Python has a useful built in module which provides a string based
handle.
  For an example of how you might use this, let's load in a bunch of
SeqRecord objects from our orchids GenBank file, and create a string
containing the records in FASTA format:
<<from Bio import SeqIO
  from io import StringIO

  records = SeqIO.parse("ls_orchid.gbk", "genbank")
  out_handle = StringIO()
  SeqIO.write(records, out_handle, "fasta")
  fasta_data = out_handle.getvalue()
  print(fasta_data)
>>

  This isn't entirely straightforward the first time you see it! On the
bright side, for the special case where you would like a string
containing a single record in a particular file format, use the the
SeqRecord class' format() method (see Section ??).
  Note that although we don't encourage it, you can use the format()
method to write to a file, for example something like this: 
<<from Bio import SeqIO

  with open("ls_orchid_long.tab", "w") as out_handle:
      for record in SeqIO.parse("ls_orchid.gbk", "genbank"):
          if len(record) > 100:
              out_handle.write(record.format("tab"))
>>
   While this style of code will work for a simple sequential file
format like FASTA or the simple tab separated format used here, it will
not work for more complex or interlaced file formats. This is why we
still recommend using Bio.SeqIO.write(), as in the following example: 
<<from Bio import SeqIO

  records = (rec for rec in SeqIO.parse("ls_orchid.gbk", "genbank") if
len(rec) > 100)
  SeqIO.write(records, "ls_orchid.tab", "tab")
>>
   Making a single call to SeqIO.write(...) is also much quicker than
multiple calls to the SeqRecord.format(...) method.


5.6 Low level FASTA and FASTQ parsers
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*


  Working with the low-level SimpleFastaParser or FastqGeneralIterator
is often more practical than Bio.SeqIO.parse when dealing with large
high-throughput FASTA or FASTQ sequencing files where speed matters. As
noted in the introduction to this chapter, the file-format neutral
Bio.SeqIO interface has the overhead of creating many objects even for
simple formats like FASTA.
  When parsing FASTA files, internally Bio.SeqIO.parse() calls the
low-level SimpleFastaParser with the file handle. You can use this
directly - it iterates over the file handle returning each record as a
tuple of two strings, the title line (everything after the > character)
and the sequence (as a plain string):
<<>>> from Bio.SeqIO.FastaIO import SimpleFastaParser
  >>> count = 0
  >>> total_len = 0
  >>> with open("ls_orchid.fasta") as in_handle:
  ...     for title, seq in SimpleFastaParser(in_handle):
  ...         count += 1
  ...         total_len += len(seq)
  ...
  >>> print("%i records with total sequence length %i" % (count,
total_len))
  94 records with total sequence length 67518
>>

  As long as you don't care about line wrapping (and you probably don't
for short read high-througput data), then outputing FASTA format from
these strings is also very fast:
<<...
  out_handle.write(">%s\n%s\n" % (title, seq))
  ...
>>

  Likewise, when parsing FASTQ files, internally Bio.SeqIO.parse() calls
the low-level FastqGeneralIterator with the file handle. If you don't
need the quality scores turned into integers, or can work with them as
ASCII strings this is ideal:
<<>>> from Bio.SeqIO.QualityIO import FastqGeneralIterator
  >>> count = 0
  >>> total_len = 0
  >>> with open("example.fastq") as in_handle:
  ...     for title, seq, qual in FastqGeneralIterator(in_handle):
  ...         count += 1
  ...         total_len += len(seq)
  ...
  >>> print("%i records with total sequence length %i" % (count,
total_len))
  3 records with total sequence length 75
>>

  There are more examples of this in the Cookbook (Chapter ??),
including how to output FASTQ efficiently from strings using this code
snippet:
<<...
  out_handle.write("@%s\n%s\n+\n%s\n" % (title, seq, qual))
  ...
>>

-----------------------------------


 (1) http://biopython.org/docs/1.79/api/Bio.SeqIO.html
 (2) https://raw.githubusercontent.com/biopython/biopython/master/Doc/ex
   amples/ls_orchid.fasta
 (3) https://raw.githubusercontent.com/biopython/biopython/master/Doc/ex
   amples/ls_orchid.gbk
 (4) http://biopython.org/docs/1.79/api/Bio.SeqIO.html
 (5) https://raw.githubusercontent.com/biopython/biopython/master/Doc/ex
   amples/ls_orchid.gbk
 (6) https://raw.githubusercontent.com/biopython/biopython/master/Doc/ex
   amples/ls_orchid.gbk
 (7) https://raw.githubusercontent.com/biopython/biopython/master/Doc/ex
   amples/ls_orchid.fasta
 (8) https://www.ncbi.nlm.nih.gov/books/NBK3837/
 (9) https://www.htslib.org/
 (10) https://www.htslib.org/doc/tabix.html
 (11)
   https://raw.githubusercontent.com/biopython/biopython/master/Doc/exam
   ples/ls_orchid.gbk


Chapter 6  Multiple Sequence Alignment objects
**********************************************

  This chapter is about Multiple Sequence Alignments, by which we mean a
collection of multiple sequences which have been aligned together --
usually with the insertion of gap characters, and addition of leading or
trailing gaps -- such that all the sequence strings are the same length.
Such an alignment can be regarded as a matrix of letters, where each row
is held as a SeqRecord object internally.
  We will introduce the MultipleSeqAlignment object which holds this
kind of data, and the Bio.AlignIO module for reading and writing them as
various file formats (following the design of the Bio.SeqIO module from
the previous chapter). Note that both Bio.SeqIO and Bio.AlignIO can read
and write sequence alignment files. The appropriate choice will depend
largely on what you want to do with the data.
  The final part of this chapter is about our command line wrappers for
common multiple sequence alignment tools like ClustalW and MUSCLE.


6.1 Parsing or Reading Sequence Alignments
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=


  We have two functions for reading in sequence alignments,
Bio.AlignIO.read() and Bio.AlignIO.parse() which following the
convention introduced in Bio.SeqIO are for files containing one or
multiple alignments respectively.
  Using Bio.AlignIO.parse() will return an iterator which gives
MultipleSeqAlignment objects. Iterators are typically used in a for
loop. Examples of situations where you will have multiple different
alignments include resampled alignments from the PHYLIP tool seqboot, or
multiple pairwise alignments from the EMBOSS tools water or needle, or
Bill Pearson's FASTA tools.
  However, in many situations you will be dealing with files which
contain only a single alignment. In this case, you should use the
Bio.AlignIO.read() function which returns a single MultipleSeqAlignment
object.
  Both functions expect two mandatory arguments:


   1. The first argument is a handle to read the data from, typically an
   open file (see Section ??), or a filename. 
   2. The second argument is a lower case string specifying the
   alignment format. As in Bio.SeqIO we don't try and guess the file
   format for you! See http://biopython.org/wiki/AlignIO for a full
   listing of supported formats. 

  There is also an optional seq_count argument which is discussed in
Section ?? below for dealing with ambiguous file formats which may
contain more than one alignment.


6.1.1 Single Alignments
=======================
   As an example, consider the following annotation rich protein
alignment in the PFAM or Stockholm file format:
<<# STOCKHOLM 1.0
  #=GS COATB_BPIKE/30-81  AC P03620.1
  #=GS COATB_BPIKE/30-81  DR PDB; 1ifl ; 1-52;
  #=GS Q9T0Q8_BPIKE/1-52  AC Q9T0Q8.1
  #=GS COATB_BPI22/32-83  AC P15416.1
  #=GS COATB_BPM13/24-72  AC P69541.1
  #=GS COATB_BPM13/24-72  DR PDB; 2cpb ; 1-49;
  #=GS COATB_BPM13/24-72  DR PDB; 2cps ; 1-49;
  #=GS COATB_BPZJ2/1-49   AC P03618.1
  #=GS Q9T0Q9_BPFD/1-49   AC Q9T0Q9.1
  #=GS Q9T0Q9_BPFD/1-49   DR PDB; 1nh4 A; 1-49;
  #=GS COATB_BPIF1/22-73  AC P03619.2
  #=GS COATB_BPIF1/22-73  DR PDB; 1ifk ; 1-50;
  COATB_BPIKE/30-81            
AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRLFKKFSSKA
  #=GR COATB_BPIKE/30-81  SS   
-HHHHHHHHHHHHHH--HHHHHHHH--HHHHHHHHHHHHHHHHHHHHH----
  Q9T0Q8_BPIKE/1-52            
AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKLFKKFVSRA
  COATB_BPI22/32-83            
DGTSTATSYATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRLFKKFSSKA
  COATB_BPM13/24-72            
AEGDDP...AKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA
  #=GR COATB_BPM13/24-72  SS   
---S-T...CHCHHHHCCCCTCCCTTCHHHHHHHHHHHHHHHHHHHHCTT--
  COATB_BPZJ2/1-49             
AEGDDP...AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFASKA
  Q9T0Q9_BPFD/1-49             
AEGDDP...AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA
  #=GR Q9T0Q9_BPFD/1-49   SS   
------...-HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH--
  COATB_BPIF1/22-73            
FAADDATSQAKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKLFKKFVSRA
  #=GR COATB_BPIF1/22-73  SS   
XX-HHHH--HHHHHH--HHHHHHH--HHHHHHHHHHHHHHHHHHHHHHH---
  #=GC SS_cons                 
XHHHHHHHHHHHHHHHCHHHHHHHHCHHHHHHHHHHHHHHHHHHHHHHHC--
  #=GC seq_cons                
AEssss...AptAhDSLpspAT-hIu.sWshVsslVsAsluIKLFKKFsSKA
  //
>>

  This is the seed alignment for the Phage_Coat_Gp8 (PF05371) PFAM
entry, downloaded from a now out of date release of PFAM from
https://pfam.xfam.org/. We can load this file as follows (assuming it
has been saved to disk as "PF05371_seed.sth" in the current working
directory):
<<>>> from Bio import AlignIO
  >>> alignment = AlignIO.read("PF05371_seed.sth", "stockholm")
>>

  This code will print out a summary of the alignment:
<<>>> print(alignment)
  Alignment with 7 rows and 52 columns
  AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRL...SKA COATB_BPIKE/30-81
  AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKL...SRA Q9T0Q8_BPIKE/1-52
  DGTSTATSYATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRL...SKA COATB_BPI22/32-83
  AEGDDP---AKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKL...SKA COATB_BPM13/24-72
  AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKL...SKA COATB_BPZJ2/1-49
  AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKL...SKA Q9T0Q9_BPFD/1-49
  FAADDATSQAKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKL...SRA COATB_BPIF1/22-73
>>

  You'll notice in the above output the sequences have been truncated.
We could instead write our own code to format this as we please by
iterating over the rows as SeqRecord objects:
<<>>> from Bio import AlignIO
  >>> alignment = AlignIO.read("PF05371_seed.sth", "stockholm")
  >>> print("Alignment length %i" % alignment.get_alignment_length())
  Alignment length 52
  >>> for record in alignment:
  ...     print("%s - %s" % (record.seq, record.id))
  ...
  AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRLFKKFSSKA -
COATB_BPIKE/30-81
  AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKLFKKFVSRA -
Q9T0Q8_BPIKE/1-52
  DGTSTATSYATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRLFKKFSSKA -
COATB_BPI22/32-83
  AEGDDP---AKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA -
COATB_BPM13/24-72
  AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFASKA -
COATB_BPZJ2/1-49
  AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA -
Q9T0Q9_BPFD/1-49
  FAADDATSQAKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKLFKKFVSRA -
COATB_BPIF1/22-73
>>

  You could also call Python's built-in format function on the alignment
object to show it in a particular file format -- see Section ?? for
details.
  Did you notice in the raw file above that several of the sequences
include database cross-references to the PDB and the associated known
secondary structure? Try this:
<<>>> for record in alignment:
  ...     if record.dbxrefs:
  ...         print("%s %s" % (record.id, record.dbxrefs))
  ...
  COATB_BPIKE/30-81 ['PDB; 1ifl ; 1-52;']
  COATB_BPM13/24-72 ['PDB; 2cpb ; 1-49;', 'PDB; 2cps ; 1-49;']
  Q9T0Q9_BPFD/1-49 ['PDB; 1nh4 A; 1-49;']
  COATB_BPIF1/22-73 ['PDB; 1ifk ; 1-50;']
>>

  To have a look at all the sequence annotation, try this:
<<>>> for record in alignment:
  ...     print(record)
  ...
>>

  PFAM provide a nice web interface at
http://pfam.xfam.org/family/PF05371 which will actually let you download
this alignment in several other formats. This is what the file looks
like in the FASTA file format:
<<>COATB_BPIKE/30-81
  AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRLFKKFSSKA
  >Q9T0Q8_BPIKE/1-52
  AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKLFKKFVSRA
  >COATB_BPI22/32-83
  DGTSTATSYATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRLFKKFSSKA
  >COATB_BPM13/24-72
  AEGDDP---AKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA
  >COATB_BPZJ2/1-49
  AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFASKA
  >Q9T0Q9_BPFD/1-49
  AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA
  >COATB_BPIF1/22-73
  FAADDATSQAKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKLFKKFVSRA
>>

  Note the website should have an option about showing gaps as periods
(dots) or dashes, we've shown dashes above. Assuming you download and
save this as file "PF05371_seed.faa" then you can load it with almost
exactly the same code:
<<>>> from Bio import AlignIO
  >>> alignment = AlignIO.read("PF05371_seed.faa", "fasta")
  >>> print(alignment)
>>

  All that has changed in this code is the filename and the format
string. You'll get the same output as before, the sequences and record
identifiers are the same. However, as you should expect, if you check
each SeqRecord there is no annotation nor database cross-references
because these are not included in the FASTA file format.
  Note that rather than using the Sanger website, you could have used
Bio.AlignIO to convert the original Stockholm format file into a FASTA
file yourself (see below).
  With any supported file format, you can load an alignment in exactly
the same way just by changing the format string. For example, use
"phylip" for PHYLIP files, "nexus" for NEXUS files or "emboss" for the
alignments output by the EMBOSS tools. There is a full listing on the
wiki page (http://biopython.org/wiki/AlignIO) and in the built in
documentation (also online (1)):
<<>>> from Bio import AlignIO
  >>> help(AlignIO)
>>



6.1.2 Multiple Alignments
=========================

  The previous section focused on reading files containing a single
alignment. In general however, files can contain more than one
alignment, and to read these files we must use the Bio.AlignIO.parse()
function.
  Suppose you have a small alignment in PHYLIP format:
<<    5    6
  Alpha     AACAAC
  Beta      AACCCC
  Gamma     ACCAAC
  Delta     CCACCA
  Epsilon   CCAAAC
>>

  If you wanted to bootstrap a phylogenetic tree using the PHYLIP tools,
one of the steps would be to create a set of many resampled alignments
using the tool bootseq. This would give output something like this,
which has been abbreviated for conciseness:
<<    5     6
  Alpha     AAACCA
  Beta      AAACCC
  Gamma     ACCCCA
  Delta     CCCAAC
  Epsilon   CCCAAA
      5     6
  Alpha     AAACAA
  Beta      AAACCC
  Gamma     ACCCAA
  Delta     CCCACC
  Epsilon   CCCAAA
      5     6
  Alpha     AAAAAC
  Beta      AAACCC
  Gamma     AACAAC
  Delta     CCCCCA
  Epsilon   CCCAAC
  ...
      5     6
  Alpha     AAAACC
  Beta      ACCCCC
  Gamma     AAAACC
  Delta     CCCCAA
  Epsilon   CAAACC
>>

  If you wanted to read this in using Bio.AlignIO you could use:
<<>>> from Bio import AlignIO
  >>> alignments = AlignIO.parse("resampled.phy", "phylip")
  >>> for alignment in alignments:
  ...     print(alignment)
  ...     print()
  ...
>>

  This would give the following output, again abbreviated for display:
<<Alignment with 5 rows and 6 columns
  AAACCA Alpha
  AAACCC Beta
  ACCCCA Gamma
  CCCAAC Delta
  CCCAAA Epsilon

  Alignment with 5 rows and 6 columns
  AAACAA Alpha
  AAACCC Beta
  ACCCAA Gamma
  CCCACC Delta
  CCCAAA Epsilon

  Alignment with 5 rows and 6 columns
  AAAAAC Alpha
  AAACCC Beta
  AACAAC Gamma
  CCCCCA Delta
  CCCAAC Epsilon

  ...

  Alignment with 5 rows and 6 columns
  AAAACC Alpha
  ACCCCC Beta
  AAAACC Gamma
  CCCCAA Delta
  CAAACC Epsilon
>>

  As with the function Bio.SeqIO.parse(), using Bio.AlignIO.parse()
returns an iterator. If you want to keep all the alignments in memory at
once, which will allow you to access them in any order, then turn the
iterator into a list:
<<>>> from Bio import AlignIO
  >>> alignments = list(AlignIO.parse("resampled.phy", "phylip"))
  >>> last_align = alignments[-1]
  >>> first_align = alignments[0]
>>



6.1.3 Ambiguous Alignments
==========================
    Many alignment file formats can explicitly store more than one
alignment, and the division between each alignment is clear. However,
when a general sequence file format has been used there is no such block
structure. The most common such situation is when alignments have been
saved in the FASTA file format. For example consider the following:
<<>Alpha
  ACTACGACTAGCTCAG--G
  >Beta
  ACTACCGCTAGCTCAGAAG
  >Gamma
  ACTACGGCTAGCACAGAAG
  >Alpha
  ACTACGACTAGCTCAGG--
  >Beta
  ACTACCGCTAGCTCAGAAG
  >Gamma
  ACTACGGCTAGCACAGAAG
>>

  This could be a single alignment containing six sequences (with
repeated identifiers). Or, judging from the identifiers, this is
probably two different alignments each with three sequences, which
happen to all have the same length.
  What about this next example?
<<>Alpha
  ACTACGACTAGCTCAG--G
  >Beta
  ACTACCGCTAGCTCAGAAG
  >Alpha
  ACTACGACTAGCTCAGG--
  >Gamma
  ACTACGGCTAGCACAGAAG
  >Alpha
  ACTACGACTAGCTCAGG--
  >Delta
  ACTACGGCTAGCACAGAAG
>>

  Again, this could be a single alignment with six sequences. However
this time based on the identifiers we might guess this is three pairwise
alignments which by chance have all got the same lengths.
  This final example is similar:
<<>Alpha
  ACTACGACTAGCTCAG--G
  >XXX
  ACTACCGCTAGCTCAGAAG
  >Alpha
  ACTACGACTAGCTCAGG
  >YYY
  ACTACGGCAAGCACAGG
  >Alpha
  --ACTACGAC--TAGCTCAGG
  >ZZZ
  GGACTACGACAATAGCTCAGG
>>

  In this third example, because of the differing lengths, this cannot
be treated as a single alignment containing all six records. However, it
could be three pairwise alignments.
  Clearly trying to store more than one alignment in a FASTA file is not
ideal. However, if you are forced to deal with these as input files
Bio.AlignIO can cope with the most common situation where all the
alignments have the same number of records. One example of this is a
collection of pairwise alignments, which can be produced by the EMBOSS
tools needle and water -- although in this situation, Bio.AlignIO should
be able to understand their native output using "emboss" as the format
string.
  To interpret these FASTA examples as several separate alignments, we
can use Bio.AlignIO.parse() with the optional seq_count argument which
specifies how many sequences are expected in each alignment (in these
examples, 3, 2 and 2 respectively). For example, using the third example
as the input data:
<<>>> for alignment in AlignIO.parse(handle, "fasta", seq_count=2):
  ...     print("Alignment length %i" %
alignment.get_alignment_length())
  ...     for record in alignment:
  ...         print("%s - %s" % (record.seq, record.id))
  ...     print()
  ...
>>

  giving:
<<Alignment length 19
  ACTACGACTAGCTCAG--G - Alpha
  ACTACCGCTAGCTCAGAAG - XXX

  Alignment length 17
  ACTACGACTAGCTCAGG - Alpha
  ACTACGGCAAGCACAGG - YYY

  Alignment length 21
  --ACTACGAC--TAGCTCAGG - Alpha
  GGACTACGACAATAGCTCAGG - ZZZ
>>

  Using Bio.AlignIO.read() or Bio.AlignIO.parse() without the seq_count
argument would give a single alignment containing all six records for
the first two examples. For the third example, an exception would be
raised because the lengths differ preventing them being turned into a
single alignment.
  If the file format itself has a block structure allowing Bio.AlignIO
to determine the number of sequences in each alignment directly, then
the seq_count argument is not needed. If it is supplied, and doesn't
agree with the file contents, an error is raised.
  Note that this optional seq_count argument assumes each alignment in
the file has the same number of sequences. Hypothetically you may come
across stranger situations, for example a FASTA file containing several
alignments each with a different number of sequences -- although I would
love to hear of a real world example of this. Assuming you cannot get
the data in a nicer file format, there is no straight forward way to
deal with this using Bio.AlignIO. In this case, you could consider
reading in the sequences themselves using Bio.SeqIO and batching them
together to create the alignments as appropriate.


6.2 Writing Alignments
*=*=*=*=*=*=*=*=*=*=*=


  We've talked about using Bio.AlignIO.read() and Bio.AlignIO.parse()
for alignment input (reading files), and now we'll look at
Bio.AlignIO.write() which is for alignment output (writing files). This
is a function taking three arguments: some MultipleSeqAlignment objects
(or for backwards compatibility the obsolete Alignment objects), a
handle or filename to write to, and a sequence format.
  Here is an example, where we start by creating a few
MultipleSeqAlignment objects the hard way (by hand, rather than by
loading them from a file). Note we create some SeqRecord objects to
construct the alignment from.
<<>>> from Bio.Seq import Seq
  >>> from Bio.SeqRecord import SeqRecord
  >>> from Bio.Align import MultipleSeqAlignment
  >>> align1 = MultipleSeqAlignment(
  ...     [
  ...         SeqRecord(Seq("ACTGCTAGCTAG"), id="Alpha"),
  ...         SeqRecord(Seq("ACT-CTAGCTAG"), id="Beta"),
  ...         SeqRecord(Seq("ACTGCTAGDTAG"), id="Gamma"),
  ...     ]
  ... )
  >>> align2 = MultipleSeqAlignment(
  ...     [
  ...         SeqRecord(Seq("GTCAGC-AG"), id="Delta"),
  ...         SeqRecord(Seq("GACAGCTAG"), id="Epsilon"),
  ...         SeqRecord(Seq("GTCAGCTAG"), id="Zeta"),
  ...     ]
  ... )
  >>> align3 = MultipleSeqAlignment(
  ...     [
  ...         SeqRecord(Seq("ACTAGTACAGCTG"), id="Eta"),
  ...         SeqRecord(Seq("ACTAGTACAGCT-"), id="Theta"),
  ...         SeqRecord(Seq("-CTACTACAGGTG"), id="Iota"),
  ...     ]
  ... )
  >>> my_alignments = [align1, align2, align3]
>>

  Now we have a list of Alignment objects, we'll write them to a PHYLIP
format file:
<<>>> from Bio import AlignIO
  >>> AlignIO.write(my_alignments, "my_example.phy", "phylip")
>>

  And if you open this file in your favourite text editor it should look
like this:
<< 3 12
  Alpha      ACTGCTAGCT AG
  Beta       ACT-CTAGCT AG
  Gamma      ACTGCTAGDT AG
   3 9
  Delta      GTCAGC-AG
  Epislon    GACAGCTAG
  Zeta       GTCAGCTAG
   3 13
  Eta        ACTAGTACAG CTG
  Theta      ACTAGTACAG CT-
  Iota       -CTACTACAG GTG
>>

  Its more common to want to load an existing alignment, and save that,
perhaps after some simple manipulation like removing certain rows or
columns.
  Suppose you wanted to know how many alignments the Bio.AlignIO.write()
function wrote to the handle? If your alignments were in a list like the
example above, you could just use len(my_alignments), however you can't
do that when your records come from a generator/iterator. Therefore the
Bio.AlignIO.write() function returns the number of alignments written to
the file.
  Note - If you tell the Bio.AlignIO.write() function to write to a file
that already exists, the old file will be overwritten without any
warning.


6.2.1 Converting between sequence alignment file formats
========================================================

  Converting between sequence alignment file formats with Bio.AlignIO
works in the same way as converting between sequence file formats with
Bio.SeqIO (Section ??). We load generally the alignment(s) using
Bio.AlignIO.parse() and then save them using the Bio.AlignIO.write() --
or just use the Bio.AlignIO.convert() helper function.
  For this example, we'll load the PFAM/Stockholm format file used
earlier and save it as a Clustal W format file:
<<>>> from Bio import AlignIO
  >>> count = AlignIO.convert("PF05371_seed.sth", "stockholm",
"PF05371_seed.aln", "clustal")
  >>> print("Converted %i alignments" % count)
  Converted 1 alignments
>>

  Or, using Bio.AlignIO.parse() and Bio.AlignIO.write():
<<>>> from Bio import AlignIO
  >>> alignments = AlignIO.parse("PF05371_seed.sth", "stockholm")
  >>> count = AlignIO.write(alignments, "PF05371_seed.aln", "clustal")
  >>> print("Converted %i alignments" % count)
  Converted 1 alignments
>>

  The Bio.AlignIO.write() function expects to be given multiple
alignment objects. In the example above we gave it the alignment
iterator returned by Bio.AlignIO.parse().
  In this case, we know there is only one alignment in the file so we
could have used Bio.AlignIO.read() instead, but notice we have to pass
this alignment to Bio.AlignIO.write() as a single element list:
<<>>> from Bio import AlignIO
  >>> alignment = AlignIO.read("PF05371_seed.sth", "stockholm")
  >>> AlignIO.write([alignment], "PF05371_seed.aln", "clustal")
>>

  Either way, you should end up with the same new Clustal W format file
"PF05371_seed.aln" with the following content:
<<CLUSTAL X (1.81) multiple sequence alignment


  COATB_BPIKE/30-81                  
AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRLFKKFSS
  Q9T0Q8_BPIKE/1-52                  
AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKLFKKFVS
  COATB_BPI22/32-83                  
DGTSTATSYATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRLFKKFSS
  COATB_BPM13/24-72                  
AEGDDP---AKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTS
  COATB_BPZJ2/1-49                   
AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFAS
  Q9T0Q9_BPFD/1-49                   
AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTS
  COATB_BPIF1/22-73                  
FAADDATSQAKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKLFKKFVS

  COATB_BPIKE/30-81                   KA
  Q9T0Q8_BPIKE/1-52                   RA
  COATB_BPI22/32-83                   KA
  COATB_BPM13/24-72                   KA
  COATB_BPZJ2/1-49                    KA
  Q9T0Q9_BPFD/1-49                    KA
  COATB_BPIF1/22-73                   RA
>>

  Alternatively, you could make a PHYLIP format file which we'll name
"PF05371_seed.phy":
<<>>> from Bio import AlignIO
  >>> AlignIO.convert("PF05371_seed.sth", "stockholm",
"PF05371_seed.phy", "phylip")
>>

  This time the output looks like this:
<< 7 52
  COATB_BPIK AEPNAATNYA TEAMDSLKTQ AIDLISQTWP VVTTVVVAGL VIRLFKKFSS
  Q9T0Q8_BPI AEPNAATNYA TEAMDSLKTQ AIDLISQTWP VVTTVVVAGL VIKLFKKFVS
  COATB_BPI2 DGTSTATSYA TEAMNSLKTQ ATDLIDQTWP VVTSVAVAGL AIRLFKKFSS
  COATB_BPM1 AEGDDP---A KAAFNSLQAS ATEYIGYAWA MVVVIVGATI GIKLFKKFTS
  COATB_BPZJ AEGDDP---A KAAFDSLQAS ATEYIGYAWA MVVVIVGATI GIKLFKKFAS
  Q9T0Q9_BPF AEGDDP---A KAAFDSLQAS ATEYIGYAWA MVVVIVGATI GIKLFKKFTS
  COATB_BPIF FAADDATSQA KAAFDSLTAQ ATEMSGYAWA LVVLVVGATV GIKLFKKFVS

             KA
             RA
             KA
             KA
             KA
             KA
             RA
>>

  One of the big handicaps of the original PHYLIP alignment file format
is that the sequence identifiers are strictly truncated at ten
characters. In this example, as you can see the resulting names are
still unique - but they are not very readable. As a result, a more
relaxed variant of the original PHYLIP format is now quite widely used:
<<>>> from Bio import AlignIO
  >>> AlignIO.convert("PF05371_seed.sth", "stockholm",
"PF05371_seed.phy", "phylip-relaxed")
>>

  This time the output looks like this, using a longer indentation to
allow all the identifiers to be given in full:
<< 7 52
  COATB_BPIKE/30-81  AEPNAATNYA TEAMDSLKTQ AIDLISQTWP VVTTVVVAGL
VIRLFKKFSS
  Q9T0Q8_BPIKE/1-52  AEPNAATNYA TEAMDSLKTQ AIDLISQTWP VVTTVVVAGL
VIKLFKKFVS
  COATB_BPI22/32-83  DGTSTATSYA TEAMNSLKTQ ATDLIDQTWP VVTSVAVAGL
AIRLFKKFSS
  COATB_BPM13/24-72  AEGDDP---A KAAFNSLQAS ATEYIGYAWA MVVVIVGATI
GIKLFKKFTS
  COATB_BPZJ2/1-49   AEGDDP---A KAAFDSLQAS ATEYIGYAWA MVVVIVGATI
GIKLFKKFAS
  Q9T0Q9_BPFD/1-49   AEGDDP---A KAAFDSLQAS ATEYIGYAWA MVVVIVGATI
GIKLFKKFTS
  COATB_BPIF1/22-73  FAADDATSQA KAAFDSLTAQ ATEMSGYAWA LVVLVVGATV
GIKLFKKFVS

                     KA
                     RA
                     KA
                     KA
                     KA
                     KA
                     RA
>>

  If you have to work with the original strict PHYLIP format, then you
may need to compress the identifiers somehow -- or assign your own names
or numbering system. This following bit of code manipulates the record
identifiers before saving the output:
<<>>> from Bio import AlignIO
  >>> alignment = AlignIO.read("PF05371_seed.sth", "stockholm")
  >>> name_mapping = {}
  >>> for i, record in enumerate(alignment):
  ...     name_mapping[i] = record.id
  ...     record.id = "seq%i" % i
  ...
  >>> print(name_mapping)
  {0: 'COATB_BPIKE/30-81', 1: 'Q9T0Q8_BPIKE/1-52', 2:
'COATB_BPI22/32-83', 3: 'COATB_BPM13/24-72', 4: 'COATB_BPZJ2/1-49', 5:
'Q9T0Q9_BPFD/1-49', 6: 'COATB_BPIF1/22-73'}
  >>> AlignIO.write([alignment], "PF05371_seed.phy", "phylip")
>>

  This code used a Python dictionary to record a simple mapping from the
new sequence system to the original identifier: 
<<{
      0: "COATB_BPIKE/30-81",
      1: "Q9T0Q8_BPIKE/1-52",
      2: "COATB_BPI22/32-83",
      # ...
  }
>>

  Here is the new (strict) PHYLIP format output: 
<< 7 52
  seq0       AEPNAATNYA TEAMDSLKTQ AIDLISQTWP VVTTVVVAGL VIRLFKKFSS
  seq1       AEPNAATNYA TEAMDSLKTQ AIDLISQTWP VVTTVVVAGL VIKLFKKFVS
  seq2       DGTSTATSYA TEAMNSLKTQ ATDLIDQTWP VVTSVAVAGL AIRLFKKFSS
  seq3       AEGDDP---A KAAFNSLQAS ATEYIGYAWA MVVVIVGATI GIKLFKKFTS
  seq4       AEGDDP---A KAAFDSLQAS ATEYIGYAWA MVVVIVGATI GIKLFKKFAS
  seq5       AEGDDP---A KAAFDSLQAS ATEYIGYAWA MVVVIVGATI GIKLFKKFTS
  seq6       FAADDATSQA KAAFDSLTAQ ATEMSGYAWA LVVLVVGATV GIKLFKKFVS

             KA
             RA
             KA
             KA
             KA
             KA
             RA
>>

  In general, because of the identifier limitation, working with strict
PHYLIP file formats shouldn't be your first choice. Using the
PFAM/Stockholm format on the other hand allows you to record a lot of
additional annotation too.


6.2.2 Getting your alignment objects as formatted strings
=========================================================
    The Bio.AlignIO interface is based on handles, which means if you
want to get your alignment(s) into a string in a particular file format
you need to do a little bit more work (see below). However, you will
probably prefer to call Python's built-in format function on the
alignment object. This takes an output format specification as a single
argument, a lower case string which is supported by Bio.AlignIO as an
output format. For example:
<<>>> from Bio import AlignIO
  >>> alignment = AlignIO.read("PF05371_seed.sth", "stockholm")
  >>> print(format(alignment, "clustal"))
  CLUSTAL X (1.81) multiple sequence alignment


  COATB_BPIKE/30-81                  
AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRLFKKFSS
  Q9T0Q8_BPIKE/1-52                  
AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKLFKKFVS
  COATB_BPI22/32-83                  
DGTSTATSYATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRLFKKFSS
  ...
>>

  Without an output format specification, format returns the same output
as str.
  As described in Section ??, the SeqRecord object has a similar method
using output formats supported by Bio.SeqIO.
  Internally format is calling Bio.AlignIO.write() with a StringIO
handle. You can do this in your own code if for example you are using an
older version of Biopython:
<<>>> from io import StringIO
  >>> from Bio import AlignIO
  >>> alignments = AlignIO.parse("PF05371_seed.sth", "stockholm")
  >>> out_handle = StringIO()
  >>> AlignIO.write(alignments, out_handle, "clustal")
  1
  >>> clustal_data = out_handle.getvalue()
  >>> print(clustal_data)
  CLUSTAL X (1.81) multiple sequence alignment


  COATB_BPIKE/30-81                  
AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRLFKKFSS
  Q9T0Q8_BPIKE/1-52                  
AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKLFKKFVS
  COATB_BPI22/32-83                  
DGTSTATSYATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRLFKKFSS
  COATB_BPM13/24-72                  
AEGDDP---AKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTS
  ...
>>



6.3 Manipulating Alignments
*=*=*=*=*=*=*=*=*=*=*=*=*=*


  Now that we've covered loading and saving alignments, we'll look at
what else you can do with them.


6.3.1 Slicing alignments
========================
   First of all, in some senses the alignment objects act like a Python
list of SeqRecord objects (the rows). With this model in mind hopefully
the actions of len() (the number of rows) and iteration (each row as a
SeqRecord) make sense:
<<>>> from Bio import AlignIO
  >>> alignment = AlignIO.read("PF05371_seed.sth", "stockholm")
  >>> print("Number of rows: %i" % len(alignment))
  Number of rows: 7
  >>> for record in alignment:
  ...     print("%s - %s" % (record.seq, record.id))
  ...
  AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRLFKKFSSKA -
COATB_BPIKE/30-81
  AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKLFKKFVSRA -
Q9T0Q8_BPIKE/1-52
  DGTSTATSYATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRLFKKFSSKA -
COATB_BPI22/32-83
  AEGDDP---AKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA -
COATB_BPM13/24-72
  AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFASKA -
COATB_BPZJ2/1-49
  AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA -
Q9T0Q9_BPFD/1-49
  FAADDATSQAKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKLFKKFVSRA -
COATB_BPIF1/22-73
>>

  You can also use the list-like append and extend methods to add more
rows to the alignment (as SeqRecord objects). Keeping the list metaphor
in mind, simple slicing of the alignment should also make sense - it
selects some of the rows giving back another alignment object:
<<>>> print(alignment)
  Alignment with 7 rows and 52 columns
  AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRL...SKA COATB_BPIKE/30-81
  AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKL...SRA Q9T0Q8_BPIKE/1-52
  DGTSTATSYATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRL...SKA COATB_BPI22/32-83
  AEGDDP---AKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKL...SKA COATB_BPM13/24-72
  AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKL...SKA COATB_BPZJ2/1-49
  AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKL...SKA Q9T0Q9_BPFD/1-49
  FAADDATSQAKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKL...SRA COATB_BPIF1/22-73
  >>> print(alignment[3:7])
  Alignment with 4 rows and 52 columns
  AEGDDP---AKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKL...SKA COATB_BPM13/24-72
  AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKL...SKA COATB_BPZJ2/1-49
  AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKL...SKA Q9T0Q9_BPFD/1-49
  FAADDATSQAKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKL...SRA COATB_BPIF1/22-73
>>

  What if you wanted to select by column? Those of you who have used the
NumPy matrix or array objects won't be surprised at this - you use a
double index.
<<>>> print(alignment[2, 6])
  T
>>

  Using two integer indices pulls out a single letter, short hand for
this:
<<>>> print(alignment[2].seq[6])
  T
>>

  You can pull out a single column as a string like this:
<<>>> print(alignment[:, 6])
  TTT---T
>>

  You can also select a range of columns. For example, to pick out those
same three rows we extracted earlier, but take just their first six
columns:
<<>>> print(alignment[3:6, :6])
  Alignment with 3 rows and 6 columns
  AEGDDP COATB_BPM13/24-72
  AEGDDP COATB_BPZJ2/1-49
  AEGDDP Q9T0Q9_BPFD/1-49
>>

  Leaving the first index as : means take all the rows:
<<>>> print(alignment[:, :6])
  Alignment with 7 rows and 6 columns
  AEPNAA COATB_BPIKE/30-81
  AEPNAA Q9T0Q8_BPIKE/1-52
  DGTSTA COATB_BPI22/32-83
  AEGDDP COATB_BPM13/24-72
  AEGDDP COATB_BPZJ2/1-49
  AEGDDP Q9T0Q9_BPFD/1-49
  FAADDA COATB_BPIF1/22-73
>>

  This brings us to a neat way to remove a section. Notice columns 7, 8
and 9 which are gaps in three of the seven sequences:
<<>>> print(alignment[:, 6:9])
  Alignment with 7 rows and 3 columns
  TNY COATB_BPIKE/30-81
  TNY Q9T0Q8_BPIKE/1-52
  TSY COATB_BPI22/32-83
  --- COATB_BPM13/24-72
  --- COATB_BPZJ2/1-49
  --- Q9T0Q9_BPFD/1-49
  TSQ COATB_BPIF1/22-73
>>

  Again, you can slice to get everything after the ninth column:
<<>>> print(alignment[:, 9:])
  Alignment with 7 rows and 43 columns
  ATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRLFKKFSSKA COATB_BPIKE/30-81
  ATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKLFKKFVSRA Q9T0Q8_BPIKE/1-52
  ATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRLFKKFSSKA COATB_BPI22/32-83
  AKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA COATB_BPM13/24-72
  AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFASKA COATB_BPZJ2/1-49
  AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA Q9T0Q9_BPFD/1-49
  AKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKLFKKFVSRA COATB_BPIF1/22-73
>>

  Now, the interesting thing is that addition of alignment objects works
by column. This lets you do this as a way to remove a block of columns:
<<>>> edited = alignment[:, :6] + alignment[:, 9:]
  >>> print(edited)
  Alignment with 7 rows and 49 columns
  AEPNAAATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRLFKKFSSKA COATB_BPIKE/30-81
  AEPNAAATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKLFKKFVSRA Q9T0Q8_BPIKE/1-52
  DGTSTAATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRLFKKFSSKA COATB_BPI22/32-83
  AEGDDPAKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA COATB_BPM13/24-72
  AEGDDPAKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFASKA COATB_BPZJ2/1-49
  AEGDDPAKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA Q9T0Q9_BPFD/1-49
  FAADDAAKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKLFKKFVSRA COATB_BPIF1/22-73
>>

  Another common use of alignment addition would be to combine
alignments for several different genes into a meta-alignment. Watch out
though - the identifiers need to match up (see Section ?? for how adding
SeqRecord objects works). You may find it helpful to first sort the
alignment rows alphabetically by id:
<<>>> edited.sort()
  >>> print(edited)
  Alignment with 7 rows and 49 columns
  DGTSTAATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRLFKKFSSKA COATB_BPI22/32-83
  FAADDAAKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKLFKKFVSRA COATB_BPIF1/22-73
  AEPNAAATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRLFKKFSSKA COATB_BPIKE/30-81
  AEGDDPAKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA COATB_BPM13/24-72
  AEGDDPAKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFASKA COATB_BPZJ2/1-49
  AEPNAAATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKLFKKFVSRA Q9T0Q8_BPIKE/1-52
  AEGDDPAKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA Q9T0Q9_BPFD/1-49
>>

  Note that you can only add two alignments together if they have the
same number of rows.


6.3.2 Alignments as arrays
==========================
   Depending on what you are doing, it can be more useful to turn the
alignment object into an array of letters -- and you can do this with
NumPy:
<<>>> import numpy as np
  >>> from Bio import AlignIO
  >>> alignment = AlignIO.read("PF05371_seed.sth", "stockholm")
  >>> align_array = np.array([list(rec) for rec in alignment],
np.character)
  >>> print("Array shape %i by %i" % align_array.shape)
  Array shape 7 by 52
>>

  If you will be working heavily with the columns, you can tell NumPy to
store the array by column (as in Fortran) rather than its default of by
row (as in C):
<<>>> align_array = np.array([list(rec) for rec in alignment],
np.character, order="F")
>>

  Note that this leaves the original Biopython alignment object and the
NumPy array in memory as separate objects - editing one will not update
the other!


6.4 Getting information on the alignment
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=




6.4.1 Substitutions
===================

  The substitutions property of an alignment reports how often letters
in the alignment are substituted for each other. This is calculated by
taking all pairs of rows in the alignment, counting the number of times
two letters are aligned to each other, and summing this over all pairs.
For example,
<<>>> from Bio.Seq import Seq
  >>> from Bio.SeqRecord import SeqRecord
  >>> from Bio.Align import MultipleSeqAlignment
  >>> alignment = MultipleSeqAlignment(
  ...     [
  ...         SeqRecord(Seq("ACTCCTA"), id='seq1'),
  ...         SeqRecord(Seq("AAT-CTA"), id='seq2'),
  ...         SeqRecord(Seq("CCTACT-"), id='seq3'),
  ...         SeqRecord(Seq("TCTCCTC"), id='seq4'),
  ...     ]
  ... )
  ...
  >>> print(alignment)
  Alignment with 4 rows and 7 columns
  ACTCCTA seq1
  AAT-CTA seq2
  CCTACT- seq3
  TCTCCTC seq4
  >>> substitutions = alignment.substitutions
  >>> print(substitutions)
      A    C    T
  A 2.0  4.5  1.0
  C 4.5 10.0  0.5
  T 1.0  0.5 12.0
  <BLANKLINE>
>>
   As the ordering of pairs is arbitrary, counts are divided equally
above and below the diagonal. For example, the 9 alignments of A to C
are stored as 4.5 at position ['A', 'C'] and 4.5 at position ['C', 'A'].
This arrangement helps to make the math easier when calculating a
substitution matrix from these counts, as described in Section ??.
  Note that alignment.substitutions contains entries for the letters
appearing in the alignment only. You can use the select method to add
entries for missing letters, for example 
<<>>> m = substitutions.select("ATCG")
  >>> print(m)
      A    T    C   G
  A 2.0  1.0  4.5 0.0
  T 1.0 12.0  0.5 0.0
  C 4.5  0.5 10.0 0.0
  G 0.0  0.0  0.0 0.0
  <BLANKLINE>
>>
   This also allows you to change the order of letters in the alphabet.


6.5 Alignment Tools
*=*=*=*=*=*=*=*=*=*


  There are lots of algorithms out there for aligning sequences, both
pairwise alignments and multiple sequence alignments. These calculations
are relatively slow, and you generally wouldn't want to write such an
algorithm in Python. For pairwise alignments Biopython contains the
Bio.pairwise2 module , which is supplemented by functions written in C
for speed enhancements and the new PairwiseAligner (see Section ??). In
addition, you can use Biopython to invoke a command line tool on your
behalf. Normally you would: 

   1. Prepare an input file of your unaligned sequences, typically this
   will be a FASTA file which you might create using Bio.SeqIO (see
   Chapter ??). 
   2. Call the command line tool to process this input file, typically
   via one of Biopython's command line wrappers (which we'll discuss
   here). 
   3. Read the output from the tool, i.e. your aligned sequences,
   typically using Bio.AlignIO (see earlier in this chapter). 

  All the command line wrappers we're going to talk about in this
chapter follow the same style. You create a command line object
specifying the options (e.g. the input filename and the output
filename), then invoke this command line via a Python operating system
call (e.g. using the subprocess module).
  WARNING: We have decided to drop these command line wrappers in a
future Biopython release. We will be updating this documentation to
instead build the command line directly, and invoke it with the
subprocess module.
  Most of these wrappers are defined in the Bio.Align.Applications
module:
<<>>> import Bio.Align.Applications
  >>> dir(Bio.Align.Applications) # doctest:+ELLIPSIS
  ['ClustalOmegaCommandline', 'ClustalwCommandline',
'DialignCommandline', 'MSAProbsCommandline', 'MafftCommandline',
'MuscleCommandline', 'PrankCommandline', 'ProbconsCommandline',
'TCoffeeCommandline', ...]
>>

  (Ignore the entries starting with an underscore -- these have special
meaning in Python.) The module Bio.Emboss.Applications has wrappers for
some of the EMBOSS suite (2), including needle and water, which are
described below in Section ??, and wrappers for the EMBOSS packaged
versions of the PHYLIP tools (which EMBOSS refer to as one of their
EMBASSY packages - third party tools with an EMBOSS style interface). We
won't explore all these alignment tools here in the section, just a
sample, but the same principles apply.


6.5.1 ClustalW
==============
    ClustalW is a popular command line tool for multiple sequence
alignment (there is also a graphical interface called ClustalX).
Biopython's Bio.Align.Applications module has a wrapper for this
alignment tool (and several others).
  Before trying to use ClustalW from within Python, you should first try
running the ClustalW tool yourself by hand at the command line, to
familiarise yourself the other options. You'll find the Biopython
wrapper is very faithful to the actual command line API:
<<>>> from Bio.Align.Applications import ClustalwCommandline
  >>> help(ClustalwCommandline)
>>

  For the most basic usage, all you need is to have a FASTA input file,
such as opuntia.fasta (3) (available online or in the Doc/examples
subdirectory of the Biopython source code). This is a small FASTA file
containing seven prickly-pear DNA sequences (from the cactus family
Opuntia).
  By default ClustalW will generate an alignment and guide tree file
with names based on the input FASTA file, in this case opuntia.aln and
opuntia.dnd, but you can override this or make it explicit:
<<>>> from Bio.Align.Applications import ClustalwCommandline
  >>> cline = ClustalwCommandline("clustalw2", infile="opuntia.fasta")
  >>> print(cline)
  clustalw2 -infile=opuntia.fasta
>>

  Notice here we have given the executable name as clustalw2, indicating
we have version two installed, which has a different filename to version
one (clustalw, the default). Fortunately both versions support the same
set of arguments at the command line (and indeed, should be functionally
identical).
  You may find that even though you have ClustalW installed, the above
command doesn't work -- you may get a message about "command not found"
(especially on Windows). This indicated that the ClustalW executable is
not on your PATH (an environment variable, a list of directories to be
searched). You can either update your PATH setting to include the
location of your copy of ClustalW tools (how you do this will depend on
your OS), or simply type in the full path of the tool. For example:
<<>>> import os
  >>> from Bio.Align.Applications import ClustalwCommandline
  >>> clustalw_exe = r"C:\Program Files\new clustal\clustalw2.exe"
  >>> clustalw_cline = ClustalwCommandline(clustalw_exe,
infile="opuntia.fasta")
>>

<<>>> assert os.path.isfile(clustalw_exe), "Clustal W executable
missing"
  >>> stdout, stderr = clustalw_cline()
>>

  Remember, in Python strings \n and \t are by default interpreted as a
new line and a tab -- which is why we're put a letter "r" at the start
for a raw string that isn't translated in this way. This is generally
good practice when specifying a Windows style file name.
  Internally this uses the subprocess module which is now the
recommended way to run another program in Python. This replaces older
options like the os.system() and the os.popen* functions.
  Now, at this point it helps to know about how command line tools
"work". When you run a tool at the command line, it will often print
text output directly to screen. This text can be captured or redirected,
via two "pipes", called standard output (the normal results) and
standard error (for error messages and debug messages). There is also
standard input, which is any text fed into the tool. These names get
shortened to stdin, stdout and stderr. When the tool finishes, it has a
return code (an integer), which by convention is zero for success.
  When you run the command line tool like this via the Biopython
wrapper, it will wait for it to finish, and check the return code. If
this is non zero (indicating an error), an exception is raised. The
wrapper then returns two strings, stdout and stderr.
  In the case of ClustalW, when run at the command line all the
important output is written directly to the output files. Everything
normally printed to screen while you wait (via stdout or stderr) is
boring and can be ignored (assuming it worked).
  What we care about are the two output files, the alignment and the
guide tree. We didn't tell ClustalW what filenames to use, but it
defaults to picking names based on the input file. In this case the
output should be in the file opuntia.aln. You should be able to work out
how to read in the alignment using Bio.AlignIO by now:
<<>>> from Bio import AlignIO
  >>> align = AlignIO.read("opuntia.aln", "clustal")
  >>> print(align)
  Alignment with 7 rows and 906 columns
  TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA
gi|6273285|gb|AF191659.1|AF191
  TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA
gi|6273284|gb|AF191658.1|AF191
  TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA
gi|6273287|gb|AF191661.1|AF191
  TATACATAAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA
gi|6273286|gb|AF191660.1|AF191
  TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA
gi|6273290|gb|AF191664.1|AF191
  TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA
gi|6273289|gb|AF191663.1|AF191
  TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA
gi|6273291|gb|AF191665.1|AF191
>>

  In case you are interested (and this is an aside from the main thrust
of this chapter), the opuntia.dnd file ClustalW creates is just a
standard Newick tree file, and Bio.Phylo can parse these:
<<>>> from Bio import Phylo
  >>> tree = Phylo.read("opuntia.dnd", "newick")
  >>> Phylo.draw_ascii(tree)
                               _______________
gi|6273291|gb|AF191665.1|AF191665
    __________________________|
   |                          |   ______
gi|6273290|gb|AF191664.1|AF191664
   |                          |__|
   |                             |_____
gi|6273289|gb|AF191663.1|AF191663
   |
  _|_________________ gi|6273287|gb|AF191661.1|AF191661
   |
   |__________ gi|6273286|gb|AF191660.1|AF191660
   |
   |    __ gi|6273285|gb|AF191659.1|AF191659
   |___|
       | gi|6273284|gb|AF191658.1|AF191658
  <BLANKLINE>
>>

  Chapter ?? covers Biopython's support for phylogenetic trees in more
depth.


6.5.2 MUSCLE
============
   MUSCLE is a more recent multiple sequence alignment tool than
ClustalW, and Biopython also has a wrapper for it under the
Bio.Align.Applications module. As before, we recommend you try using
MUSCLE from the command line before trying it from within Python, as the
Biopython wrapper is very faithful to the actual command line API:
<<>>> from Bio.Align.Applications import MuscleCommandline
  >>> help(MuscleCommandline)
>>

  For the most basic usage, all you need is to have a FASTA input file,
such as opuntia.fasta (4) (available online or in the Doc/examples
subdirectory of the Biopython source code). You can then tell MUSCLE to
read in this FASTA file, and write the alignment to an output file:
<<>>> from Bio.Align.Applications import MuscleCommandline
  >>> cline = MuscleCommandline(input="opuntia.fasta",
out="opuntia.txt")
  >>> print(cline)
  muscle -in opuntia.fasta -out opuntia.txt
>>

  Note that MUSCLE uses "-in" and "-out" but in Biopython we have to use
"input" and "out" as the keyword arguments or property names. This is
because "in" is a reserved word in Python.
  By default MUSCLE will output the alignment as a FASTA file (using
gapped sequences). The Bio.AlignIO module should be able to read this
alignment using format="fasta". You can also ask for ClustalW-like
output:
<<>>> from Bio.Align.Applications import MuscleCommandline
  >>> cline = MuscleCommandline(input="opuntia.fasta",
out="opuntia.aln", clw=True)
  >>> print(cline)
  muscle -in opuntia.fasta -out opuntia.aln -clw
>>

  Or, strict ClustalW output where the original ClustalW header line is
used for maximum compatibility:
<<>>> from Bio.Align.Applications import MuscleCommandline
  >>> cline = MuscleCommandline(input="opuntia.fasta",
out="opuntia.aln", clwstrict=True)
  >>> print(cline)
  muscle -in opuntia.fasta -out opuntia.aln -clwstrict
>>

  The Bio.AlignIO module should be able to read these alignments using
format="clustal".
  MUSCLE can also output in GCG MSF format (using the msf argument), but
Biopython can't currently parse that, or using HTML which would give a
human readable web page (not suitable for parsing).
  You can also set the other optional parameters, for example the
maximum number of iterations. See the built in help for details.
  You would then run MUSCLE command line string as described above for
ClustalW, and parse the output using Bio.AlignIO to get an alignment
object.


6.5.3 MUSCLE using stdout
=========================

  Using a MUSCLE command line as in the examples above will write the
alignment to a file. This means there will be no important information
written to the standard out (stdout) or standard error (stderr) handles.
However, by default MUSCLE will write the alignment to standard output
(stdout). We can take advantage of this to avoid having a temporary
output file! For example:
<<>>> from Bio.Align.Applications import MuscleCommandline
  >>> muscle_cline = MuscleCommandline(input="opuntia.fasta")
  >>> print(muscle_cline)
  muscle -in opuntia.fasta
>>

  If we run this via the wrapper, we get back the output as a string. In
order to parse this we can use StringIO to turn it into a handle.
Remember that MUSCLE defaults to using FASTA as the output format:
<<>>> from Bio.Align.Applications import MuscleCommandline
  >>> muscle_cline = MuscleCommandline(input="opuntia.fasta")
  >>> stdout, stderr = muscle_cline()
  >>> from io import StringIO
  >>> from Bio import AlignIO
  >>> align = AlignIO.read(StringIO(stdout), "fasta")
  >>> print(align)
  Alignment with 7 rows and 906 columns
  TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA
gi|6273289|gb|AF191663.1|AF191663
  TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA
gi|6273291|gb|AF191665.1|AF191665
  TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA
gi|6273290|gb|AF191664.1|AF191664
  TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA
gi|6273287|gb|AF191661.1|AF191661
  TATACATAAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA
gi|6273286|gb|AF191660.1|AF191660
  TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA
gi|6273285|gb|AF191659.1|AF191659
  TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA
gi|6273284|gb|AF191658.1|AF191658
>>

  The above approach is fairly simple, but if you are dealing with very
large output text the fact that all of stdout and stderr is loaded into
memory as a string can be a potential drawback. Using the subprocess
module we can work directly with handles instead:
<<>>> import subprocess
  >>> from Bio.Align.Applications import MuscleCommandline
  >>> muscle_cline = MuscleCommandline(input="opuntia.fasta")
  >>> child = subprocess.Popen(str(muscle_cline),
  ...                          stdout=subprocess.PIPE,
  ...                          stderr=subprocess.PIPE,
  ...                          universal_newlines=True,
  ...                          shell=(sys.platform!="win32"))
  ...
  >>> from Bio import AlignIO
  >>> align = AlignIO.read(child.stdout, "fasta")
  >>> print(align)
  Alignment with 7 rows and 906 columns
  TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA
gi|6273289|gb|AF191663.1|AF191663
  TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA
gi|6273291|gb|AF191665.1|AF191665
  TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA
gi|6273290|gb|AF191664.1|AF191664
  TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA
gi|6273287|gb|AF191661.1|AF191661
  TATACATAAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA
gi|6273286|gb|AF191660.1|AF191660
  TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA
gi|6273285|gb|AF191659.1|AF191659
  TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA
gi|6273284|gb|AF191658.1|AF191658
>>



6.5.4 MUSCLE using stdin and stdout
===================================

  We don't actually need to have our FASTA input sequences prepared in a
file, because by default MUSCLE will read in the input sequence from
standard input! Note this is a bit more advanced and fiddly, so don't
bother with this technique unless you need to.
  First, we'll need some unaligned sequences in memory as SeqRecord
objects. For this demonstration I'm going to use a filtered version of
the original FASTA file (using a generator expression), taking just six
of the seven sequences:
<<>>> from Bio import SeqIO
  >>> records = (r for r in SeqIO.parse("opuntia.fasta", "fasta") if
len(r) < 900)
>>

  Then we create the MUSCLE command line, leaving the input and output
to their defaults (stdin and stdout). I'm also going to ask for strict
ClustalW format as for the output.
<<>>> from Bio.Align.Applications import MuscleCommandline
  >>> muscle_cline = MuscleCommandline(clwstrict=True)
  >>> print(muscle_cline)
  muscle -clwstrict
>>

  Now for the fiddly bits using the subprocess module, stdin and stdout:
<<>>> import subprocess
  >>> import sys
  >>> child = subprocess.Popen(str(cline),
  ...                          stdin=subprocess.PIPE,
  ...                          stdout=subprocess.PIPE,
  ...                          stderr=subprocess.PIPE,
  ...                          universal_newlines=True,
  ...                          shell=(sys.platform!="win32"))
>>

  That should start MUSCLE, but it will be sitting waiting for its FASTA
input sequences, which we must supply via its stdin handle:
<<>>> SeqIO.write(records, child.stdin, "fasta")
  6
  >>> child.stdin.close()
>>

  After writing the six sequences to the handle, MUSCLE will still be
waiting to see if that is all the FASTA sequences or not -- so we must
signal that this is all the input data by closing the handle. At that
point MUSCLE should start to run, and we can ask for the output:
<<>>> from Bio import AlignIO
  >>> align = AlignIO.read(child.stdout, "clustal")
  >>> print(align)
  Alignment with 6 rows and 900 columns
  TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA
gi|6273290|gb|AF191664.1|AF19166
  TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA
gi|6273289|gb|AF191663.1|AF19166
  TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA
gi|6273287|gb|AF191661.1|AF19166
  TATACATAAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA
gi|6273286|gb|AF191660.1|AF19166
  TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA
gi|6273285|gb|AF191659.1|AF19165
  TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA
gi|6273284|gb|AF191658.1|AF19165
>>

  Wow! There we are with a new alignment of just the six records,
without having created a temporary FASTA input file, or a temporary
alignment output file. However, a word of caution: Dealing with errors
with this style of calling external programs is much more complicated.
It also becomes far harder to diagnose problems, because you can't try
running MUSCLE manually outside of Biopython (because you don't have the
input file to supply). There can also be subtle cross platform issues
(e.g. Windows versus Linux), and how you run your script can have an
impact (e.g. at the command line, from IDLE or an IDE, or as a GUI
script). These are all generic Python issues though, and not specific to
Biopython.
  If you find working directly with subprocess like this scary, there is
an alternative. If you execute the tool with muscle_cline() you can
supply any standard input as a big string, muscle_cline(stdin=...). So,
provided your data isn't very big, you can prepare the FASTA input in
memory as a string using StringIO (see Section ??):
<<>>> from Bio import SeqIO
  >>> records = (r for r in SeqIO.parse("opuntia.fasta", "fasta") if
len(r) < 900)
  >>> from io import StringIO
  >>> handle = StringIO()
  >>> SeqIO.write(records, handle, "fasta")
  6
  >>> data = handle.getvalue()
>>

  You can then run the tool and parse the alignment as follows:
<<>>> stdout, stderr = muscle_cline(stdin=data)
  >>> from Bio import AlignIO
  >>> align = AlignIO.read(StringIO(stdout), "clustal")
  >>> print(align)
  Alignment with 6 rows and 900 columns
  TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA
gi|6273290|gb|AF191664.1|AF19166
  TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA
gi|6273289|gb|AF191663.1|AF19166
  TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA
gi|6273287|gb|AF191661.1|AF19166
  TATACATAAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA
gi|6273286|gb|AF191660.1|AF19166
  TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA
gi|6273285|gb|AF191659.1|AF19165
  TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA
gi|6273284|gb|AF191658.1|AF19165
>>

  You might find this easier, but it does require more memory (RAM) for
the strings used for the input FASTA and output Clustal formatted data.


6.5.5 EMBOSS needle and water
=============================
    The EMBOSS (5) suite includes the water and needle tools for
Smith-Waterman algorithm local alignment, and Needleman-Wunsch global
alignment. The tools share the same style interface, so switching
between the two is trivial -- we'll just use needle here.
  Suppose you want to do a global pairwise alignment between two
sequences, prepared in FASTA format as follows:
<<>HBA_HUMAN
  MVLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKTYFPHFDLSHGSAQVKGHG
  KKVADALTNAVAHVDDMPNALSALSDLHAHKLRVDPVNFKLLSHCLLVTLAAHLPAEFTP
  AVHASLDKFLASVSTVLTSKYR
>>

  in a file alpha.faa, and secondly in a file beta.faa:
<<>HBB_HUMAN
  MVHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPK
  VKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFG
  KEFTPPVQAAYQKVVAGVANALAHKYH
>>

  You can find copies of these example files with the Biopython source
code under the Doc/examples/ directory.
  Let's start by creating a complete needle command line object in one
go:
<<>>> from Bio.Emboss.Applications import NeedleCommandline
  >>> needle_cline = NeedleCommandline(asequence="alpha.faa",
bsequence="beta.faa",
  ...                                  gapopen=10, gapextend=0.5,
outfile="needle.txt")
  >>> print(needle_cline)
  needle -outfile=needle.txt -asequence=alpha.faa -bsequence=beta.faa
-gapopen=10 -gapextend=0.5
>>

  Why not try running this by hand at the command prompt? You should see
it does a pairwise comparison and records the output in the file
needle.txt (in the default EMBOSS alignment file format).
  Even if you have EMBOSS installed, running this command may not work
-- you might get a message about "command not found" (especially on
Windows). This probably means that the EMBOSS tools are not on your PATH
environment variable. You can either update your PATH setting, or simply
tell Biopython the full path to the tool, for example:
<<>>> from Bio.Emboss.Applications import NeedleCommandline
  >>> needle_cline = NeedleCommandline(r"C:\EMBOSS\needle.exe",
  ...                                  asequence="alpha.faa",
bsequence="beta.faa",
  ...                                  gapopen=10, gapextend=0.5,
outfile="needle.txt")
>>

  Remember in Python that for a default string \n or \t means a new line
or a tab -- which is why we're put a letter "r" at the start for a raw
string.
  At this point it might help to try running the EMBOSS tools yourself
by hand at the command line, to familiarise yourself the other options
and compare them to the Biopython help text:
<<>>> from Bio.Emboss.Applications import NeedleCommandline
  >>> help(NeedleCommandline)
>>

  Note that you can also specify (or change or look at) the settings
like this:
<<>>> from Bio.Emboss.Applications import NeedleCommandline
  >>> needle_cline = NeedleCommandline()
  >>> needle_cline.asequence="alpha.faa"
  >>> needle_cline.bsequence="beta.faa"
  >>> needle_cline.gapopen=10
  >>> needle_cline.gapextend=0.5
  >>> needle_cline.outfile="needle.txt"
  >>> print(needle_cline)
  needle -outfile=needle.txt -asequence=alpha.faa -bsequence=beta.faa
-gapopen=10 -gapextend=0.5
  >>> print(needle_cline.outfile)
  needle.txt
>>

  Next we want to use Python to run this command for us. As explained
above, for full control, we recommend you use the built in Python
subprocess module, but for simple usage the wrapper object usually
suffices:
<<>>> stdout, stderr = needle_cline()
  >>> print(stdout + stderr)
  Needleman-Wunsch global alignment of two sequences
>>

  Next we can load the output file with Bio.AlignIO as discussed earlier
in this chapter, as the emboss format:
<<>>> from Bio import AlignIO
  >>> align = AlignIO.read("needle.txt", "emboss")
  >>> print(align)
  Alignment with 2 rows and 149 columns
  MV-LSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKTY...KYR HBA_HUMAN
  MVHLTPEEKSAVTALWGKV--NVDEVGGEALGRLLVVYPWTQRF...KYH HBB_HUMAN
>>

  In this example, we told EMBOSS to write the output to a file, but you
can tell it to write the output to stdout instead (useful if you don't
want a temporary output file to get rid of -- use stdout=True rather
than the outfile argument), and also to read one of the one of the
inputs from stdin (e.g. asequence="stdin", much like in the MUSCLE
example in the section above).
  This has only scratched the surface of what you can do with needle and
water. One useful trick is that the second file can contain multiple
sequences (say five), and then EMBOSS will do five pairwise alignments.


6.6 Pairwise sequence alignment
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*


  Pairwise sequence alignment is the process of aligning two sequences
to each other by optimizing the similarity score between them. Biopython
includes two built-in pairwise aligners: the 'old' Bio.pairwise2 module
and the new PairwiseAligner class within the Bio.Align module (since
Biopython version 1.72). Both can perform global and local alignments
and offer numerous options to change the alignment parameters. Although
pairwise2 has gained some speed and memory enhancements recently, the
new PairwiseAligner is much faster; so if you need to make many
alignments with larger sequences, the latter would be the tool to
choose. pairwise2, on the contrary, is also able to align lists, which
can be useful if your sequences do not consist of single characters
only. 
  Given that the parameters and sequences are the same, both aligners
will return the same alignments and alignment score (if the number of
alignments is too high they may return different subsets of all valid
alignments).


6.6.1 pairwise2
===============

  Bio.pairwise2 contains essentially the same algorithms as water
(local) and needle (global) from the EMBOSS (6) suite (see above) and
should return the same results. The pairwise2 module has undergone some
optimization regarding speed and memory consumption recently (Biopython
versions >1.67) so that for short sequences (global alignments: ~2000
residues, local alignments ~600 residues) it's faster (or equally fast)
to use pairwise2 than calling EMBOSS'  water or needle via the command
line tools.
  Suppose you want to do a global pairwise alignment between the same
two hemoglobin sequences from above (HBA_HUMAN, HBB_HUMAN) stored in
alpha.faa and beta.faa:
<<>>> from Bio import pairwise2
  >>> from Bio import SeqIO
  >>> seq1 = SeqIO.read("alpha.faa", "fasta")
  >>> seq2 = SeqIO.read("beta.faa", "fasta")
  >>> alignments = pairwise2.align.globalxx(seq1.seq, seq2.seq)
>>

  As you see, we call the alignment function with align.globalxx. The
tricky part are the last two letters of the function name (here: xx),
which are used for decoding the scores and penalties for matches (and
mismatches) and gaps. The first letter decodes the match score, e.g. x
means that a match counts 1 while mismatches have no costs. With m
general values for either matches or mismatches can be defined (for more
options see Biopython's API (7)). The second letter decodes the cost for
gaps; x means no gap costs at all, with s different penalties for
opening and extending a gap can be assigned. So, globalxx means that
only matches between both sequences are counted.
  Our variable alignments now contains a list of alignments (at least
one) which have the same optimal score for the given conditions. In our
example this are 80 different alignments with the score 72
(Bio.pairwise2 will return up to 1000 alignments). Have a look at one of
these alignments:
<<>>> len(alignments)
  80
  >>> print(alignments[0]) # doctest:+ELLIPSIS
  Alignment(seqA='MV-LSPADKTNV---K-A--A-WGKVGAHAG...YR-',
seqB='MVHL-----T--PEEKSAVTALWGKV----...Y-H', score=72.0, start=0,
end=217)
>>

  Each alignment is a named tuple consisting of the two aligned
sequences, the score, the start and the end positions of the alignment
(in global alignments the start is always 0 and the end the length of
the alignment). Bio.pairwise2 has a function format_alignment for a
nicer printout:
<<>>> print(pairwise2.format_alignment(*alignments[0])) #
doctest:+ELLIPSIS
  MV-LSPADKTNV---K-A--A-WGKVGAHAG---EY-GA-EALE-RMFLSF----PTTK-TY--F...YR
-
  || |     |     | |  | ||||        |  |  |||  |  |      |    |   |...| 

  MVHL-----T--PEEKSAVTALWGKV-----NVDE-VG-GEAL-GR--L--LVVYP---WT-QRF...Y-
H
    Score=72
  <BLANKLINE>
>>

  Since Biopython 1.77 the required parameters can be supplied with
keywords. The last example can now also be written as:
<<>>> alignments = pairwise2.align.globalxx(sequenceA=seq1.seq,
sequenceB=seq2.seq)
>>

  Better alignments are usually obtained by penalizing gaps: higher
costs for opening a gap and lower costs for extending an existing gap.
For amino acid sequences match scores are usually encoded in matrices
like PAM or BLOSUM. Thus, a more meaningful alignment for our example
can be obtained by using the BLOSUM62 matrix, together with a gap open
penalty of 10 and a gap extension penalty of 0.5 (using globalds):
<<>>> from Bio import pairwise2
  >>> from Bio import SeqIO
  >>> from Bio.Align import substitution_matrices
  >>> blosum62 = substitution_matrices.load("BLOSUM62")
  >>> seq1 = SeqIO.read("alpha.faa", "fasta")
  >>> seq2 = SeqIO.read("beta.faa", "fasta")
  >>> alignments = pairwise2.align.globalds(seq1.seq, seq2.seq,
blosum62, -10, -0.5)
  >>> len(alignments)
  2
  >>> print(pairwise2.format_alignment(*alignments[0]))
  MV-LSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKTY...KYR
  || |.|..|..|.|.|||| ......|............|.......||.
  MVHLTPEEKSAVTALWGKV-NVDEVGGEALGRLLVVYPWTQRFF...KYH
    Score=292.5

>>

  This alignment has the same score that we obtained earlier with EMBOSS
needle using the same sequences and the same parameters.
  Local alignments are called similarly with the function align.localXX,
where again XX stands for a two letter code for the match and gap
functions:
<<>>> from Bio import pairwise2
  >>> from Bio.Align import substitution_matrices
  >>> blosum62 = substitution_matrices.load("BLOSUM62")
  >>> alignments = pairwise2.align.localds("LSPADKTNVKAA", "PEEKSAV",
blosum62, -10, -1)
  >>> print(pairwise2.format_alignment(*alignments[0]))
  3 PADKTNV
    |..|..|
  1 PEEKSAV
    Score=16
  <BLANKLINE>
>>

  In recent Biopython versions, format_alignment will only print the 
aligned part of a local alignment (together with the start positions in
1-based notation, as shown in the above example). If you are also
interested in the non- aligned parts of the sequences, use the
keyword-parameter full_sequences=True:
<<>>> from Bio import pairwise2
  >>> from Bio.Align import substitution_matrices
  >>> blosum62 = substitution_matrices.load("BLOSUM62")
  >>> alignments = pairwise2.align.localds("LSPADKTNVKAA", "PEEKSAV",
blosum62, -10, -1)
  >>> print(pairwise2.format_alignment(*alignments[0],
full_sequences=True))
  LSPADKTNVKAA
    |..|..|   
  --PEEKSAV---
    Score=16
  <BLANKLINE>
>>

  Note that local alignments must, as defined by Smith & Waterman, have
a  positive score (>0). Thus, pairwise2 may return no alignments if no
score >0 has been obtained. Also, pairwise2 will not report alignments
which are the result of the addition of zero-scoring extensions on
either site. In the next example, the pairs serin/aspartate (S/D) and
lysin/asparagin (K/N) both have a match score of 0. As you see, the
aligned part has not been extended:
<<>>> from Bio import pairwise2
  >>> from Bio.Align import substitution_matrices
  >>> blosum62 = substitution_matrices.load("BLOSUM62")
  >>> alignments = pairwise2.align.localds("LSSPADKTNVKKAA",
"DDPEEKSAVNN", blosum62, -10, -1)
  >>> print(pairwise2.format_alignment(*alignments[0]))
  4 PADKTNV
    |..|..|
  3 PEEKSAV
    Score=16
  <BLANKLINE>
>>

  Instead of supplying a complete match/mismatch matrix, the match code
m allows for easy defining general match/mismatch values. The next
example uses match/mismatch scores of 5/-4 and gap penalties
(open/extend) of 2/0.5 using localms:
<<>>> alignments = pairwise2.align.localms("AGAACT", "GAC", 5, -4, -2,
-0.5)
  >>> print(pairwise2.format_alignment(*alignments[0]))
  2 GAAC
    | ||
  1 G-AC
    Score=13
  <BLANKLINE>
>>

  One useful keyword argument of the Bio.pairwise2.align functions is
score_only. When set to True it will only return the score of the best
alignment(s), but in a significantly shorter time. It will also allow
the alignment of longer sequences before a memory error is raised.
Another useful keyword argument is one_alignment_only=True which will
also result in some speed gain.
  Unfortunately, Bio.pairwise2 does not work with Biopython's multiple
sequence alignment objects (yet). However, the module has some
interesting advanced features: you can define your own match and gap
functions (interested in testing affine logarithmic gap costs?), gap
penalties and end gaps penalties can be different for both sequences,
sequences can be supplied as lists (useful if you have residues that are
encoded by more than one character), etc. These features are hard (if at
all) to realize with other alignment tools. For more details see the
modules documentation in Biopython's API (8).


6.6.2 PairwiseAligner
=====================
    The new Bio.Align.PairwiseAligner implements the Needleman-Wunsch,
Smith-Waterman, Gotoh (three-state), and Waterman-Smith-Beyer global and
local pairwise alignment algorithms. We refer to Durbin et al. [] for
in-depth information on sequence alignment algorithms.


6.6.2.1 Basic usage
-------------------

  To generate pairwise alignments, first create a PairwiseAligner
object:
<<>>> from Bio import Align
  >>> aligner = Align.PairwiseAligner()
>>
   The PairwiseAligner object aligner (see Section ??) stores the
alignment parameters to be used for the pairwise alignments.
  These attributes can be set in the constructor of the object or after
the object is made.
<<>>> aligner = Align.PairwiseAligner(match_score=1.0)
>>

  Or, equivalently:
<<>>> aligner.match_score = 1.0
>>

  Use the aligner.score method to calculate the alignment score between
two sequences:
<<>>> seq1 = "GAACT"
  >>> seq2 = "GAT"
  >>> score = aligner.score(seq1, seq2)
  >>> score
  3.0
>>

  To see the actual alignments, use the aligner.align method and iterate
over the PairwiseAlignment objects returned:
<<>>> alignments = aligner.align(seq1, seq2)
  >>> for alignment in alignments:
  ...     print(alignment)
  ... 
  GAACT
  ||--|
  GA--T
  <BLANKLINE>
  GAACT
  |-|-|
  G-A-T
  <BLANKLINE>
>>

  By default, a global pairwise alignment is performed, which finds the
optimal alignment over the whole length of seq1 and seq2. Instead, a
local alignment will find the subsequence of seq1 and seq2 with the
highest alignment score. Local alignments can be generated by setting
aligner.mode to "local":
<<>>> aligner.mode = 'local'
  >>> seq1 = "AGAACTC"
  >>> seq2 = "GAACT"
  >>> score = aligner.score(seq1, seq2)
  >>> score
  5.0
  >>> alignments = aligner.align(seq1, seq2)
  >>> for alignment in alignments:
  ...     print(alignment)
  ...
  AGAACTC
   |||||
   GAACT
  <BLANKLINE>
>>

  Note that there is some ambiguity in the definition of the best local
alignments if segments with a score 0 can be added to the alignment. We
follow the suggestion by Waterman & Eggert [] and disallow such
extensions.


6.6.2.2 The pairwise aligner object
-----------------------------------

  The PairwiseAligner object stores all alignment parameters to be used
for the pairwise alignments. To see an overview of the values for all
parameters, use
<<>>> print(aligner)
  Pairwise sequence aligner with parameters
    wildcard: None
    match_score: 1.000000
    mismatch_score: 0.000000
    target_internal_open_gap_score: 0.000000
    target_internal_extend_gap_score: 0.000000
    target_left_open_gap_score: 0.000000
    target_left_extend_gap_score: 0.000000
    target_right_open_gap_score: 0.000000
    target_right_extend_gap_score: 0.000000
    query_internal_open_gap_score: 0.000000
    query_internal_extend_gap_score: 0.000000
    query_left_open_gap_score: 0.000000
    query_left_extend_gap_score: 0.000000
    query_right_open_gap_score: 0.000000
    query_right_extend_gap_score: 0.000000
    mode: local
  <BLANKLINE>
>>
   See Sections ??, ??, and ?? below for the definition of these
parameters. The attribute mode (described above in Section ??) can be
set equal to "global" or "local" to specify global or local pairwise
alignment, respectively.
  Depending on the gap scoring parameters (see Sections ?? and ??) and
mode, a PairwiseAligner object automatically chooses the appropriate
algorithm to use for pairwise sequence alignment. To verify the selected
algorithm, use
<<>>> aligner.algorithm
  'Smith-Waterman'
>>
   This attribute is read-only.
  A PairwiseAligner object also stores the precision epsilon to be used
during alignment. The value of epsilon is stored in the attribute
aligner.epsilon, and by default is equal to 10^-6:
<<>>> aligner.epsilon
  1e-06
>>
   Two scores will be considered equal to each other for the purpose of
the alignment if the absolute difference between them is less than
epsilon.


6.6.2.3 Substitution scores
---------------------------

  Substitution scores define the value to be added to the total score
when two letters (nucleotides or amino acids) are aligned to each other.
The substitution scores to be used by the PairwiseAligner can be
specified in two ways:


   - By specifying a match score for identical letters, and a mismatch
   scores for mismatched letters. Nucleotide sequence alignments are
   typically based on match and mismatch scores. For example, by default
   BLAST [] uses a match score of +1 and a mismatch score of -2 for
   nucleotide alignments by megablast, with a gap penalty of 2.5 (see
   section ?? for more information on gap scores). Match and mismatch
   scores can be specified by setting the match and mismatch attributes
   of the PairwiseAligner object:
   <<>>> from Bio import Align
     >>> aligner = Align.PairwiseAligner()
     >>> aligner.match_score
     1.0
     >>> aligner.mismatch_score
     0.0
     >>> score = aligner.score("ACGT","ACAT")
     >>> print(score)
     3.0
     >>> aligner.match_score = 1.0
     >>> aligner.mismatch_score = -2.0
     >>> aligner.gap_score = -2.5
     >>> score = aligner.score("ACGT","ACAT")
     >>> print(score)
     1.0
   >>
  When using match and mismatch scores, you can specify a wildcard
   character (+None+ by default) for unknown letters. These will get a
   zero score in alignments, irrespective of the value of the match or
   mismatch score: 
   <<>>> aligner.wildcard = "?"
     >>> score = aligner.score("ACGT","AC?T")
     >>> print(score)
     3.0
   >>

   - Alternatively, you can use the substitution_matrix attribute of the
   PairwiseAligner object to specify a substitution matrix. This allows
   you to apply different scores for different pairs of matched and
   mismatched letters. This is typically used for amino acid sequence
   alignments. For example, by default BLAST [] uses the BLOSUM62
   substitution matrix for protein alignments by blastp. This
   substitution matrix is available from Biopython:
   <<>>> from Bio.Align import substitution_matrices
     >>> substitution_matrices.load()  #doctest: +ELLIPSIS
     ['BENNER22', 'BENNER6', 'BENNER74', 'BLOSUM45', 'BLOSUM50',
   'BLOSUM62', ..., 'TRANS']
     >>> matrix = substitution_matrices.load("BLOSUM62")
     >>> print(matrix)  #doctest: +ELLIPSIS
     #  Matrix made by matblas from blosum62.iij
     ...
          A    R    N    D    C    Q ...
     A  4.0 -1.0 -2.0 -2.0  0.0 -1.0 ...
     R -1.0  5.0  0.0 -2.0 -3.0  1.0 ...
     N -2.0  0.0  6.0  1.0 -3.0  0.0 ...
     D -2.0 -2.0  1.0  6.0 -3.0  0.0 ...
     C  0.0 -3.0 -3.0 -3.0  9.0 -3.0 ...
     Q -1.0  1.0  0.0  0.0 -3.0  5.0 ...
     ...
     >>> aligner.substitution_matrix = matrix
     >>> score = aligner.score("ACDQ", "ACDQ")
     >>> score
     24.0
     >>> score = aligner.score("ACDQ", "ACNQ")
     >>> score
     19.0
   >>
  When using a substitution matrix, X is not interpreted as an unknown
   character. Instead, the score provided by the substutition matrix
   will be used:
   <<>>> matrix['D','X']
     -1.0
     >>> score = aligner.score("ACDQ", "ACXQ")
     >>> score
     17.0
   >>


  By default, aligner.substitution_matrix is None. The attributes
aligner.match_score and aligner.mismatch_score are ignored if
aligner.substitution_matrix is not None. Setting aligner.match_score or
aligner.mismatch_score to valid values will reset
aligner.substitution_matrix to None.


6.6.2.4 Affine gap scores
-------------------------

  Affine gap scores are defined by a score to open a gap, and a score to
extend an existing gap:
  gap score = open gap score + (n-1) X extend gap score,
  where n is the length of the gap. Biopython's pairwise sequence
aligner allows fine-grained control over the gap scoring scheme by
specifying the following twelve attributes of a PairwiseAligner object:
   --------------------------------------------------------------------

   ------------------------------------------------------------------
   |Opening scores                |Extending scores                 |
   ------------------------------------------------------------------
   |query_left_open_gap_score     |query_left_extend_gap_score      |
   |query_internal_open_gap_score |query_internal_extend_gap_score  |
   |query_right_open_gap_score    |query_right_extend_gap_score     |
   |target_left_open_gap_score    |target_left_extend_gap_score     |
   |target_internal_open_gap_score|target_internal_extend_gap_score |
   |target_right_open_gap_score   |target_right_extend_gap_score    |
   ------------------------------------------------------------------

   --------------------------------------------------------------------

  These attributes allow for different gap scores for internal gaps and
on either end of the sequence, as shown in this example:
   --------------------------------------------------------------------

   ------------------------------------------------
   |target|query|score                            |
   ------------------------------------------------
   |  A   |  -  |query left open gap score        |
   |  C   |  -  |query left extend gap score      |
   |  C   |  -  |query left extend gap score      |
   |  G   |  G  |match score                      |
   |  G   |  T  |mismatch score                   |
   |  G   |  -  |query internal open gap score    |
   |  A   |  -  |query internal extend gap score  |
   |  A   |  -  |query internal extend gap score  |
   |  T   |  T  |match score                      |
   |  A   |  A  |match score                      |
   |  G   |  -  |query internal open gap score    |
   |  C   |  C  |match score                      |
   |  -   |  C  |target internal open gap score   |
   |  -   |  C  |target internal extend gap score |
   |  C   |  C  |match score                      |
   |  T   |  G  |mismatch score                   |
   |  C   |  C  |match score                      |
   |  -   |  C  |target internal open gap score   |
   |  A   |  A  |match score                      |
   |  -   |  T  |target right open gap score      |
   |  -   |  A  |target right extend gap score    |
   |  -   |  A  |target right extend gap score    |
   ------------------------------------------------

   --------------------------------------------------------------------

  For convenience, PairwiseAligner objects have additional attributes
that refer to a number of these values collectively, as shown
(hierarchically) in Table ??.
   --------------------------------------------------------------------

     Table 6.1: Meta-attributes of the pairwise aligner objects.

   ---------------------------------------------------------------------
   ---------------------------
   |Meta-attribute             |Attributes it maps to                   
                            |
   ---------------------------------------------------------------------
   ---------------------------
   |gap_score                  |target_gap_score, query_gap_score       
                            |
   |open_gap_score             |target_open_gap_score,
   query_open_gap_score                       |
   |extend_gap_score           |target_extend_gap_score,
   query_extend_gap_score                   |
   |internal_gap_score         |target_internal_gap_score,
   query_internal_gap_score               |
   |internal_open_gap_score    |target_internal_open_gap_score,
   query_internal_open_gap_score     |
   |internal_extend_gap_score  |target_internal_extend_gap_score,
   query_internal_extend_gap_score |
   |end_gap_score              |target_end_gap_score,
   query_end_gap_score                         |
   |end_open_gap_score         |target_end_open_gap_score,
   query_end_open_gap_score               |
   |end_extend_gap_score       |target_end_extend_gap_score,
   query_end_extend_gap_score           |
   |left_gap_score             |target_left_gap_score,
   query_left_gap_score                       |
   |right_gap_score            |target_right_gap_score,
   query_right_gap_score                     |
   |left_open_gap_score        |target_left_open_gap_score,
   query_left_open_gap_score             |
   |left_extend_gap_score      |target_left_extend_gap_score,
   query_left_extend_gap_score         |
   |right_open_gap_score       |target_right_open_gap_score,
   query_right_open_gap_score           |
   |right_extend_gap_score     |target_right_extend_gap_score,
   query_right_extend_gap_score       |
   |target_open_gap_score      |target_internal_open_gap_score,
   target_left_open_gap_score,       |
   |                           |target_right_open_gap_score             
                            |
   |target_extend_gap_score    |target_internal_extend_gap_score,
   target_left_extend_gap_score,   |
   |                           |target_right_extend_gap_score           
                            |
   |target_gap_score           |target_open_gap_score,
   target_extend_gap_score                    |
   |query_open_gap_score       |query_internal_open_gap_score,
   query_left_open_gap_score,         |
   |                           |query_right_open_gap_score              
                            |
   |query_extend_gap_score     |query_internal_extend_gap_score,
   query_left_extend_gap_score,     |
   |                           |query_right_extend_gap_score            
                            |
   |query_gap_score            |query_open_gap_score,
   query_extend_gap_score                      |
   |target_internal_gap_score  |target_internal_open_gap_score,
   target_internal_extend_gap_score  |
   |target_end_gap_score       |target_end_open_gap_score,
   target_end_extend_gap_score            |
   |target_end_open_gap_score  |target_left_open_gap_score,
   target_right_open_gap_score           |
   |target_end_extend_gap_score|target_left_extend_gap_score,
   target_right_extend_gap_score       |
   |target_left_gap_score      |target_left_open_gap_score,
   target_left_extend_gap_score          |
   |target_right_gap_score     |target_right_open_gap_score,
   target_right_extend_gap_score        |
   |query_end_gap_score        |query_end_open_gap_score,
   query_end_extend_gap_score              |
   |query_end_open_gap_score   |query_left_open_gap_score,
   query_right_open_gap_score             |
   |query_end_extend_gap_score |query_left_extend_gap_score,
   query_right_extend_gap_score         |
   |query_internal_gap_score   |query_internal_open_gap_score,
   query_internal_extend_gap_score    |
   |query_left_gap_score       |query_left_open_gap_score,
   query_left_extend_gap_score            |
   |query_right_gap_score      |query_right_open_gap_score,
   query_right_extend_gap_score          |
   ---------------------------------------------------------------------
   ---------------------------

   --------------------------------------------------------------------



6.6.2.5 General gap scores
--------------------------

  For even more fine-grained control over the gap scores, you can
specify a gap scoring function. For example, the gap scoring function
below disallows a gap after two nucleotides in the query sequence:
<<>>> from Bio import Align
  >>> aligner = Align.PairwiseAligner()
  >>> def my_gap_score_function(start, length):
  ...     if start==2:
  ...         return -1000
  ...     else:
  ...         return -1 * length
  ...
  >>> aligner.query_gap_score = my_gap_score_function
  >>> alignments = aligner.align("AACTT", "AATT")
  >>> for alignment in alignments:
  ...     print(alignment)
  ... 
  AACTT
  -|.||
  -AATT
  <BLANKLINE>
  AACTT
  |-.||
  A-ATT
  <BLANKLINE>
  AACTT
  ||.-|
  AAT-T
  <BLANKLINE>
  AACTT
  ||.|-
  AATT-
  <BLANKLINE>
>>



6.6.2.6 Iterating over alignments
---------------------------------

  The alignments returned by aligner.align are a kind of immutable
iterable objects (similar to range). While they appear similarto a tuple
or list of PairwiseAlignment objects, they are different in the sense
that each PairwiseAlignment object is created dynamically when it is
needed. This approach was chosen because the number of alignments can be
extremely large, in particular for poor alignments (see Section ?? for
an example).
  You can perform the following operations on alignments: 

   - len(alignments) returns the number of alignments stored. This
   function returns quickly, even if the number of alignments is huge.
   If the number of alignments is extremely large (typically, larger
   than 9,223,372,036,854,775,807, which is the largest integer that can
   be stored as a long int on 64 bit machines), len(alignments) will
   raise an OverflowError. A large number of alignments suggests that
   the alignment quality is low.
   <<>>> from Bio import Align
     >>> aligner = Align.PairwiseAligner()
     >>> alignments = aligner.align("AAA", "AA")
     >>> len(alignments)
     3
   >>

   - You can extract a specific alignment by index:
   <<>>> from Bio import Align
     >>> aligner = Align.PairwiseAligner()
     >>> alignments = aligner.align("AAA", "AA")
     >>> print(alignments[2])
     AAA
     -||
     -AA
     <BLANKLINE>
     >>> print(alignments[0])
     AAA
     ||-
     AA-
     <BLANKLINE>
   >>

   - You can iterate over alignments, for example as in 
   <<>>> for alignment in alignments:
     ...     print(alignment)
     ...
   >>
  Note that alignments can be reused, i.e. you can iterate over
   alignments multiple times:
   <<>>> from Bio import Align
     >>> aligner = Align.PairwiseAligner()
     >>> alignments = aligner.align("AAA", "AA")
     >>> for alignment in alignments:
     ...     print(alignment)
     ...
     AAA
     ||-
     AA-
     <BLANKLINE>
     AAA
     |-|
     A-A
     <BLANKLINE>
     AAA
     -||
     -AA
     <BLANKLINE>
     >>> for alignment in alignments:
     ...     print(alignment)
     ...
     AAA
     ||-
     AA-
     <BLANKLINE>
     AAA
     |-|
     A-A
     <BLANKLINE>
     AAA
     -||
     -AA
     <BLANKLINE>
   >>
  You can also convert the alignments iterator into a list or tuple: 
   <<>>> alignments = list(alignments)
   >>
  It is wise to check the number of alignments by calling
   len(alignments) before attempting to call list(alignments) to save
   all alignments as a list. 
   - The alignment score (which has the same value for each alignment in
   alignments) is stored as an attribute. This allows you to check the
   alignment score before proceeding to extract individual alignments:
   <<>>> print(alignments.score)
     2.0
   >>




6.6.2.7 Alignment objects
-------------------------
   The aligner.align method returns PairwiseAlignment objects, each
representing one alignment between the two sequences.
<<>>> from Bio import Align
  >>> aligner = Align.PairwiseAligner()
  >>> seq1 = "GAACT"
  >>> seq2 = "GAT"
  >>> alignments = aligner.align(seq1, seq2)
  >>> alignment = alignments[0]
  >>> alignment # doctest: +SKIP
  <Bio.Align.PairwiseAlignment object at 0x10204d250>
>>

  Each alignment stores the alignment score:
<<>>> alignment.score
  3.0
>>
   as well as pointers to the sequences that were aligned:
<<>>> alignment.target
  'GAACT'
  >>> alignment.query
  'GAT'
>>

  Print the PairwiseAlignment object to show the alignment explicitly:
<<>>> print(alignment)
  GAACT
  ||--|
  GA--T
  <BLANKLINE>
>>

  The length of the alignment is defined as the number of aligned
sequences, which is always 2 for a pairwise alignment:
<<>>> len(alignment)
  2
>>

  The shape property returns a tuple consisting of the length of the
alignnment and the number of columns in the alignment as printed:
<<>>> alignment.shape
  (2, 5)
>>

  For local alignments, sections that are not aligned are not included
in the number of columns: 
<<>>> aligner.mode = 'local'
  >>> local_alignments = aligner.align("TGAACT", "GAC")
  >>> local_alignment = local_alignments[0]
  >>> print(local_alignment)
  TGAACT
   ||-|
   GA-C
  <BLANKLINE>
  >>> local_alignment.shape
  (2, 4)
>>

  Use the aligned property to find the start and end indices of
subsequences in the target and query sequence that were aligned to each
other. Generally, if the alignment between target (t) and query (q)
consists of N chunks, you get two tuples of length N:
<<(
      ((t_start1, t_end1), (t_start2, t_end2), ..., (t_startN, t_endN)),
      ((q_start1, q_end1), (q_start2, q_end2), ..., (q_startN, q_endN)),
  )
>>

  In the current example, alignment.aligned returns two tuples of length
2:
<<>>> alignment.aligned
  (((0, 2), (4, 5)), ((0, 2), (2, 3)))
>>
   while for the alternative alignment, two tuples of length 3 are
returned:
<<>>> alignment = alignments[1]
  >>> print(alignment)
  GAACT
  |-|-|
  G-A-T
  <BLANKLINE>
  >>> alignment.aligned
  (((0, 1), (2, 3), (4, 5)), ((0, 1), (1, 2), (2, 3)))
>>
   Note that different alignments may have the same subsequences aligned
to each other. In particular, this may occur if alignments differ from
each other in terms of their gap placement only:
<<>>> aligner.mode = "global"
  >>> aligner.mismatch_score = -10
  >>> alignments = aligner.align("AAACAAA", "AAAGAAA")
  >>> len(alignments)
  2
  >>> print(alignments[0])
  AAAC-AAA
  |||--|||
  AAA-GAAA
  <BLANKLINE>
  >>> alignments[0].aligned
  (((0, 3), (4, 7)), ((0, 3), (4, 7)))
  >>> print(alignments[1])
  AAA-CAAA
  |||--|||
  AAAG-AAA
  <BLANKLINE>
  >>> alignments[1].aligned
  (((0, 3), (4, 7)), ((0, 3), (4, 7)))
>>
   The aligned property can be used to identify alignments that are
identical to each other in terms of their aligned sequences.
  The sort method sorts the alignment sequences. By default, sorting is
done based on the id attribute of each sequence if available, or the
sequence contents otherwise. 
<<>>> print(local_alignment)
  TGAACT
   ||-|
   GA-C
  <BLANKLINE>
  >>> local_alignment.sort()
  >>> print(local_alignment)
   GA-C
   ||-|
  TGAACT
  <BLANKLINE>
>>
   Alternatively, you can supply a key function to determine the sort
order. For example, you can sort the sequences by increasing GC content:
<<>>> from Bio.SeqUtils import GC
  >>> local_alignment.sort(key=GC)
  >>> print(local_alignment)
  TGAACT
   ||-|
   GA-C
  <BLANKLINE>
>>
   The reverse argument lets you reverse the sort order to obtain the
sequences in decreasing GC content: 
<<>>> local_alignment.sort(key=GC, reverse=True)
  >>> print(local_alignment)
   GA-C
   ||-|
  TGAACT
  <BLANKLINE>
>>

  Use the substitutions method to find the number of substitutions
between each pair of nucleotides: 
<<>>> target = "AAAAAAAACCCCCCCCGGGGGGGGTTTTTTTT"
  >>> query = "AAAAAAACCCTCCCCGGCCGGGGTTTAGTTT"
  >>> alignments = aligner.align(target, query)
  >>> aligner.mismatch_score = -1
  >>> aligner.gap_score = -1
  >>> alignments = aligner.align(target, query)
  >>> len(alignments)
  8
  >>> print(alignments[0])
  AAAAAAAACCCCCCCCGGGGGGGGTTTTTTTT
  |||||||-|||.||||||..|||||||..|||
  AAAAAAA-CCCTCCCCGGCCGGGGTTTAGTTT
  <BLANKLINE>
  >>> m = alignments[0].substitutions
  >>> print(m)
      A   C   G   T
  A 7.0 0.0 0.0 0.0
  C 0.0 7.0 0.0 1.0
  G 0.0 2.0 6.0 0.0
  T 1.0 0.0 1.0 6.0
  <BLANKLINE>
>>

  Note that the matrix is not symmetric: rows correspond to the target
sequence, and columns to the query sequence. For example, the number of
G's in the target sequence that are aligned to a C in the query sequence
is 
<<>>> m['G', 'C']
  2.0
>>
   and the number of C's in the query sequence tat are aligned to a T in
the query sequence is 
<<>>> m['C', 'G']
  0.0
>>
   To get a symmetric matrix, use 
<<>>> m += m.transpose()
  >>> m /= 2.0
  >>> print(m)
      A   C   G   T
  A 7.0 0.0 0.0 0.5
  C 0.0 7.0 1.0 0.5
  G 0.0 1.0 6.0 0.5
  T 0.5 0.5 0.5 6.0
  <BLANKLINE>
  >>> m['G', 'C']
  1.0
  >>> m['C', 'G']
  1.0
>>
   The total number of substitutions between C's and G's in the
alignment is 1.0 + 1.0 = 2.
  The map method can be applied on a pairwise alignment alignment1 to
find the pairwise alignment of the query of alignment2 to the target of
alignment1, where the target of alignment2 and the query of alignment1
are identical. A typical example is where alignment1 is the pairwise
alignment between a chromosome and a transcript, alignment2 is the
pairwise alignment between the transcript and a sequence (e.g., an
RNA-seq read), and we want to find the alignment of the sequence to the
chromosome:
<<>>> aligner.mode = 'local'
  >>> aligner.open_gap_score = -1
  >>> aligner.extend_gap_score = 0
  >>> chromosome = "AAAAAAAACCCCCCCAAAAAAAAAAAGGGGGGAAAAAAAA"
  >>> transcript = "CCCCCCCGGGGGG"
  >>> alignments1 = aligner.align(chromosome, transcript)
  >>> len(alignments1)
  1
  >>> alignment1 = alignments1[0]
  >>> print(alignment1)
  AAAAAAAACCCCCCCAAAAAAAAAAAGGGGGGAAAAAAAA
          |||||||-----------||||||
          CCCCCCC-----------GGGGGG
  <BLANKLINE>
  >>> sequence = "CCCCGGGG"
  >>> alignments2 = aligner.align(transcript, sequence)
  >>> len(alignments2)
  1
  >>> alignment2 = alignments2[0]
  >>> print(alignment2)
  CCCCCCCGGGGGG
     ||||||||
     CCCCGGGG
  <BLANKLINE>
  >>> mapped_alignment = alignment1.map(alignment2)
  >>> print(mapped_alignment)
  AAAAAAAACCCCCCCAAAAAAAAAAAGGGGGGAAAAAAAA
             ||||-----------||||
             CCCC-----------GGGG
  <BLANKLINE>
  >>> format(mapped_alignment, "psl")
  '8\t0\t0\t0\t0\t0\t1\t11\t+\tquery\t8\t0\t8\ttarget\t40\t11\t30\t2\t4,
4,\t0,4,\t11,26,\n'
>>

  Mapping the alignment does not depend on the sequence contents. If we
delete the sequence contents, the same alignment is found in PSL format
(though we obviously lose the ability to print the sequence alignment):
<<>>> from Bio.Seq import Seq
  >>> alignment1.target = Seq(None, len(alignment1.target))
  >>> alignment1.query = Seq(None, len(alignment1.query))
  >>> alignment2.target = Seq(None, len(alignment2.target))
  >>> alignment2.query = Seq(None, len(alignment2.query))
  >>> mapped_alignment = alignment1.map(alignment2)
  >>> format(mapped_alignment, "psl")
  '8\t0\t0\t0\t0\t0\t1\t11\t+\tquery\t8\t0\t8\ttarget\t40\t11\t30\t2\t4,
4,\t0,4,\t11,26,\n'
>>

Slicing and indexing a pairwise alignment

  Currently, only slices of the form alignment[:, i:j] are implemented,
where i and j are integers or are absent. This returns a new
PairwiseAlignment object that includes only the columns i through j in
the printed alignment. To illustrate this, in the following example the
printed alignment has 5 columns:
<<>>> print(alignment)
  GAACT
  |-|-|
  G-A-T
  <BLANKLINE>
>>

  Extracting the first 4 columns gives: 
<<>>> alignment[:, :4]  # doctest:+ELLIPSIS
  <Bio.Align.PairwiseAlignment object at ...>
  >>> print(alignment[:, :4])
  GAACT
  |-|-
  G-A-T
  <BLANKLINE>
>>
   Here, the final T nucleotides are still shown, but they are not
aligned to each other. Note that alignment is a global alignment, but
alignment[:, :4] is a local alignment.
  Similarly, extracting the last 3 columns gives: 
<<>>> alignment[:, -3:]  # doctest:+ELLIPSIS
  <Bio.Align.PairwiseAlignment object at ...>
  >>> print(alignment[:, -3:])
  GAACT
    |-|
   GA-T
  <BLANKLINE>
>>
   This is also now a local alignment, with the initial GA nucleotides
in the target and G nucleotide in the query not aligned to each other.
Exporting alignments

  Use the format method to create a string representation of the
alignment in various file formats. This method takes an argument fmt
specifying the file format, and may take additional keyword arguments
depending on file type. The following values for fmt are supported:


   - "" (empty string; default): Create a human-readable representation
   of the alignment (same as when you print the alignment). 
   - "SAM": Create a line representing the alignment in the Sequence
   Alignment/Map (SAM) format: 
   <<>>> alignment.format('sam')
     'query\t0\ttarget\t1\t255\t1M1D1M1D1M\t*\t0\t0\tGAT\t*\tAS:i:3\n'
   >>

   - "BED": Create a line representing the alignment in the Browser
   Extensible Data (BED) file format: 
   <<>>> alignment.format('bed')
     'target\t0\t5\tquery\t3.0\t+\t0\t5\t0\t3\t1,1,1,\t0,2,4,\n'
   >>

   - "PSL": Create a line representing the alignment in the Pattern
   Space Layout (PSL) file format as generated by BLAT []). 
   <<>>> alignment.format('psl')
     '3\t0\t0\t0\t0\t0\t2\t2\t+\tquery\t3\t0\t3\ttarget\t5\t0\t5\t3\t1,1
   ,1,\t0,1,2,\t0,2,4,\n'
   >>
  The first four columns in the PSL output contain the number of matched
   and mismatched characters, the number of matches to repeat regions,
   and the number of matches to unknown nucleotides. Repeat regions in
   the target sequence are indicated by masking the sequence as
   lower-case or upper-case characters, as defined by the following
   values for the mask keyword argument: 

      - False (default): Do not count matches to masked sequences
      separately; 
      - "lower": Count and report matches to lower-case characters as
      matches to repeat regions; 
      - "upper": Count and report matches to upper-case characters as
      matches to repeat regions; 
  The character used for unknown nucleotides is defined by the wildcard
   argument. For consistency with BLAT, the wildcard character is "N" by
   default. Use wildcard=None if you don't want to count matches to any
   unknown nucleotides separately. 
   <<>>> from Bio import Align
     >>> aligner = Align.PairwiseAligner()
     >>> aligner.mismatch_score = -1
     >>> aligner.internal_gap_score = -5
     >>> aligner.wildcard = "N"
     >>> target = "AAAAAAAggggGGNGAAAAA"
     >>> query = "GGTGGGGG"
     >>> alignments = aligner.align(target.upper(), query)
     >>> print(len(alignments))
     1
     >>> alignment = alignments[0]
     >>> print(alignment)
     AAAAAAAGGGGGGNGAAAAA
     -------||.|||.|-----
     -------GGTGGGGG-----
     <BLANKLINE>
     >>> alignment.score
     5.0
     >>> alignment.target
     'AAAAAAAGGGGGGNGAAAAA'
     >>> alignment.target = target
     >>> alignment.target
     'AAAAAAAggggGGNGAAAAA'
     >>> print(alignment)
     AAAAAAAggggGGNGAAAAA
     -------....||.|-----
     -------GGTGGGGG-----
     <BLANKLINE>
     >>> print(alignment.format("psl"))  #doctest: +NORMALIZE_WHITESPACE
     6   1   0   1   0   0   0   0   +   query   8   0   8   target   20
     7   15   1   8,   0,   7,
     >>> print(alignment.format("psl", mask="lower"))  #doctest:
   +NORMALIZE_WHITESPACE
     3   1   3   1   0   0   0   0   +   query   8   0   8   target   20
     7   15   1   8,   0,   7,
     >>> print(alignment.format("psl", mask="lower", wildcard=None)) 
   #doctest: +NORMALIZE_WHITESPACE
     3   2   3   0   0   0   0   0   +   query   8   0   8   target   20
     7   15   1   8,   0,   7,
   >>


  In addition to the format method, you can use Python's built-in format
function: 
<<>>> print(format(alignment, "psl"))  #doctest: +NORMALIZE_WHITESPACE
  6   1   0   1   0   0   0   0   +   query   8   0   8   target   20  
7   15   1   8,   0,   7,
>>
   allowing PairwiseAlignment objects to be used in formatted (f-)
strings in Python: 
<<>>> print(f"The alignment in PSL format is '{alignment:psl}'.") 
#doctest: +NORMALIZE_WHITESPACE
  The alignment in PSL format is '6   1   0   1   0   0   0   0   +  
query   8   0   8   target   20   7   15   1   8,   0,   7,
  '
>>
   Note that optional keyword arguments cannot be used with the format
function or with formatted strings.


6.6.2.8 Aligning to the reverse strand
--------------------------------------

  By default, the pairwise aligner aligns the forward strand of the
query to the forward strand of the target. To calculate the alignment
score for seq2 to the reverse strand of seq1, use strand="-": 
<<>>> from Bio import Align
  >>> from Bio.Seq import reverse_complement
  >>> seq1 = "AAAACCC"
  >>> seq2 = "AACC"
  >>> aligner = Align.PairwiseAligner()
  >>> aligner.mismatch_score = -1
  >>> aligner.internal_gap_score = -1
  >>> aligner.score(seq1, seq2)  # strand is "+" by default
  4.0
  >>> aligner.score(seq1, reverse_complement(seq2), strand="-")
  4.0
  >>> aligner.score(seq1, seq2, strand="-")
  0.0
  >>> aligner.score(seq1, reverse_complement(seq2))
  0.0

>>

  The alignments against the reverse strand can be obtained by
specifying strand="-" when calling aligner.align: 
<<>>> alignments = aligner.align(seq1, seq2)
  >>> len(alignments)
  1
  >>> print(alignments[0])
  AAAACCC
  --||||-
  --AACC-
  <BLANKLINE>
  >>> print(alignments[0].format("bed"))  #doctest:
+NORMALIZE_WHITESPACE
  target   2   6   query   4.0   +   2   6   0   1   4,   0,
  <BLANKLINE>
  >>> alignments = aligner.align(seq1, reverse_complement(seq2),
strand="-")
  >>> len(alignments)
  1
  >>> print(alignments[0])
  AAAACCC
  --||||-
  --AACC-
  <BLANKLINE>
  >>> print(alignments[0].format("bed"))  #doctest:
+NORMALIZE_WHITESPACE
  target   2   6   query   4.0   -   2   6   0   1   4,   0,
  <BLANKLINE>
  >>> alignments = aligner.align(seq1, seq2, strand="-")
  >>> len(alignments)
  2
  >>> print(alignments[0])
  AAAACCC----
  -----------
  -------GGTT
  <BLANKLINE>
  >>> print(alignments[1])
  ----AAAACCC
  -----------
  GGTT-------
  <BLANKLINE>
>>

  Note that the score for aligning seq2 to the reverse strand of seq1
may be different from the score for aligning the reverse complement of
seq2 to the forward strand of seq1 if the left and right gap scores are
different: 
<<>>> aligner.left_gap_score = -0.5
  >>> aligner.right_gap_score = -0.2
  >>> aligner.score(seq1, seq2)
  2.8
  >>> alignments = aligner.align(seq1, seq2)
  >>> len(alignments)
  1
  >>> print(alignments[0])
  AAAACCC
  --||||-
  --AACC-
  <BLANKLINE>
  >>> aligner.score(seq1, reverse_complement(seq2), strand="-")
  3.1
  >>> alignments = aligner.align(seq1, reverse_complement(seq2),
strand="-")
  >>> len(alignments)
  1
  >>> print(alignments[0])
  AAAACCC
  --||||-
  --AACC-
  <BLANKLINE>
>>



6.6.2.9 Examples
----------------

  Suppose you want to do a global pairwise alignment between the same
two hemoglobin sequences from above (HBA_HUMAN, HBB_HUMAN) stored in
alpha.faa and beta.faa:
<<>>> from Bio import Align
  >>> from Bio import SeqIO
  >>> seq1 = SeqIO.read("alpha.faa", "fasta")
  >>> seq2 = SeqIO.read("beta.faa", "fasta")
  >>> aligner = Align.PairwiseAligner()
  >>> score = aligner.score(seq1.seq, seq2.seq)
  >>> print(score)
  72.0
>>

  showing an alignment score of 72.0. To see the individual alignments,
do
<<>>> alignments = aligner.align(seq1.seq, seq2.seq)
>>
   In this example, the total number of optimal alignments is huge (more
than 4 X 10^37), and calling len(alignments) will raise an
OverflowError:
<<>>> len(alignments)
  Traceback (most recent call last):
  ...
  OverflowError: number of optimal alignments is larger than
9223372036854775807
>>
   Let's have a look at the first alignment:
<<>>> alignment = alignments[0]
>>

  The alignment object stores the alignment score, as well as the
alignment itself:
<<>>> print(alignment.score)
  72.0
  >>> print(alignment)  #doctest: +ELLIPSIS
  MV-LS-PAD--KTN--VK-AA-WGKV-----GAHAGEYGAEALE-RMFLSF----P-TTKTY--FPHF--
...
  ||-|--|----|----|--|--||||-----|---||--|--|--|--|------|-|------|--|--
...
  MVHL-TP--EEK--SAV-TA-LWGKVNVDEVG---GE--A--L-GR--L--LVVYPWT----QRF--FES
...
>>

  Better alignments are usually obtained by penalizing gaps: higher
costs for opening a gap and lower costs for extending an existing gap.
For amino acid sequences match scores are usually encoded in matrices
like PAM or BLOSUM. Thus, a more meaningful alignment for our example
can be obtained by using the BLOSUM62 matrix, together with a gap open
penalty of 10 and a gap extension penalty of 0.5:
<<>>> from Bio import Align
  >>> from Bio import SeqIO
  >>> from Bio.Align import substitution_matrices
  >>> seq1 = SeqIO.read("alpha.faa", "fasta")
  >>> seq2 = SeqIO.read("beta.faa", "fasta")
  >>> aligner = Align.PairwiseAligner()
  >>> aligner.open_gap_score = -10
  >>> aligner.extend_gap_score = -0.5
  >>> aligner.substitution_matrix =
substitution_matrices.load("BLOSUM62")
  >>> score = aligner.score(seq1.seq, seq2.seq)
  >>> print(score)
  292.5
  >>> alignments = aligner.align(seq1.seq, seq2.seq)
  >>> len(alignments)
  2
  >>> print(alignments[0].score)
  292.5
  >>> print(alignments[0])  #doctest: +ELLIPSIS
  MV-LSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKTYFPHF-DLS-----HGSAQVKGHGKKV
...
  ||-|.|..|..|.|.||||--...|.|.|||.|.....|.|...|..|-|||-----.|...||.|||||
...
  MVHLTPEEKSAVTALWGKV--NVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKVKAHGKKV
...
  <BLANKLINE>
>>

  This alignment has the same score that we obtained earlier with EMBOSS
needle using the same sequences and the same parameters.
  To perform a local alignment, set aligner.mode to 'local':
<<>>> aligner.mode = 'local'
  >>> aligner.open_gap_score = -10
  >>> aligner.extend_gap_score = -1
  >>> alignments = aligner.align("LSPADKTNVKAA", "PEEKSAV")
  >>> print(len(alignments))
  1
  >>> alignment = alignments[0]
  >>> print(alignment)
  LSPADKTNVKAA
    |..|..|
    PEEKSAV
  <BLANKLINE>
  >>> print(alignment.score)
  16.0
>>



6.6.2.10 Generalized pairwise alignments
----------------------------------------

  In most cases, PairwiseAligner is used to perform alignments of
sequences (strings or Seq objects) consisting of single-letter
nucleotides or amino acids. More generally, PairwiseAligner can also be
applied to lists or tuples of arbitrary objects. This section will
describe some examples of such generalized pairwise alignments.
Generalized pairwise alignments using a substitution matrix and alphabet

  Schneider et al. [] created a substitution matrix for aligning
three-nucleotide codons (see below in section ?? for more information).
This substitution matrix is associated with an alphabet consisting of
all three-letter codons:
<<>>> from Bio.Align import substitution_matrices
  >>> m = substitution_matrices.load("SCHNEIDER")
  >>> m.alphabet  #doctest: +ELLIPSIS
  ('AAA', 'AAC', 'AAG', 'AAT', 'ACA', 'ACC', 'ACG', 'ACT', ..., 'TTG',
'TTT')
>>
   We can use this matrix to align codon sequences to each other:
<<>>> from Bio import Align
  >>> aligner = Align.PairwiseAligner()
  >>> aligner.substitution_matrix = m
  >>> aligner.gap_score = -1.0
  >>> s1 = ('AAT', 'CTG', 'TTT', 'TTT')
  >>> s2 = ('AAT', 'TTA', 'TTT')
  >>> alignments = aligner.align(s1, s2)
  >>> len(alignments)
  2
  >>> print(alignments[0])
  AAT CTG TTT TTT
  ||| ... ||| ---
  AAT TTA TTT ---
  <BLANKLINE>
  >>> print(alignments[1])
  AAT CTG TTT TTT
  ||| ... --- |||
  AAT TTA --- TTT
  <BLANKLINE>
>>
   Note that aligning TTT to TTA, as in this example: 
<<AAT CTG TTT TTT
  ||| --- ... |||
  AAT --- TTA TTT
>>
   would get a much lower score:
<<>>> print(m['CTG', 'TTA'])
  7.6
  >>> print(m['TTT', 'TTA'])
  -0.3
>>
   presumably because CTG and TTA both code for leucine, while TTT codes
for phenylalanine. The three-letter codon substitution matrix also
reveals a preference among codons representing the same amino acid. For
example, TTA has a preference for CTG preferred compared to CTC, though
all three code for leucine:
<<>>> s1 = ('AAT', 'CTG', 'CTC', 'TTT')
  >>> s2 = ('AAT', 'TTA', 'TTT')
  >>> alignments = aligner.align(s1, s2)
  >>> len(alignments)
  1
  >>> print(alignments[0])
  AAT CTG CTC TTT
  ||| ... --- |||
  AAT TTA --- TTT
  <BLANKLINE>
  >>> print(m['CTC', 'TTA'])
  6.5
>>

Generalized pairwise alignments using match/mismatch scores and an
alphabet

  Using the three-letter amino acid symbols, the sequences above
translate to
<<>>> s1 = ('Asn', 'Leu', 'Leu', 'Phe')
  >>> s2 = ('Asn', 'Leu', 'Phe')
>>
   We can align these sequences directly to each other by using a
three-letter amino acid alphabet:
<<>>> from Bio import Align
  >>> aligner = Align.PairwiseAligner()
  >>> aligner.alphabet = ['Ala', 'Arg', 'Asn', 'Asp', 'Cys',
  ...                     'Gln', 'Glu', 'Gly', 'His', 'Ile',
  ...                     'Leu', 'Lys', 'Met', 'Phe', 'Pro',
  ...                     'Ser', 'Thr', 'Trp', 'Tyr', 'Val']
>>
   We use +6/-1 match and mismatch scores as an approximation of the
BLOSUM62 matrix, and align these sequences to each other:
<<>>> aligner.match = +6
  >>> aligner.mismatch = -1
  >>> alignments = aligner.align(s1, s2)
  >>> print(len(alignments))
  2
  >>> print(alignments[0])
  Asn Leu Leu Phe
  ||| ||| --- |||
  Asn Leu --- Phe
  <BLANKLINE>
  >>> print(alignments[1])
  Asn Leu Leu Phe
  ||| --- ||| |||
  Asn --- Leu Phe
  <BLANKLINE>
  >>> print(alignments.score)
  18.0
>>

Generalized pairwise alignments using match/mismatch scores and integer
sequences

  Internally, the first step when performing an alignment is to replace
the two sequences by integer arrays consisting of the indices of each
letter in each sequence in the alphabet associated with the aligner.
This step can be bypassed by passing integer arrays directly:
<<>>> import numpy
  >>> from Bio import Align
  >>> aligner = Align.PairwiseAligner()
  >>> s1 = numpy.array([2, 10, 10, 13], numpy.int32)
  >>> s2 = numpy.array([2, 10, 13], numpy.int32)
  >>> aligner.match = +6
  >>> aligner.mismatch = -1
  >>> alignments = aligner.align(s1, s2)
  >>> print(len(alignments))
  2
  >>> print(alignments[0])
  2 10 10 13
  | || -- ||
  2 10 -- 13
  <BLANKLINE>
  >>> print(alignments[1])
  2 10 10 13
  | -- || ||
  2 -- 10 13
  <BLANKLINE>
  >>> print(alignments.score)
  18.0
>>
   Note that the indices should consist of 32-bit integers, as specified
in this example by numpy.int32.
  Unknown letters can again be included by defining a wildcard
character, and using the corresponding Unicode code point number as the
index:
<<>>> aligner.wildcard = "?"
  >>> ord(aligner.wildcard)
  63
  >>> s2 = numpy.array([2, 63, 13], numpy.int32)
  >>> aligner.gap_score = -3
  >>> alignments = aligner.align(s1, s2)
  >>> print(len(alignments))
  2
  >>> print(alignments[0])
  2 10 10 13
  | .. -- ||
  2 63 -- 13
  <BLANKLINE>
  >>> print(alignments[1])
  2 10 10 13
  | -- .. ||
  2 -- 63 13
  <BLANKLINE>
  >>> print(alignments.score)
  9.0
>>

Generalized pairwise alignments using a substitution matrix and integer
sequences

  Integer sequences can also be aligned using a substitution matrix, in
this case a numpy square array without an alphabet associated with it.
In this case, all index values must be non-negative, and smaller than
the size of the substitution matrix:
<<>>> from Bio import Align
  >>> import numpy
  >>> aligner = Align.PairwiseAligner()
  >>> m = numpy.eye(5)
  >>> m[0, 1:] = m[1:, 0] = -2
  >>> m[2,2] = 3
  >>> print(m)
  [[ 1. -2. -2. -2. -2.]
   [-2.  1.  0.  0.  0.]
   [-2.  0.  3.  0.  0.]
   [-2.  0.  0.  1.  0.]
   [-2.  0.  0.  0.  1.]]
  >>> aligner.substitution_matrix = m
  >>> aligner.gap_score = -1
  >>> s1 = numpy.array([0, 2, 3, 4], numpy.int32)
  >>> s2 = numpy.array([0, 3, 2, 1], numpy.int32)
  >>> alignments = aligner.align(s1, s2)
  >>> print(len(alignments))
  2
  >>> print(alignments[0])
  0 - 2 3 4
  | - | . -
  0 3 2 1 -
  <BLANKLINE>
  >>> print(alignments[1])
  0 - 2 3 4
  | - | - .
  0 3 2 - 1
  <BLANKLINE>
  >>> print(alignments.score)
  2.0
>>



6.7 Substitution matrices
*=*=*=*=*=*=*=*=*=*=*=*=*


  The Array class in Bio.Align.substitution_matrices is a subclass of
numpy arrays that supports indexing both by integers and by specific
strings. An Array instance can either be a one-dimensional array or a
square two-dimensional arrays. A one-dimensional Array object can for
example be used to store the nucleotide frequency of a DNA sequence,
while a two-dimensional Array object can be used to represent a scoring
matrix for sequence alignments.


Creating an Array object
========================

  To create a one-dimensional Array, only the alphabet of allowed
letters needs to be specified:
<<>>> from Bio.Align.substitution_matrices import Array
  >>> counts = Array("ACGT")
  >>> print(counts)
  A 0.0
  C 0.0
  G 0.0
  T 0.0
  <BLANKLINE>
>>
   The allowed letters are stored in the alphabet property:
<<>>> counts.alphabet
  'ACGT'
>>
   This property is read-only; modifying the underlying _alphabet
attribute may lead to unexpected results. Elements can be accessed both
by letter and by integer index:
<<>>> counts['C'] = -3
  >>> counts[2] = 7
  >>> print(counts)
  A  0.0
  C -3.0
  G  7.0
  T  0.0
  <BLANKLINE>
  >>> counts[1]
  -3.0
>>

  Using a letter that is not in the alphabet, or an index that is out of
bounds, will cause a IndexError:
<<>>> counts['U']
  Traceback (most recent call last):
      ...
  IndexError: 'U'
  >>> counts['X'] = 6
  Traceback (most recent call last):
      ...
  IndexError: 'X'
  >>> counts[7]
  Traceback (most recent call last):
      ...
  IndexError: index 7 is out of bounds for axis 0 with size 4
>>

  A two-dimensional Array can be created by specifying dims=2:
<<>>> from Bio.Align.substitution_matrices import Array
  >>> counts = Array("ACGT", dims=2)
  >>> print(counts)
      A   C   G   T
  A 0.0 0.0 0.0 0.0
  C 0.0 0.0 0.0 0.0
  G 0.0 0.0 0.0 0.0
  T 0.0 0.0 0.0 0.0
  <BLANKLINE>
>>
   Again, both letters and integers can be used for indexing, and
specifying a letter that is not in the alphabet will cause an
IndexError:
<<>>> counts['A', 'C'] = 12.0
  >>> counts[2, 1] = 5.0
  >>> counts[3, 'T'] = -2
  >>> print(counts)
      A    C   G    T
  A 0.0 12.0 0.0  0.0
  C 0.0  0.0 0.0  0.0
  G 0.0  5.0 0.0  0.0
  T 0.0  0.0 0.0 -2.0
  <BLANKLINE>
  >>> counts['X', 1]
  Traceback (most recent call last):
      ...
  IndexError: 'X'
  >>> counts['A', 5]
  Traceback (most recent call last):
      ...
  IndexError: index 5 is out of bounds for axis 1 with size 4
>>
   Selecting a row or column from the two-dimensional array will return
a one-dimensional Array:
<<>>> counts = Array("ACGT", dims=2)
  >>> counts['A', 'C'] = 12.0
  >>> counts[2, 1] = 5.0
  >>> counts[3, 'T'] = -2
>>

<<>>> counts['G']
  Array([0., 5., 0., 0.],
        alphabet='ACGT')
  >>> counts[:, 'C']
  Array([12.,  0.,  5.,  0.],
        alphabet='ACGT')
>>

  Array objects can thus be used as an array and as a dictionary. They
can be converted to plain numpy arrays or plain dictionary objects:
<<>>> import numpy
  >>> x = Array("ACGT")
  >>> x['C'] = 5
>>

<<>>> x
  Array([0., 5., 0., 0.],
        alphabet='ACGT')
  >>> a = numpy.array(x)  # create a plain numpy array
  >>> a
  array([0., 5., 0., 0.])
  >>> d = dict(x)  # create a plain dictionary
  >>> d
  {'A': 0.0, 'C': 5.0, 'G': 0.0, 'T': 0.0}
>>

  While the alphabet of an Array is usually a string, you may also use a
tuple of (immutable) objects. This is used for example for a codon
substitution matrix, where the keys are not individual nucleotides or
amino acids but instead three-nucleotide codons.
  While the alphabet property of an Array is immutable, you can create a
new Array object by selecting the letters you are interested in from the
alphabet. For example, 
<<>>> a = Array("ABCD", dims=2, data=numpy.arange(16).reshape(4,4))
  >>> print(a)
       A    B    C    D
  A  0.0  1.0  2.0  3.0
  B  4.0  5.0  6.0  7.0
  C  8.0  9.0 10.0 11.0
  D 12.0 13.0 14.0 15.0
  <BLANKLINE>
  >>> b = a.select("CAD")
  >>> print(b)
       C    A    D
  C 10.0  8.0 11.0
  A  2.0  0.0  3.0
  D 14.0 12.0 15.0
  <BLANKLINE>
>>
   Note that this also allows you to reorder the alphabet.
  Data for letters that are not found in the alphabet are set to zero: 
<<>>> c = a.select("DEC")
  >>> print(c)
       D   E    C
  D 15.0 0.0 14.0
  E  0.0 0.0  0.0
  C 11.0 0.0 10.0
  <BLANKLINE>
>>



Calculating a substitution matrix from a pairwise sequence alignment
====================================================================

  As Array is a subclass of a numpy array, you can apply mathematical
operations on an Array object in much the same way. Here, we illustrate
this by calculating a scoring matrix from the alignment of the 16S
ribosomal RNA gene sequences of Escherichia coli and Bacillus subtilis.
First, we create a PairwiseAligner and initialize it with the default
scores used by blastn:
<<>>> from Bio.Align import PairwiseAligner
  >>> aligner = PairwiseAligner()
  >>> aligner.mode = 'local'
  >>> aligner.match_score = 2
  >>> aligner.mismatch_score = -3
  >>> aligner.open_gap_score = -7
  >>> aligner.extend_gap_score = -2
>>
   Next, we read in the 16S ribosomal RNA gene sequence of Escherichia
coli and Bacillus subtilis (provided in Tests/scoring_matrices/ecoli.fa
and Tests/scoring_matrices/bsubtilis.fa), and align them to each other:
<<>>> from Bio import SeqIO
  >>> sequence1 = SeqIO.read('ecoli.fa', 'fasta')
  >>> sequence2 = SeqIO.read('bsubtilis.fa', 'fasta')
  >>> alignments = aligner.align(sequence1.seq, sequence2.seq)
>>
   The number of alignments generated is very large:
<<>>> len(alignments)
  1990656
>>
   However, as they only differ trivially from each other, we
arbitrarily choose the first alignment, and count the number of each
substitution:
<<>>> alignment = alignments[0]
  >>> from Bio.Align.substitution_matrices import Array
  >>> frequency = Array("ACGT", dims=2)
  >>> for (start1, end1), (start2, end2) in zip(*alignment.aligned):
  ...     seq1 = sequence1[start1:end1]
  ...     seq2 = sequence2[start2:end2]
  ...     for c1, c2 in zip(seq1, seq2):
  ...         frequency[c1, c2] += 1
  ...
  >>> print(frequency)
        A     C     G     T
  A 307.0  19.0  34.0  19.0
  C  15.0 280.0  25.0  29.0
  G  34.0  24.0 401.0  20.0
  T  24.0  36.0  20.0 228.0
  <BLANKLINE>
>>
   We normalize against the total number to find the probability of each
substitution, and create a symmetric matrix:
<<>>> import numpy
  >>> probabilities = frequency / numpy.sum(frequency)
  >>> probabilities = (probabilities + probabilities.transpose()) / 2.0
  >>> print(probabilities.format("%.4f"))
         A      C      G      T
  A 0.2026 0.0112 0.0224 0.0142
  C 0.0112 0.1848 0.0162 0.0215
  G 0.0224 0.0162 0.2647 0.0132
  T 0.0142 0.0215 0.0132 0.1505
  <BLANKLINE>
>>
   The background probability is the probability of finding an A, C, G,
or T nucleotide in each sequence separately. This can be calculated as
the sum of each row or column:
<<>>> background = numpy.sum(probabilities, 0)
  >>> print(background.format("%.4f"))
  A 0.2505
  C 0.2337
  G 0.3165
  T 0.1993
  <BLANKLINE>
>>
   The number of substitutions expected at random is simply the product
of the background distribution with itself:
<<>>> expected = numpy.dot(background[:,None], background[None, :])
  >>> print(expected.format("%.4f"))
         A      C      G      T
  A 0.0627 0.0585 0.0793 0.0499
  C 0.0585 0.0546 0.0740 0.0466
  G 0.0793 0.0740 0.1002 0.0631
  T 0.0499 0.0466 0.0631 0.0397
  <BLANKLINE>
>>
   The scoring matrix can then be calculated as the logarithm of the
odds-ratio of the observed and the expected probabilities:
<<>>> oddsratios = probabilities / expected
  >>> scoring_matrix = numpy.log2(oddsratios)
  >>> print(scoring_matrix)
       A    C    G    T
  A  1.7 -2.4 -1.8 -1.8
  C -2.4  1.8 -2.2 -1.1
  G -1.8 -2.2  1.4 -2.3
  T -1.8 -1.1 -2.3  1.9
  <BLANKLINE>
>>
   The matrix can be used to set the substitution matrix for the
pairwise aligner:
<<>>> aligner.substitution_matrix = scoring_matrix
>>

  A ValueError is triggered if the Array objects appearing in a
mathematical operation have different alphabets:
<<>>> from Bio.Align.substitution_matrices import Array
  >>> d = Array("ACGT")
  >>> r = Array("ACGU")
  >>> d + r
  Traceback (most recent call last):
      ...
  ValueError: alphabets are inconsistent
>>



Reading Array object from file
==============================

  Bio.Align.substitution_matrices includes a parser to read one- and
two-dimensional Array objects from file. One-dimensional arrays are
represented by a simple two-column format, with the first column
containing the key and the second column the corresponding value. For
example, the file hg38.chrom.sizes (obtained from UCSC), available in
the Tests/Align subdirectory of the Biopython distribution, contains the
size in nucleotides of each chromosome in human genome assembly hg38: 
<<chr1    248956422
  chr2    242193529
  chr3    198295559
  chr4    190214555
  ...
  chrUn_KI270385v1    990
  chrUn_KI270423v1    981
  chrUn_KI270392v1    971
  chrUn_KI270394v1    970
>>
   To parse this file, use
<<>>> from Bio.Align import substitution_matrices
  >>> with open("hg38.chrom.sizes") as handle:
  ...    table = substitution_matrices.read(handle)
  ...
  >>> print(table)  #doctest: +ELLIPSIS
  chr1 248956422.0
  chr2 242193529.0
  chr3 198295559.0
  chr4 190214555.0
  ...
  chrUn_KI270423v1       981.0
  chrUn_KI270392v1       971.0
  chrUn_KI270394v1       970.0
  <BLANKLINE>
>>
   Use dtype=int to read the values as integers:
<<>>> with open("hg38.chrom.sizes") as handle:
  ...    table = substitution_matrices.read(handle, int)
  ...
  >>> print(table)  #doctest: +ELLIPSIS
  chr1 248956422
  chr2 242193529
  chr3 198295559
  chr4 190214555
  ...
  chrUn_KI270423v1       981
  chrUn_KI270392v1       971
  chrUn_KI270394v1       970
  <BLANKLINE>
>>

  For two-dimensional arrays, we follow the file format of substitution
matrices provided by NCBI. For example, the BLOSUM62 matrix, which is
the default substitution matrix for NCBI's protein-protein BLAST []
program blastp, is stored as follows: 
<<#  Matrix made by matblas from blosum62.iij
  #  * column uses minimum score
  #  BLOSUM Clustered Scoring Matrix in 1/2 Bit Units
  #  Blocks Database = /data/blocks_5.0/blocks.dat
  #  Cluster Percentage: >= 62
  #  Entropy =   0.6979, Expected =  -0.5209
     A  R  N  D  C  Q  E  G  H  I  L  K  M  F  P  S  T  W  Y  V  B  Z  X
 *
  A  4 -1 -2 -2  0 -1 -1  0 -2 -1 -1 -1 -1 -2 -1  1  0 -3 -2  0 -2 -1  0
-4 
  R -1  5  0 -2 -3  1  0 -2  0 -3 -2  2 -1 -3 -2 -1 -1 -3 -2 -3 -1  0 -1
-4 
  N -2  0  6  1 -3  0  0  0  1 -3 -3  0 -2 -3 -2  1  0 -4 -2 -3  3  0 -1
-4 
  D -2 -2  1  6 -3  0  2 -1 -1 -3 -4 -1 -3 -3 -1  0 -1 -4 -3 -3  4  1 -1
-4 
  C  0 -3 -3 -3  9 -3 -4 -3 -3 -1 -1 -3 -1 -2 -3 -1 -1 -2 -2 -1 -3 -3 -2
-4 
  Q -1  1  0  0 -3  5  2 -2  0 -3 -2  1  0 -3 -1  0 -1 -2 -1 -2  0  3 -1
-4 
  E -1  0  0  2 -4  2  5 -2  0 -3 -3  1 -2 -3 -1  0 -1 -3 -2 -2  1  4 -1
-4 
  G  0 -2  0 -1 -3 -2 -2  6 -2 -4 -4 -2 -3 -3 -2  0 -2 -2 -3 -3 -1 -2 -1
-4 
  H -2  0  1 -1 -3  0  0 -2  8 -3 -3 -1 -2 -1 -2 -1 -2 -2  2 -3  0  0 -1
-4 
  ...
>>
   This file is included in the Biopython distribution under
Bio/Align/substitution_matrices/data. To parse this file, use
<<>>> from Bio.Align import substitution_matrices
  >>> with open("BLOSUM62") as handle:
  ...    matrix = substitution_matrices.read(handle)
  ...
  >>> print(matrix.alphabet)
  ARNDCQEGHILKMFPSTWYVBZX*
  >>> print(matrix['A','D'])
  -2.0
>>
   The header lines starting with # are stored in the attribute header:
<<>>> matrix.header[0]
  'Matrix made by matblas from blosum62.iij'
>>
   We can now use this matrix as the substitution matrix on an aligner
object:
<<>>> from Bio.Align import PairwiseAligner
  >>> aligner = PairwiseAligner()
  >>> aligner.substitution_matrix = matrix
>>
   To save an Array object, create a string first:
<<>>> text = str(matrix)
  >>> print(text)  #doctest: +ELLIPSIS
  #  Matrix made by matblas from blosum62.iij
  #  * column uses minimum score
  #  BLOSUM Clustered Scoring Matrix in 1/2 Bit Units
  #  Blocks Database = /data/blocks_5.0/blocks.dat
  #  Cluster Percentage: >= 62
  #  Entropy =   0.6979, Expected =  -0.5209
       A    R    N    D    C    Q    E    G    H    I    L    K    M   
F    P    S ...
  A  4.0 -1.0 -2.0 -2.0  0.0 -1.0 -1.0  0.0 -2.0 -1.0 -1.0 -1.0 -1.0
-2.0 -1.0  1.0 ...
  R -1.0  5.0  0.0 -2.0 -3.0  1.0  0.0 -2.0  0.0 -3.0 -2.0  2.0 -1.0
-3.0 -2.0 -1.0 ...
  N -2.0  0.0  6.0  1.0 -3.0  0.0  0.0  0.0  1.0 -3.0 -3.0  0.0 -2.0
-3.0 -2.0  1.0 ...
  D -2.0 -2.0  1.0  6.0 -3.0  0.0  2.0 -1.0 -1.0 -3.0 -4.0 -1.0 -3.0
-3.0 -1.0  0.0 ...
  C  0.0 -3.0 -3.0 -3.0  9.0 -3.0 -4.0 -3.0 -3.0 -1.0 -1.0 -3.0 -1.0
-2.0 -3.0 -1.0 ...
  ...
>>
   and write the text to a file.


Loading predefined substitution matrices
========================================

  Biopython contains a large set of substitution matrices defined in the
literature, including BLOSUM (Blocks Substitution Matrix) [] and PAM
(Point Accepted Mutation) matrices []. These matrices are available as
flat files in the Bio/Align/scoring_matrices/data directory, and can be
loaded into Python using the load function in the scoring_matrices
submodule. For example, the BLOSUM62 matrix can be loaded by running
<<>>> from Bio.Align import substitution_matrices
  >>> m = substitution_matrices.load("BLOSUM62")
>>
   This substitution matrix has an alphabet consisting of the 20 amino
acids used in the genetic code, the three ambiguous amino acids B
(asparagine or aspartic acid), Z (glutamine or glutamic acid), and X
(representing any amino acid), and the stop codon represented by an
asterisk:
<<>>> m.alphabet
  'ARNDCQEGHILKMFPSTWYVBZX*'
>>

  To get a full list of available substitution matrices, use load
without an argument:
<<>>> substitution_matrices.load()  #doctest: +ELLIPSIS
  ['BENNER22', 'BENNER6', 'BENNER74', 'BLOSUM45', 'BLOSUM50', ...,
'TRANS']
>>

  Note that the substitution matrix provided by Schneider et al. [] uses
an alphabet consisting of three-nucleotide codons:
<<>>> m = substitution_matrices.load("SCHNEIDER")
  >>> m.alphabet  #doctest: +ELLIPSIS
  ('AAA', 'AAC', 'AAG', 'AAT', 'ACA', 'ACC', 'ACG', 'ACT', ..., 'TTG',
'TTT')
>>

-----------------------------------


 (1) http://biopython.org/docs/1.79/api/Bio.AlignIO.html
 (2) http://emboss.sourceforge.net/
 (3) https://raw.githubusercontent.com/biopython/biopython/master/Doc/ex
   amples/opuntia.fasta
 (4) https://raw.githubusercontent.com/biopython/biopython/master/Doc/ex
   amples/opuntia.fasta
 (5) http://emboss.sourceforge.net/
 (6) http://emboss.sourceforge.net/
 (7) http://biopython.org/docs/1.77/api/Bio.pairwise2.html
 (8) http://biopython.org/docs/1.79/api/Bio.pairwise2.html


Chapter 7  BLAST
****************
    Hey, everybody loves BLAST right? I mean, geez, how can it get any
easier to do comparisons between one of your sequences and every other
sequence in the known world? But, of course, this section isn't about
how cool BLAST is, since we already know that. It is about the problem
with BLAST -- it can be really difficult to deal with the volume of data
generated by large runs, and to automate BLAST runs in general.
  Fortunately, the Biopython folks know this only too well, so they've
developed lots of tools for dealing with BLAST and making things much
easier. This section details how to use these tools and do useful things
with them.
  Dealing with BLAST can be split up into two steps, both of which can
be done from within Biopython. Firstly, running BLAST for your query
sequence(s), and getting some output. Secondly, parsing the BLAST output
in Python for further analysis.
  Your first introduction to running BLAST was probably via the NCBI
web-service. In fact, there are lots of ways you can run BLAST, which
can be categorised in several ways. The most important distinction is
running BLAST locally (on your own machine), and running BLAST remotely
(on another machine, typically the NCBI servers). We're going to start
this chapter by invoking the NCBI online BLAST service from within a
Python script.
  NOTE: The following Chapter ?? describes Bio.SearchIO, an experimental
module in Biopython. We intend this to ultimately replace the older
Bio.Blast module, as it provides a more general framework handling other
related sequence searching tools as well. However, until that is
declared stable, for production code please continue to use the
Bio.Blast module for dealing with NCBI BLAST.


7.1 Running BLAST over the Internet
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*


  We use the function qblast() in the Bio.Blast.NCBIWWW module to call
the online version of BLAST. This has three non-optional arguments: 

   - The first argument is the blast program to use for the search, as a
   lower case string. The options and descriptions of the programs are
   available at https://blast.ncbi.nlm.nih.gov/Blast.cgi. Currently
   qblast only works with blastn, blastp, blastx, tblast and tblastx. 
   - The second argument specifies the databases to search against.
   Again, the options for this are available on the NCBI Guide to BLAST 
   ftp://ftp.ncbi.nlm.nih.gov/pub/factsheets/HowTo_BLASTGuide.pdf. 
   - The third argument is a string containing your query sequence. This
   can either be the sequence itself, the sequence in fasta format, or
   an identifier like a GI number. 

  The qblast function also take a number of other option arguments which
are basically analogous to the different parameters you can set on the
BLAST web page. We'll just highlight a few of them here:


   - The argument url_base sets the base URL for running BLAST over the
   internet. By default it connects to the NCBI, but one can use this to
   connect to an instance of NCBI BLAST running in the cloud. Please
   refer to the documentation for the qblast function for further
   details. 
   - The qblast function can return the BLAST results in various
   formats, which you can choose with the optional format_type keyword:
   "HTML", "Text", "ASN.1", or "XML". The default is "XML", as that is
   the format expected by the parser, described in section ?? below. 
   - The argument expect sets the expectation or e-value threshold. 

  For more about the optional BLAST arguments, we refer you to the
NCBI's own documentation, or that built into Biopython:
<<>>> from Bio.Blast import NCBIWWW
  >>> help(NCBIWWW.qblast)
  ...
>>

  Note that the default settings on the NCBI BLAST website are not quite
the same as the defaults on QBLAST. If you get different results, you'll
need to check the parameters (e.g., the expectation value threshold and
the gap values).
  For example, if you have a nucleotide sequence you want to search
against the nucleotide database (nt) using BLASTN, and you know the GI
number of your query sequence, you can use:
<<>>> from Bio.Blast import NCBIWWW
  >>> result_handle = NCBIWWW.qblast("blastn", "nt", "8332116")
>>

  Alternatively, if we have our query sequence already in a FASTA
formatted file, we just need to open the file and read in this record as
a string, and use that as the query argument:
<<>>> from Bio.Blast import NCBIWWW
  >>> fasta_string = open("m_cold.fasta").read()
  >>> result_handle = NCBIWWW.qblast("blastn", "nt", fasta_string)
>>

  We could also have read in the FASTA file as a SeqRecord and then
supplied just the sequence itself:
<<>>> from Bio.Blast import NCBIWWW
  >>> from Bio import SeqIO
  >>> record = SeqIO.read("m_cold.fasta", format="fasta")
  >>> result_handle = NCBIWWW.qblast("blastn", "nt", record.seq)
>>

  Supplying just the sequence means that BLAST will assign an identifier
for your sequence automatically. You might prefer to use the SeqRecord
object's format method to make a FASTA string (which will include the
existing identifier):
<<>>> from Bio.Blast import NCBIWWW
  >>> from Bio import SeqIO
  >>> record = SeqIO.read("m_cold.fasta", format="fasta")
  >>> result_handle = NCBIWWW.qblast("blastn", "nt",
record.format("fasta"))
>>

  This approach makes more sense if you have your sequence(s) in a
non-FASTA file format which you can extract using Bio.SeqIO (see
Chapter ??).
  Whatever arguments you give the qblast() function, you should get back
your results in a handle object (by default in XML format). The next
step would be to parse the XML output into Python objects representing
the search results (Section ??), but you might want to save a local copy
of the output file first. I find this especially useful when debugging
my code that extracts info from the BLAST results (because re-running
the online search is slow and wastes the NCBI computer time).

  We need to be a bit careful since we can use result_handle.read() to
read the BLAST output only once -- calling result_handle.read() again
returns an empty string.
<<>>> with open("my_blast.xml", "w") as out_handle:
  ...     out_handle.write(result_handle.read())
  ...
  >>> result_handle.close()
>>

  After doing this, the results are in the file my_blast.xml and the
original handle has had all its data extracted (so we closed it).
However, the parse function of the BLAST parser (described in ??) takes
a file-handle-like object, so we can just open the saved file for input:
<<>>> result_handle = open("my_blast.xml")
>>

  Now that we've got the BLAST results back into a handle again, we are
ready to do something with them, so this leads us right into the parsing
section (see Section ?? below). You may want to jump ahead to that now
....


7.2 Running BLAST locally
*=*=*=*=*=*=*=*=*=*=*=*=*




7.2.1 Introduction
==================

  Running BLAST locally (as opposed to over the internet, see
Section ??) has at least major two advantages: 

   - Local BLAST may be faster than BLAST over the internet; 
   - Local BLAST allows you to make your own database to search for
   sequences against. 
   Dealing with proprietary or unpublished sequence data can be another
reason to run BLAST locally. You may not be allowed to redistribute the
sequences, so submitting them to the NCBI as a BLAST query would not be
an option.
  Unfortunately, there are some major drawbacks too -- installing all
the bits and getting it setup right takes some effort: 

   - Local BLAST requires command line tools to be installed. 
   - Local BLAST requires (large) BLAST databases to be setup (and
   potentially kept up to date). 

  To further confuse matters there are several different BLAST packages
available, and there are also other tools which can produce imitation
BLAST output files, such as BLAT.


7.2.2 Standalone NCBI BLAST+
============================

  The "new" NCBI BLAST+ (1) suite was released in 2009. This replaces
the old NCBI "legacy" BLAST package (see below).
  This section will show briefly how to use these tools from within
Python. If you have already read or tried the alignment tool examples in
Section ?? this should all seem quite straightforward. First, we
construct a command line string (as you would type in at the command
line prompt if running standalone BLAST by hand). Then we can execute
this command from within Python.
  For example, taking a FASTA file of gene nucleotide sequences, you
might want to run a BLASTX (translation) search against the
non-redundant (NR) protein database. Assuming you (or your systems
administrator) has downloaded and installed the NR database, you might
run:
<<$ blastx -query opuntia.fasta -db nr -out opuntia.xml -evalue 0.001
-outfmt 5
>>

  This should run BLASTX against the NR database, using an expectation
cut-off value of 0.001 and produce XML output to the specified file
(which we can then parse). On my computer this takes about six minutes -
a good reason to save the output to a file so you can repeat any
analysis as needed.
  From within Biopython we can use the NCBI BLASTX wrapper from the
Bio.Blast.Applications module to build the command line string, and run
it:
<<>>> from Bio.Blast.Applications import NcbiblastxCommandline
  >>> help(NcbiblastxCommandline)
  ...
  >>> blastx_cline = NcbiblastxCommandline(query="opuntia.fasta",
db="nr", evalue=0.001,
  ...                                      outfmt=5, out="opuntia.xml")
  >>> blastx_cline
  NcbiblastxCommandline(cmd='blastx', out='opuntia.xml', outfmt=5,
query='opuntia.fasta',
  db='nr', evalue=0.001)
  >>> print(blastx_cline)
  blastx -out opuntia.xml -outfmt 5 -query opuntia.fasta -db nr -evalue
0.001
  >>> stdout, stderr = blastx_cline()
>>

  In this example there shouldn't be any output from BLASTX to the
terminal, so stdout and stderr should be empty. You may want to check
the output file opuntia.xml has been created.
  As you may recall from earlier examples in the tutorial, the
opuntia.fasta contains seven sequences, so the BLAST XML output should
contain multiple results. Therefore use Bio.Blast.NCBIXML.parse() to
parse it as described below in Section ??.
  WARNING:	We have decided to drop these command line wrappers in a
future Biopython release. We will be updating this documentation to
instead build the command line directly, and invoke it with the
subprocess module.


7.2.3 Other versions of BLAST
=============================

  NCBI BLAST+ (written in C++) was first released in 2009 as a
replacement for the original NCBI "legacy" BLAST (written in C) which is
no longer being updated. There were a lot of changes -- the old version
had a single core command line tool blastall which covered multiple
different BLAST search types (which are now separate commands in
BLAST+), and all the command line options were renamed. Biopython's
wrappers for the NCBI "legacy" BLAST tools have been deprecated and will
be removed in a future release. To try to avoid confusion, we do not
cover calling these old tools from Biopython in this tutorial.
  You may also come across Washington University BLAST (2) (WU-BLAST),
and its successor, Advanced Biocomputing BLAST (3) (AB-BLAST, released
in 2009, not free/open source). These packages include the command line
tools wu-blastall and ab-blastall, which mimicked blastall from the NCBI
"legacy" BLAST suite. Biopython does not currently provide wrappers for
calling these tools, but should be able to parse any NCBI compatible
output from them.


7.3 Parsing BLAST output
*=*=*=*=*=*=*=*=*=*=*=*=


  As mentioned above, BLAST can generate output in various formats, such
as XML, HTML, and plain text. Originally, Biopython had parsers for
BLAST plain text and HTML output, as these were the only output formats
offered at the time. Unfortunately, the BLAST output in these formats
kept changing, each time breaking the Biopython parsers. Our HTML BLAST
parser has been removed, while the deprectaed plain text BLAST parser is
now only available via Bio.SearchIO. Use it at your own risk, it may or
may not work, depending on which BLAST version you're using.
  As keeping up with changes in BLAST became a hopeless endeavor,
especially with users running different BLAST versions, we now recommend
to parse the output in XML format, which can be generated by recent
versions of BLAST. Not only is the XML output more stable than the plain
text and HTML output, it is also much easier to parse automatically,
making Biopython a whole lot more stable.
  You can get BLAST output in XML format in various ways. For the
parser, it doesn't matter how the output was generated, as long as it is
in the XML format. 

   - You can use Biopython to run BLAST over the internet, as described
   in section ??. 
   - You can use Biopython to run BLAST locally, as described in
   section ??. 
   - You can do the BLAST search yourself on the NCBI site through your
   web browser, and then save the results. You need to choose XML as the
   format in which to receive the results, and save the final BLAST page
   you get (you know, the one with all of the interesting results!) to a
   file. 
   - You can also run BLAST locally without using Biopython, and save
   the output in a file. Again, you need to choose XML as the format in
   which to receive the results. 
   The important point is that you do not have to use Biopython scripts
to fetch the data in order to be able to parse it. Doing things in one
of these ways, you then need to get a handle to the results. In Python,
a handle is just a nice general way of describing input to any info
source so that the info can be retrieved using read() and readline()
functions (see Section ??).
  If you followed the code above for interacting with BLAST through a
script, then you already have result_handle, the handle to the BLAST
results. For example, using a GI number to do an online search:
<<>>> from Bio.Blast import NCBIWWW
  >>> result_handle = NCBIWWW.qblast("blastn", "nt", "8332116")
>>

  If instead you ran BLAST some other way, and have the BLAST output (in
XML format) in the file my_blast.xml, all you need to do is to open the
file for reading:
<<>>> result_handle = open("my_blast.xml")
>>

  Now that we've got a handle, we are ready to parse the output. The
code to parse it is really quite small. If you expect a single BLAST
result (i.e., you used a single query):
<<>>> from Bio.Blast import NCBIXML
  >>> blast_record = NCBIXML.read(result_handle)
>>

  or, if you have lots of results (i.e., multiple query sequences):
<<>>> from Bio.Blast import NCBIXML
  >>> blast_records = NCBIXML.parse(result_handle)
>>

  Just like Bio.SeqIO and Bio.AlignIO (see Chapters ?? and ??), we have
a pair of input functions, read and parse, where read is for when you
have exactly one object, and parse is an iterator for when you can have
lots of objects -- but instead of getting SeqRecord or
MultipleSeqAlignment objects, we get BLAST record objects.
  To be able to handle the situation where the BLAST file may be huge,
containing thousands of results, NCBIXML.parse() returns an iterator. In
plain English, an iterator allows you to step through the BLAST output,
retrieving BLAST records one by one for each BLAST search result:
<<>>> from Bio.Blast import NCBIXML
  >>> blast_records = NCBIXML.parse(result_handle)
  >>> blast_record = next(blast_records)
  # ... do something with blast_record
  >>> blast_record = next(blast_records)
  # ... do something with blast_record
  >>> blast_record = next(blast_records)
  # ... do something with blast_record
  >>> blast_record = next(blast_records)
  Traceback (most recent call last):
    File "<stdin>", line 1, in <module>
  StopIteration
  # No further records
>>

  Or, you can use a for-loop: 
<<>>> for blast_record in blast_records:
  ...     # Do something with blast_record
>>

  Note though that you can step through the BLAST records only once.
Usually, from each BLAST record you would save the information that you
are interested in. If you want to save all returned BLAST records, you
can convert the iterator into a list: 
<<>>> blast_records = list(blast_records)
>>
   Now you can access each BLAST record in the list with an index as
usual. If your BLAST file is huge though, you may run into memory
problems trying to save them all in a list.
  Usually, you'll be running one BLAST search at a time. Then, all you
need to do is to pick up the first (and only) BLAST record in
blast_records: 
<<>>> from Bio.Blast import NCBIXML
  >>> blast_records = NCBIXML.parse(result_handle)
  >>> blast_record = next(blast_records)
>>
   or more elegantly: 
<<>>> from Bio.Blast import NCBIXML
  >>> blast_record = NCBIXML.read(result_handle)
>>

  I guess by now you're wondering what is in a BLAST record.


7.4 The BLAST record class
*=*=*=*=*=*=*=*=*=*=*=*=*=


  A BLAST Record contains everything you might ever want to extract from
the BLAST output. Right now we'll just show an example of how to get
some info out of the BLAST report, but if you want something in
particular that is not described here, look at the info on the record
class in detail, and take a gander into the code or automatically
generated documentation -- the docstrings have lots of good info about
what is stored in each piece of information.
  To continue with our example, let's just print out some summary info
about all hits in our blast report greater than a particular threshold.
The following code does this:
<<>>> E_VALUE_THRESH = 0.04

  >>> for alignment in blast_record.alignments:
  ...     for hsp in alignment.hsps:
  ...         if hsp.expect < E_VALUE_THRESH:
  ...             print("****Alignment****")
  ...             print("sequence:", alignment.title)
  ...             print("length:", alignment.length)
  ...             print("e value:", hsp.expect)
  ...             print(hsp.query[0:75] + "...")
  ...             print(hsp.match[0:75] + "...")
  ...             print(hsp.sbjct[0:75] + "...")
>>

  This will print out summary reports like the following:
<<****Alignment****
  sequence: >gb|AF283004.1|AF283004 Arabidopsis thaliana cold
acclimation protein WCOR413-like protein
  alpha form mRNA, complete cds
  length: 783
  e value: 0.034
  tacttgttgatattggatcgaacaaactggagaaccaacatgctcacgtcacttttagtcccttacatat
tcctc...
  ||||||||| | ||||||||||| || ||||  || || |||||||| |||||| |  | ||||||||
||| ||...
  tacttgttggtgttggatcgaaccaattggaagacgaatatgctcacatcacttctcattccttacatct
tcttc...
>>

  Basically, you can do anything you want to with the info in the BLAST
report once you have parsed it. This will, of course, depend on what you
want to use it for, but hopefully this helps you get started on doing
what you need to do!
  An important consideration for extracting information from a BLAST
report is the type of objects that the information is stored in. In
Biopython, the parsers return Record objects, either Blast or PSIBlast
depending on what you are parsing. These objects are defined in
Bio.Blast.Record and are quite complete.
  Here are my attempts at UML class diagrams for the Blast and PSIBlast
record classes. If you are good at UML and see mistakes/improvements
that can be made, please let me know. The Blast class diagram is shown
in Figure ??.
    *images/BlastRecord.png* 

  The PSIBlast record object is similar, but has support for the rounds
that are used in the iteration steps of PSIBlast. The class diagram for
PSIBlast is shown in Figure ??.
    *images/PSIBlastRecord.png* 



7.5 Dealing with PSI-BLAST
*=*=*=*=*=*=*=*=*=*=*=*=*=


  You can run the standalone version of PSI-BLAST (the legacy NCBI
command line tool blastpgp, or its replacement psiblast) using the
wrappers in Bio.Blast.Applications module.
  At the time of writing, the NCBI do not appear to support tools
running a PSI-BLAST search via the internet.
  Note that the Bio.Blast.NCBIXML parser can read the XML output from
current versions of PSI-BLAST, but information like which sequences in
each iteration is new or reused isn't present in the XML file.


7.6 Dealing with RPS-BLAST
*=*=*=*=*=*=*=*=*=*=*=*=*=


  You can run the standalone version of RPS-BLAST (either the legacy
NCBI command line tool rpsblast, or its replacement with the same name)
using the wrappers in Bio.Blast.Applications module.
  At the time of writing, the NCBI do not appear to support tools
running an RPS-BLAST search via the internet.
  You can use the Bio.Blast.NCBIXML parser to read the XML output from
current versions of RPS-BLAST.   
-----------------------------------


 (1) https://blast.ncbi.nlm.nih.gov/Blast.cgi?CMD=WebPAGE_TYPE=BlastDocs
   DOC_TYPE=Download
 (2) http://blast.wustl.edu/
 (3) https://blast.advbiocomp.com


Chapter 8  BLAST and other sequence search tools
************************************************

  Biological sequence identification is an integral part of
bioinformatics. Several tools are available for this, each with their
own algorithms and approaches, such as BLAST (arguably the most
popular), FASTA, HMMER, and many more. In general, these tools usually
use your sequence to search a database of potential matches. With the
growing number of known sequences (hence the growing number of potential
matches), interpreting the results becomes increasingly hard as there
could be hundreds or even thousands of potential matches. Naturally,
manual interpretation of these searches' results is out of the question.
Moreover, you often need to work with several sequence search tools,
each with its own statistics, conventions, and output format. Imagine
how daunting it would be when you need to work with multiple sequences
using multiple search tools.
  We know this too well ourselves, which is why we created the
Bio.SearchIO submodule in Biopython. Bio.SearchIO allows you to extract
information from your search results in a convenient way, while also
dealing with the different standards and conventions used by different
search tools. The name SearchIO is a homage to BioPerl's module of the
same name.
  In this chapter, we'll go through the main features of Bio.SearchIO to
show what it can do for you. We'll use two popular search tools along
the way: BLAST and BLAT. They are used merely for illustrative purposes,
and you should be able to adapt the workflow to any other search tools
supported by Bio.SearchIO in a breeze. You're very welcome to follow
along with the search output files we'll be using. The BLAST output file
can be downloaded here (1), and the BLAT output file here (2) or are
included with the Biopython source code under the Doc/examples/ folder.
Both output files were generated using this sequence:
<<>mystery_seq
  CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG
>>

  The BLAST result is an XML file generated using blastn against the
NCBI refseq_rna database. For BLAT, the sequence database was the
February 2009 hg19 human genome draft and the output format is PSL.
  We'll start from an introduction to the Bio.SearchIO object model. The
model is the representation of your search results, thus it is core to
Bio.SearchIO itself. After that, we'll check out the main functions in
Bio.SearchIO that you may often use.
  Now that we're all set, let's go to the first step: introducing the
core object model.


8.1 The SearchIO object model
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*


  Despite the wildly differing output styles among many sequence search
tools, it turns out that their underlying concept is similar:


   - The output file may contain results from one or more search
   queries. 
   - In each search query, you will see one or more hits from the given
   search database. 
   - In each database hit, you will see one or more regions containing
   the actual sequence alignment between your query sequence and the
   database sequence. 
   - Some programs like BLAT or Exonerate may further split these
   regions into several alignment fragments (or blocks in BLAT and
   possibly exons in exonerate). This is not something you always see,
   as programs like BLAST and HMMER do not do this. 

  Realizing this generality, we decided use it as base for creating the
Bio.SearchIO object model. The object model consists of a nested
hierarchy of Python objects, each one representing one concept outlined
above. These objects are:


   - QueryResult, to represent a single search query. 
   - Hit, to represent a single database hit. Hit objects are contained
   within QueryResult and in each QueryResult there is zero or more Hit
   objects. 
   - HSP (short for high-scoring pair), to represent region(s) of
   significant alignments between query and hit sequences. HSP objects
   are contained within Hit objects and each Hit has one or more HSP
   objects. 
   - HSPFragment, to represent a single contiguous alignment between
   query and hit sequences. HSPFragment objects are contained within HSP
   objects. Most sequence search tools like BLAST and HMMER unify HSP
   and HSPFragment objects as each HSP will only have a single
   HSPFragment. However there are tools like BLAT and Exonerate that
   produce HSP containing multiple HSPFragment. Don't worry if this
   seems a tad confusing now, we'll elaborate more on these two objects
   later on. 

  These four objects are the ones you will interact with when you use
Bio.SearchIO. They are created using one of the main Bio.SearchIO
methods: read, parse, index, or index_db. The details of these methods
are provided in later sections. For this section, we'll only be using
read and parse. These functions behave similarly to their Bio.SeqIO and
Bio.AlignIO counterparts:


   - read is used for search output files with a single query and
   returns a QueryResult object 
   - parse is used for search output files with multiple queries and
   returns a generator that yields QueryResult objects 

  With that settled, let's start probing each Bio.SearchIO object,
beginning with QueryResult.


8.1.1 QueryResult
=================

  The QueryResult object represents a single search query and contains
zero or more Hit objects. Let's see what it looks like using the BLAST
file we have:
<<>>> from Bio import SearchIO
  >>> blast_qresult = SearchIO.read("my_blast.xml", "blast-xml")
  >>> print(blast_qresult)
  Program: blastn (2.2.27+)
    Query: 42291 (61)
           mystery_seq
   Target: refseq_rna
     Hits: ----  ----- 
----------------------------------------------------------
              #  # HSP  ID + description
           ----  ----- 
----------------------------------------------------------
              0      1  gi|262205317|ref|NR_030195.1|  Homo sapiens
microRNA 52...
              1      1  gi|301171311|ref|NR_035856.1|  Pan troglodytes
microRNA...
              2      1  gi|270133242|ref|NR_032573.1|  Macaca mulatta
microRNA ...
              3      2  gi|301171322|ref|NR_035857.1|  Pan troglodytes
microRNA...
              4      1  gi|301171267|ref|NR_035851.1|  Pan troglodytes
microRNA...
              5      2  gi|262205330|ref|NR_030198.1|  Homo sapiens
microRNA 52...
              6      1  gi|262205302|ref|NR_030191.1|  Homo sapiens
microRNA 51...
              7      1  gi|301171259|ref|NR_035850.1|  Pan troglodytes
microRNA...
              8      1  gi|262205451|ref|NR_030222.1|  Homo sapiens
microRNA 51...
              9      2  gi|301171447|ref|NR_035871.1|  Pan troglodytes
microRNA...
             10      1  gi|301171276|ref|NR_035852.1|  Pan troglodytes
microRNA...
             11      1  gi|262205290|ref|NR_030188.1|  Homo sapiens
microRNA 51...
             12      1  gi|301171354|ref|NR_035860.1|  Pan troglodytes
microRNA...
             13      1  gi|262205281|ref|NR_030186.1|  Homo sapiens
microRNA 52...
             14      2  gi|262205298|ref|NR_030190.1|  Homo sapiens
microRNA 52...
             15      1  gi|301171394|ref|NR_035865.1|  Pan troglodytes
microRNA...
             16      1  gi|262205429|ref|NR_030218.1|  Homo sapiens
microRNA 51...
             17      1  gi|262205423|ref|NR_030217.1|  Homo sapiens
microRNA 52...
             18      1  gi|301171401|ref|NR_035866.1|  Pan troglodytes
microRNA...
             19      1  gi|270133247|ref|NR_032574.1|  Macaca mulatta
microRNA ...
             20      1  gi|262205309|ref|NR_030193.1|  Homo sapiens
microRNA 52...
             21      2  gi|270132717|ref|NR_032716.1|  Macaca mulatta
microRNA ...
             22      2  gi|301171437|ref|NR_035870.1|  Pan troglodytes
microRNA...
             23      2  gi|270133306|ref|NR_032587.1|  Macaca mulatta
microRNA ...
             24      2  gi|301171428|ref|NR_035869.1|  Pan troglodytes
microRNA...
             25      1  gi|301171211|ref|NR_035845.1|  Pan troglodytes
microRNA...
             26      2  gi|301171153|ref|NR_035838.1|  Pan troglodytes
microRNA...
             27      2  gi|301171146|ref|NR_035837.1|  Pan troglodytes
microRNA...
             28      2  gi|270133254|ref|NR_032575.1|  Macaca mulatta
microRNA ...
             29      2  gi|262205445|ref|NR_030221.1|  Homo sapiens
microRNA 51...
             ~~~
             97      1  gi|356517317|ref|XM_003527287.1|  PREDICTED:
Glycine ma...
             98      1  gi|297814701|ref|XM_002875188.1|  Arabidopsis
lyrata su...
             99      1  gi|397513516|ref|XM_003827011.1|  PREDICTED: Pan
panisc...
>>

  We've just begun to scratch the surface of the object model, but you
can see that there's already some useful information. By invoking print
on the QueryResult object, you can see:


   - The program name and version (blastn version 2.2.27+) 
   - The query ID, description, and its sequence length (ID is 42291,
   description is `mystery_seq', and it is 61 nucleotides long) 
   - The target database to search against (refseq_rna) 
   - A quick overview of the resulting hits. For our query sequence,
   there are 100 potential hits (numbered 0--99 in the table). For each
   hit, we can also see how many HSPs it contains, its ID, and a snippet
   of its description. Notice here that Bio.SearchIO truncates the hit
   table overview, by showing only hits numbered 0--29, and then 97--99.


  Now let's check our BLAT results using the same procedure as above:
<<>>> blat_qresult = SearchIO.read("my_blat.psl", "blat-psl")
  >>> print(blat_qresult)
  Program: blat (<unknown version>)
    Query: mystery_seq (61)
           <unknown description>
   Target: <unknown target>
     Hits: ----  ----- 
----------------------------------------------------------
              #  # HSP  ID + description
           ----  ----- 
----------------------------------------------------------
              0     17  chr19  <unknown description>
>>

  You'll immediately notice that there are some differences. Some of
these are caused by the way PSL format stores its details, as you'll
see. The rest are caused by the genuine program and target database
differences between our BLAST and BLAT searches:


   - The program name and version. Bio.SearchIO knows that the program
   is BLAT, but in the output file there is no information regarding the
   program version so it defaults to `<unknown version>'. 
   - The query ID, description, and its sequence length. Notice here
   that these details are slightly different from the ones we saw in
   BLAST. The ID is `mystery_seq' instead of 42991, there is no known
   description, but the query length is still 61. This is actually a
   difference introduced by the file formats themselves. BLAST sometimes
   creates its own query IDs and uses your original ID as the sequence
   description. 
   - The target database is not known, as it is not stated in the BLAT
   output file. 
   - And finally, the list of hits we have is completely different.
   Here, we see that our query sequence only hits the `chr19' database
   entry, but in it we see 17 HSP regions. This should not be surprising
   however, given that we are using a different program, each with its
   own target database. 

  All the details you saw when invoking the print method can be accessed
individually using Python's object attribute access notation (a.k.a. the
dot notation). There are also other format-specific attributes that you
can access using the same method.
<<>>> print("%s %s" % (blast_qresult.program, blast_qresult.version))
  blastn 2.2.27+
  >>> print("%s %s" % (blat_qresult.program, blat_qresult.version))
  blat <unknown version>
  >>> blast_qresult.param_evalue_threshold    # blast-xml specific
  10.0
>>

  For a complete list of accessible attributes, you can check each
format-specific documentation. Here are the ones for BLAST (3) and for
BLAT (4).
  Having looked at using print on QueryResult objects, let's drill down
deeper. What exactly is a QueryResult? In terms of Python objects,
QueryResult is a hybrid between a list and a dictionary. In other words,
it is a container object with all the convenient features of lists and
dictionaries.
  Like Python lists and dictionaries, QueryResult objects are iterable.
Each iteration returns a Hit object:
<<>>> for hit in blast_qresult:
  ...     hit
  Hit(id='gi|262205317|ref|NR_030195.1|', query_id='42291', 1 hsps)
  Hit(id='gi|301171311|ref|NR_035856.1|', query_id='42291', 1 hsps)
  Hit(id='gi|270133242|ref|NR_032573.1|', query_id='42291', 1 hsps)
  Hit(id='gi|301171322|ref|NR_035857.1|', query_id='42291', 2 hsps)
  Hit(id='gi|301171267|ref|NR_035851.1|', query_id='42291', 1 hsps)
  ...
>>

  To check how many items (hits) a QueryResult has, you can simply
invoke Python's len method:
<<>>> len(blast_qresult)
  100
  >>> len(blat_qresult)
  1
>>

  Like Python lists, you can retrieve items (hits) from a QueryResult
using the slice notation:
<<>>> blast_qresult[0]        # retrieves the top hit
  Hit(id='gi|262205317|ref|NR_030195.1|', query_id='42291', 1 hsps)
  >>> blast_qresult[-1]       # retrieves the last hit
  Hit(id='gi|397513516|ref|XM_003827011.1|', query_id='42291', 1 hsps)
>>

  To retrieve multiple hits, you can slice QueryResult objects using the
slice notation as well. In this case, the slice will return a new
QueryResult object containing only the sliced hits:
<<>>> blast_slice = blast_qresult[:3]     # slices the first three hits
  >>> print(blast_slice)
  Program: blastn (2.2.27+)
    Query: 42291 (61)
           mystery_seq
   Target: refseq_rna
     Hits: ----  ----- 
----------------------------------------------------------
              #  # HSP  ID + description
           ----  ----- 
----------------------------------------------------------
              0      1  gi|262205317|ref|NR_030195.1|  Homo sapiens
microRNA 52...
              1      1  gi|301171311|ref|NR_035856.1|  Pan troglodytes
microRNA...
              2      1  gi|270133242|ref|NR_032573.1|  Macaca mulatta
microRNA ...
>>

  Like Python dictionaries, you can also retrieve hits using the hit's
ID. This is particularly useful if you know a given hit ID exists within
a search query results:
<<>>> blast_qresult["gi|262205317|ref|NR_030195.1|"]
  Hit(id='gi|262205317|ref|NR_030195.1|', query_id='42291', 1 hsps)
>>

  You can also get a full list of Hit objects using hits and a full list
of Hit IDs using hit_keys:
<<>>> blast_qresult.hits
  [...]       # list of all hits
  >>> blast_qresult.hit_keys
  [...]       # list of all hit IDs
>>

  What if you just want to check whether a particular hit is present in
the query results? You can do a simple Python membership test using the
in keyword:
<<>>> "gi|262205317|ref|NR_030195.1|" in blast_qresult
  True
  >>> "gi|262205317|ref|NR_030194.1|" in blast_qresult
  False
>>

  Sometimes, knowing whether a hit is present is not enough; you also
want to know the rank of the hit. Here, the index method comes to the
rescue:
<<>>> blast_qresult.index("gi|301171437|ref|NR_035870.1|")
  22
>>

  Remember that we're using Python's indexing style here, which is
zero-based. This means our hit above is ranked at no. 23, not 22.
  Also, note that the hit rank you see here is based on the native hit
ordering present in the original search output file. Different search
tools may order these hits based on different criteria.
  If the native hit ordering doesn't suit your taste, you can use the
sort method of the QueryResult object. It is very similar to Python's
list.sort method, with the addition of an option to create a new sorted
QueryResult object or not.
  Here is an example of using QueryResult.sort to sort the hits based on
each hit's full sequence length. For this particular sort, we'll set the
in_place flag to False so that sorting will return a new QueryResult
object and leave our initial object unsorted. We'll also set the reverse
flag to True so that we sort in descending order.
<<>>> for hit in blast_qresult[:5]:   # id and sequence length of the
first five hits
  ...     print("%s %i" % (hit.id, hit.seq_len))
  ...
  gi|262205317|ref|NR_030195.1| 61
  gi|301171311|ref|NR_035856.1| 60
  gi|270133242|ref|NR_032573.1| 85
  gi|301171322|ref|NR_035857.1| 86
  gi|301171267|ref|NR_035851.1| 80

  >>> sort_key = lambda hit: hit.seq_len
  >>> sorted_qresult = blast_qresult.sort(key=sort_key, reverse=True,
in_place=False)
  >>> for hit in sorted_qresult[:5]:
  ...     print("%s %i" % (hit.id, hit.seq_len))
  ...
  gi|397513516|ref|XM_003827011.1| 6002
  gi|390332045|ref|XM_776818.2| 4082
  gi|390332043|ref|XM_003723358.1| 4079
  gi|356517317|ref|XM_003527287.1| 3251
  gi|356543101|ref|XM_003539954.1| 2936
>>

  The advantage of having the in_place flag here is that we're
preserving the native ordering, so we may use it again later. You should
note that this is not the default behavior of QueryResult.sort, however,
which is why we needed to set the in_place flag to True explicitly.
  At this point, you've known enough about QueryResult objects to make
it work for you. But before we go on to the next object in the
Bio.SearchIO model, let's take a look at two more sets of methods that
could make it even easier to work with QueryResult objects: the filter
and map methods.
  If you're familiar with Python's list comprehensions, generator
expressions or the built in filter and map functions, you'll know how
useful they are for working with list-like objects (if you're not, check
them out!). You can use these built in methods to manipulate QueryResult
objects, but you'll end up with regular Python lists and lose the
ability to do more interesting manipulations.
  That's why, QueryResult objects provide its own flavor of filter and
map methods. Analogous to filter, there are hit_filter and hsp_filter
methods. As their name implies, these methods filter its QueryResult
object either on its Hit objects or HSP objects. Similarly, analogous to
map, QueryResult objects also provide the hit_map and hsp_map methods.
These methods apply a given function to all hits or HSPs in a
QueryResult object, respectively.
  Let's see these methods in action, beginning with hit_filter. This
method accepts a callback function that checks whether a given Hit
object passes the condition you set or not. In other words, the function
must accept as its argument a single Hit object and returns True or
False.
  Here is an example of using hit_filter to filter out Hit objects that
only have one HSP:
<<>>> filter_func = lambda hit: len(hit.hsps) > 1     # the callback
function
  >>> len(blast_qresult)      # no. of hits before filtering
  100
  >>> filtered_qresult = blast_qresult.hit_filter(filter_func)
  >>> len(filtered_qresult)   # no. of hits after filtering
  37
  >>> for hit in filtered_qresult[:5]:    # quick check for the hit
lengths
  ...     print("%s %i" % (hit.id, len(hit.hsps)))
  gi|301171322|ref|NR_035857.1| 2
  gi|262205330|ref|NR_030198.1| 2
  gi|301171447|ref|NR_035871.1| 2
  gi|262205298|ref|NR_030190.1| 2
  gi|270132717|ref|NR_032716.1| 2
>>

  hsp_filter works the same as hit_filter, only instead of looking at
the Hit objects, it performs filtering on the HSP objects in each hits.
  As for the map methods, they too accept a callback function as their
arguments. However, instead of returning True or False, the callback
function must return the modified Hit or HSP object (depending on
whether you're using hit_map or hsp_map).
  Let's see an example where we're using hit_map to rename the hit IDs:
<<>>> def map_func(hit):
  ...     hit.id = hit.id.split("|")[3]   # renames
"gi|301171322|ref|NR_035857.1|" to "NR_035857.1"
  ...     return hit
  ...
  >>> mapped_qresult = blast_qresult.hit_map(map_func)
  >>> for hit in mapped_qresult[:5]:
  ...     print(hit.id)
  NR_030195.1
  NR_035856.1
  NR_032573.1
  NR_035857.1
  NR_035851.1
>>

  Again, hsp_map works the same as hit_map, but on HSP objects instead
of Hit objects.


8.1.2 Hit
=========

  Hit objects represent all query results from a single database entry.
They are the second-level container in the Bio.SearchIO object
hierarchy. You've seen that they are contained by QueryResult objects,
but they themselves contain HSP objects.
  Let's see what they look like, beginning with our BLAST search:
<<>>> from Bio import SearchIO
  >>> blast_qresult = SearchIO.read("my_blast.xml", "blast-xml")
  >>> blast_hit = blast_qresult[3]    # fourth hit from the query result
  >>> print(blast_hit)
  Query: 42291
         mystery_seq
    Hit: gi|301171322|ref|NR_035857.1| (86)
         Pan troglodytes microRNA mir-520c (MIR520C), microRNA
   HSPs: ----  --------  ---------  ------  --------------- 
---------------------
            #   E-value  Bit score    Span      Query range             
Hit range
         ----  --------  ---------  ------  --------------- 
---------------------
            0   8.9e-20     100.47      60           [1:61]             
  [13:73]
            1   3.3e-06      55.39      60           [0:60]             
  [13:73]
>>

  You see that we've got the essentials covered here:


   - The query ID and description is present. A hit is always tied to a
   query, so we want to keep track of the originating query as well.
   These values can be accessed from a hit using the query_id and
   query_description attributes. 
   - We also have the unique hit ID, description, and full sequence
   lengths. They can be accessed using id, description, and seq_len,
   respectively. 
   - Finally, there's a table containing quick information about the
   HSPs this hit contains. In each row, we've got the important HSP
   details listed: the HSP index, its e-value, its bit score, its span
   (the alignment length including gaps), its query coordinates, and its
   hit coordinates. 

  Now let's contrast this with the BLAT search. Remember that in the
BLAT search we had one hit with 17 HSPs.
<<>>> blat_qresult = SearchIO.read("my_blat.psl", "blat-psl")
  >>> blat_hit = blat_qresult[0]      # the only hit
  >>> print(blat_hit)
  Query: mystery_seq
         <unknown description>
    Hit: chr19 (59128983)
         <unknown description>
   HSPs: ----  --------  ---------  ------  --------------- 
---------------------
            #   E-value  Bit score    Span      Query range             
Hit range
         ----  --------  ---------  ------  --------------- 
---------------------
            0         ?          ?       ?           [0:61]   
[54204480:54204541]
            1         ?          ?       ?           [0:61]   
[54233104:54264463]
            2         ?          ?       ?           [0:61]   
[54254477:54260071]
            3         ?          ?       ?           [1:61]   
[54210720:54210780]
            4         ?          ?       ?           [0:60]   
[54198476:54198536]
            5         ?          ?       ?           [0:61]   
[54265610:54265671]
            6         ?          ?       ?           [0:61]   
[54238143:54240175]
            7         ?          ?       ?           [0:60]   
[54189735:54189795]
            8         ?          ?       ?           [0:61]   
[54185425:54185486]
            9         ?          ?       ?           [0:60]   
[54197657:54197717]
           10         ?          ?       ?           [0:61]   
[54255662:54255723]
           11         ?          ?       ?           [0:61]   
[54201651:54201712]
           12         ?          ?       ?           [8:60]   
[54206009:54206061]
           13         ?          ?       ?          [10:61]   
[54178987:54179038]
           14         ?          ?       ?           [8:61]   
[54212018:54212071]
           15         ?          ?       ?           [8:51]   
[54234278:54234321]
           16         ?          ?       ?           [8:61]   
[54238143:54238196]
>>

  Here, we've got a similar level of detail as with the BLAST hit we saw
earlier. There are some differences worth explaining, though:


   - The e-value and bit score column values. As BLAT HSPs do not have
   e-values and bit scores, the display defaults to `?'. 
   - What about the span column? The span values is meant to display the
   complete alignment length, which consists of all residues and any
   gaps that may be present. The PSL format do not have this information
   readily available and Bio.SearchIO does not attempt to try guess what
   it is, so we get a `?' similar to the e-value and bit score columns. 

  In terms of Python objects, Hit behaves almost the same as Python
lists, but contain HSP objects exclusively. If you're familiar with
lists, you should encounter no difficulties working with the Hit object.
  Just like Python lists, Hit objects are iterable, and each iteration
returns one HSP object it contains:
<<>>> for hsp in blast_hit:
  ...     hsp
  HSP(hit_id='gi|301171322|ref|NR_035857.1|', query_id='42291', 1
fragments)
  HSP(hit_id='gi|301171322|ref|NR_035857.1|', query_id='42291', 1
fragments)
>>

  You can invoke len on a Hit to see how many HSP objects it has:
<<>>> len(blast_hit)
  2
  >>> len(blat_hit)
  17
>>

  You can use the slice notation on Hit objects, whether to retrieve
single HSP or multiple HSP objects. Like QueryResult, if you slice for
multiple HSP, a new Hit object will be returned containing only the
sliced HSP objects:
<<>>> blat_hit[0]                 # retrieve single items
  HSP(hit_id='chr19', query_id='mystery_seq', 1 fragments)
  >>> sliced_hit = blat_hit[4:9]  # retrieve multiple items
  >>> len(sliced_hit)
  5
  >>> print(sliced_hit)
  Query: mystery_seq
         <unknown description>
    Hit: chr19 (59128983)
         <unknown description>
   HSPs: ----  --------  ---------  ------  --------------- 
---------------------
            #   E-value  Bit score    Span      Query range             
Hit range
         ----  --------  ---------  ------  --------------- 
---------------------
            0         ?          ?       ?           [0:60]   
[54198476:54198536]
            1         ?          ?       ?           [0:61]   
[54265610:54265671]
            2         ?          ?       ?           [0:61]   
[54238143:54240175]
            3         ?          ?       ?           [0:60]   
[54189735:54189795]
            4         ?          ?       ?           [0:61]   
[54185425:54185486]
>>

  You can also sort the HSP inside a Hit, using the exact same arguments
like the sort method you saw in the QueryResult object.
  Finally, there are also the filter and map methods you can use on Hit
objects. Unlike in the QueryResult object, Hit objects only have one
variant of filter (Hit.filter) and one variant of map (Hit.map). Both of
Hit.filter and Hit.map work on the HSP objects a Hit has.


8.1.3 HSP
=========

  HSP (high-scoring pair) represents region(s) in the hit sequence that
contains significant alignment(s) to the query sequence. It contains the
actual match between your query sequence and a database entry. As this
match is determined by the sequence search tool's algorithms, the HSP
object contains the bulk of the statistics computed by the search tool.
This also makes the distinction between HSP objects from different
search tools more apparent compared to the differences you've seen in
QueryResult or Hit objects.
  Let's see some examples from our BLAST and BLAT searches. We'll look
at the BLAST HSP first:
<<>>> from Bio import SearchIO
  >>> blast_qresult = SearchIO.read("my_blast.xml", "blast-xml")
  >>> blast_hsp = blast_qresult[0][0]    # first hit, first hsp
  >>> print(blast_hsp)
        Query: 42291 mystery_seq
          Hit: gi|262205317|ref|NR_030195.1| Homo sapiens microRNA 520b
(MIR520...
  Query range: [0:61] (1)
    Hit range: [0:61] (1)
  Quick stats: evalue 4.9e-23; bitscore 111.29
    Fragments: 1 (61 columns)
       Query -
CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG

|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
         Hit -
CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG
>>

  Just like QueryResult and Hit, invoking print on an HSP shows its
general details: 

   - There are the query and hit IDs and descriptions. We need these to
   identify our HSP. 
   - We've also got the matching range of the query and hit sequences.
   The slice notation we're using here is an indication that the range
   is displayed using Python's indexing style (zero-based, half open).
   The number inside the parenthesis denotes the strand. In this case,
   both sequences have the plus strand. 
   - Some quick statistics are available: the e-value and bitscore. 
   - There is information about the HSP fragments. Ignore this for now;
   it will be explained later on. 
   - And finally, we have the query and hit sequence alignment itself. 

  These details can be accessed on their own using the dot notation,
just like in QueryResult and Hit:
<<>>> blast_hsp.query_range
  (0, 61)
>>

<<>>> blast_hsp.evalue
  4.91307e-23
>>

  They're not the only attributes available, though. HSP objects come
with a default set of properties that makes it easy to probe their
various details. Here are some examples:
<<>>> blast_hsp.hit_start         # start coordinate of the hit sequence
  0
  >>> blast_hsp.query_span        # how many residues in the query
sequence
  61
  >>> blast_hsp.aln_span          # how long the alignment is
  61
>>

  Check out the HSP documentation (5) for a full list of these
predefined properties.
  Furthermore, each sequence search tool usually computes its own
statistics / details for its HSP objects. For example, an XML BLAST
search also outputs the number of gaps and identical residues. These
attributes can be accessed like so:
<<>>> blast_hsp.gap_num       # number of gaps
  0
  >>> blast_hsp.ident_num     # number of identical residues
  61
>>

  These details are format-specific; they may not be present in other
formats. To see which details are available for a given sequence search
tool, you should check the format's documentation in Bio.SearchIO.
Alternatively, you may also use .__dict__.keys() for a quick list of
what's available:
<<>>> blast_hsp.__dict__.keys()
  ['bitscore', 'evalue', 'ident_num', 'gap_num', 'bitscore_raw',
'pos_num', '_items']
>>

  Finally, you may have noticed that the query and hit attributes of our
HSP are not just regular strings:
<<>>> blast_hsp.query
  SeqRecord(seq=Seq('CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCC
TTT...GGG'), id='42291', name='aligned query sequence',
description='mystery_seq', dbxrefs=[])
  >>> blast_hsp.hit
  SeqRecord(seq=Seq('CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCC
TTT...GGG'), id='gi|262205317|ref|NR_030195.1|', name='aligned hit
sequence', description='Homo sapiens microRNA 520b (MIR520B), microRNA',
dbxrefs=[])
>>

  They are SeqRecord objects you saw earlier in Section ??! This means
that you can do all sorts of interesting things you can do with
SeqRecord objects on HSP.query and/or HSP.hit.
  It should not surprise you now that the HSP object has an alignment
property which is a MultipleSeqAlignment object:
<<>>> print(blast_hsp.aln)
  Alignment with 2 rows and 61 columns
  CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAG...GGG 42291
  CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAG...GGG
gi|262205317|ref|NR_030195.1|
>>

  Having probed the BLAST HSP, let's now take a look at HSPs from our
BLAT results for a different kind of HSP. As usual, we'll begin by
invoking print on it:
<<>>> blat_qresult = SearchIO.read("my_blat.psl", "blat-psl")
  >>> blat_hsp = blat_qresult[0][0]       # first hit, first hsp
  >>> print(blat_hsp)
        Query: mystery_seq <unknown description>
          Hit: chr19 <unknown description>
  Query range: [0:61] (1)
    Hit range: [54204480:54204541] (1)
  Quick stats: evalue ?; bitscore ?
    Fragments: 1 (? columns)
>>

  Some of the outputs you may have already guessed. We have the query
and hit IDs and descriptions and the sequence coordinates. Values for
evalue and bitscore is `?' as BLAT HSPs do not have these attributes.
But The biggest difference here is that you don't see any sequence
alignments displayed. If you look closer, PSL formats themselves do not
have any hit or query sequences, so Bio.SearchIO won't create any
sequence or alignment objects. What happens if you try to access
HSP.query, HSP.hit, or HSP.aln? You'll get the default values for these
attributes, which is None:
<<>>> blat_hsp.hit is None
  True
  >>> blat_hsp.query is None
  True
  >>> blat_hsp.aln is None
  True
>>

  This does not affect other attributes, though. For example, you can
still access the length of the query or hit alignment. Despite not
displaying any attributes, the PSL format still have this information so
Bio.SearchIO can extract them:
<<>>> blat_hsp.query_span     # length of query match
  61
  >>> blat_hsp.hit_span       # length of hit match
  61
>>

  Other format-specific attributes are still present as well:
<<>>> blat_hsp.score          # PSL score
  61
  >>> blat_hsp.mismatch_num   # the mismatch column
  0
>>

  So far so good? Things get more interesting when you look at another
`variant' of HSP present in our BLAT results. You might recall that in
BLAT searches, sometimes we get our results separated into `blocks'.
These blocks are essentially alignment fragments that may have some
intervening sequence between them.
  Let's take a look at a BLAT HSP that contains multiple blocks to see
how Bio.SearchIO deals with this:
<<>>> blat_hsp2 = blat_qresult[0][1]      # first hit, second hsp
  >>> print(blat_hsp2)
        Query: mystery_seq <unknown description>
          Hit: chr19 <unknown description>
  Query range: [0:61] (1)
    Hit range: [54233104:54264463] (1)
  Quick stats: evalue ?; bitscore ?
    Fragments: ---  --------------  ---------------------- 
----------------------
                 #            Span             Query range              
Hit range
               ---  --------------  ---------------------- 
----------------------
                 0               ?                  [0:18]    
[54233104:54233122]
                 1               ?                 [18:61]    
[54264420:54264463]
>>

  What's happening here? We still some essential details covered: the
IDs and descriptions, the coordinates, and the quick statistics are
similar to what you've seen before. But the fragments detail is all
different. Instead of showing `Fragments: 1', we now have a table with
two data rows.
  This is how Bio.SearchIO deals with HSPs having multiple fragments. As
mentioned before, an HSP alignment may be separated by intervening
sequences into fragments. The intervening sequences are not part of the
query-hit match, so they should not be considered part of query nor hit
sequence. However, they do affect how we deal with sequence coordinates,
so we can't ignore them.
  Take a look at the hit coordinate of the HSP above. In the Hit range:
field, we see that the coordinate is [54233104:54264463]. But looking at
the table rows, we see that not the entire region spanned by this
coordinate matches our query. Specifically, the intervening region spans
from 54233122 to 54264420.
  Why then, is the query coordinates seem to be contiguous, you ask?
This is perfectly fine. In this case it means that the query match is
contiguous (no intervening regions), while the hit match is not.
  All these attributes are accessible from the HSP directly, by the way:
<<>>> blat_hsp2.hit_range         # hit start and end coordinates of the
entire HSP
  (54233104, 54264463)
  >>> blat_hsp2.hit_range_all     # hit start and end coordinates of
each fragment
  [(54233104, 54233122), (54264420, 54264463)]
  >>> blat_hsp2.hit_span          # hit span of the entire HSP
  31359
  >>> blat_hsp2.hit_span_all      # hit span of each fragment
  [18, 43]
  >>> blat_hsp2.hit_inter_ranges  # start and end coordinates of
intervening regions in the hit sequence
  [(54233122, 54264420)]
  >>> blat_hsp2.hit_inter_spans   # span of intervening regions in the
hit sequence
  [31298]
>>

  Most of these attributes are not readily available from the PSL file
we have, but Bio.SearchIO calculates them for you on the fly when you
parse the PSL file. All it needs are the start and end coordinates of
each fragment.
  What about the query, hit, and aln attributes? If the HSP has multiple
fragments, you won't be able to use these attributes as they only fetch
single SeqRecord or MultipleSeqAlignment objects. However, you can use
their *_all counterparts: query_all, hit_all, and aln_all. These
properties will return a list containing SeqRecord or
MultipleSeqAlignment objects from each of the HSP fragment. There are
other attributes that behave similarly, i.e. they only work for HSPs
with one fragment. Check out the HSP documentation (6) for a full list.
  Finally, to check whether you have multiple fragments or not, you can
use the is_fragmented property like so:
<<>>> blat_hsp2.is_fragmented     # BLAT HSP with 2 fragments
  True
  >>> blat_hsp.is_fragmented      # BLAT HSP from earlier, with one
fragment
  False
>>

  Before we move on, you should also know that we can use the slice
notation on HSP objects, just like QueryResult or Hit objects. When you
use this notation, you'll get an HSPFragment object in return, the last
component of the object model.


8.1.4 HSPFragment
=================

  HSPFragment represents a single, contiguous match between the query
and hit sequences. You could consider it the core of the object model
and search result, since it is the presence of these fragments that
determine whether your search have results or not.
  In most cases, you don't have to deal with HSPFragment objects
directly since not that many sequence search tools fragment their HSPs.
When you do have to deal with them, what you should remember is that
HSPFragment objects were written with to be as compact as possible. In
most cases, they only contain attributes directly related to sequences:
strands, reading frames, molecule types, coordinates, the sequences
themselves, and their IDs and descriptions.
  These attributes are readily shown when you invoke print on an
HSPFragment. Here's an example, taken from our BLAST search:
<<>>> from Bio import SearchIO
  >>> blast_qresult = SearchIO.read("my_blast.xml", "blast-xml")
  >>> blast_frag = blast_qresult[0][0][0]    # first hit, first hsp,
first fragment
  >>> print(blast_frag)
        Query: 42291 mystery_seq
          Hit: gi|262205317|ref|NR_030195.1| Homo sapiens microRNA 520b
(MIR520...
  Query range: [0:61] (1)
    Hit range: [0:61] (1)
    Fragments: 1 (61 columns)
       Query -
CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG

|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
         Hit -
CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG
>>

  At this level, the BLAT fragment looks quite similar to the BLAST
fragment, save for the query and hit sequences which are not present:
<<>>> blat_qresult = SearchIO.read("my_blat.psl", "blat-psl")
  >>> blat_frag = blat_qresult[0][0][0]    # first hit, first hsp, first
fragment
  >>> print(blat_frag)
        Query: mystery_seq <unknown description>
          Hit: chr19 <unknown description>
  Query range: [0:61] (1)
    Hit range: [54204480:54204541] (1)
    Fragments: 1 (? columns)
>>

  In all cases, these attributes are accessible using our favorite dot
notation. Some examples:
<<>>> blast_frag.query_start      # query start coordinate
  0
  >>> blast_frag.hit_strand       # hit sequence strand
  1
  >>> blast_frag.hit              # hit sequence, as a SeqRecord object
  SeqRecord(seq=Seq('CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCC
TTT...GGG'), id='gi|262205317|ref|NR_030195.1|', name='aligned hit
sequence', description='Homo sapiens microRNA 520b (MIR520B), microRNA',
dbxrefs=[])
>>



8.2 A note about standards and conventions
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=


  Before we move on to the main functions, there is something you ought
to know about the standards Bio.SearchIO uses. If you've worked with
multiple sequence search tools, you might have had to deal with the many
different ways each program deals with things like sequence coordinates.
It might not have been a pleasant experience as these search tools
usually have their own standards. For example, one tools might use
one-based coordinates, while the other uses zero-based coordinates. Or,
one program might reverse the start and end coordinates if the strand is
minus, while others don't. In short, these often creates unnecessary
mess must be dealt with.
  We realize this problem ourselves and we intend to address it in
Bio.SearchIO. After all, one of the goals of Bio.SearchIO is to create a
common, easy to use interface to deal with various search output files.
This means creating standards that extend beyond the object model you
just saw.
  Now, you might complain, "Not another standard!". Well, eventually we
have to choose one convention or the other, so this is necessary. Plus,
we're not creating something entirely new here; just adopting a standard
we think is best for a Python programmer (it is Biopython, after all).
  There are three implicit standards that you can expect when working
with Bio.SearchIO:


   - The first one pertains to sequence coordinates. In Bio.SearchIO,
   all sequence coordinates follows Python's coordinate style:
   zero-based and half open. For example, if in a BLAST XML output file
   the start and end coordinates of an HSP are 10 and 28, they would
   become 9 and 28 in Bio.SearchIO. The start coordinate becomes 9
   because Python indices start from zero, while the end coordinate
   remains 28 as Python slices omit the last item in an interval. 
   - The second is on sequence coordinate orders. In Bio.SearchIO, start
   coordinates are always less than or equal to end coordinates. This
   isn't always the case with all sequence search tools, as some of them
   have larger start coordinates when the sequence strand is minus. 
   - The last one is on strand and reading frame values. For strands,
   there are only four valid choices: 1 (plus strand), -1 (minus
   strand), 0 (protein sequences), and None (no strand). For reading
   frames, the valid choices are integers from -3 to 3 and None. 

  Note that these standards only exist in Bio.SearchIO objects. If you
write Bio.SearchIO objects into an output format, Bio.SearchIO will use
the format's standard for the output. It does not force its standard
over to your output file.


8.3 Reading search output files
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*


  There are two functions you can use for reading search output files
into Bio.SearchIO objects: read and parse. They're essentially similar
to read and parse functions in other submodules like Bio.SeqIO or
Bio.AlignIO. In both cases, you need to supply the search output file
name and the file format name, both as Python strings. You can check the
documentation for a list of format names Bio.SearchIO recognizes.
  Bio.SearchIO.read is used for reading search output files with only
one query and returns a QueryResult object. You've seen read used in our
previous examples. What you haven't seen is that read may also accept
additional keyword arguments, depending on the file format.
  Here are some examples. In the first one, we use read just like
previously to read a BLAST tabular output file. In the second one, we
use a keyword argument to modify so it parses the BLAST tabular variant
with comments in it:
<<>>> from Bio import SearchIO
  >>> qresult = SearchIO.read("tab_2226_tblastn_003.txt", "blast-tab")
  >>> qresult
  QueryResult(id='gi|16080617|ref|NP_391444.1|', 3 hits)
  >>> qresult2 = SearchIO.read("tab_2226_tblastn_007.txt", "blast-tab",
comments=True)
  >>> qresult2
  QueryResult(id='gi|16080617|ref|NP_391444.1|', 3 hits)
>>

  These keyword arguments differs among file formats. Check the format
documentation to see if it has keyword arguments that modifies its
parser's behavior.
  As for the Bio.SearchIO.parse, it is used for reading search output
files with any number of queries. The function returns a generator
object that yields a QueryResult object in each iteration. Like
Bio.SearchIO.read, it also accepts format-specific keyword arguments:
<<>>> from Bio import SearchIO
  >>> qresults = SearchIO.parse("tab_2226_tblastn_001.txt", "blast-tab")
  >>> for qresult in qresults:
  ...     print(qresult.id)
  gi|16080617|ref|NP_391444.1|
  gi|11464971:4-101
  >>> qresults2 = SearchIO.parse("tab_2226_tblastn_005.txt",
"blast-tab", comments=True)
  >>> for qresult in qresults2:
  ...     print(qresult.id)
  random_s00
  gi|16080617|ref|NP_391444.1|
  gi|11464971:4-101
>>



8.4 Dealing with large search output files with indexing
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=


  Sometimes, you're handed a search output file containing hundreds or
thousands of queries that you need to parse. You can of course use
Bio.SearchIO.parse for this file, but that would be grossly inefficient
if you need to access only a few of the queries. This is because parse
will parse all queries it sees before it fetches your query of interest.
  In this case, the ideal choice would be to index the file using
Bio.SearchIO.index or Bio.SearchIO.index_db. If the names sound
familiar, it's because you've seen them before in Section ??. These
functions also behave similarly to their Bio.SeqIO counterparts, with
the addition of format-specific keyword arguments.
  Here are some examples. You can use index with just the filename and
format name:
<<>>> from Bio import SearchIO
  >>> idx = SearchIO.index("tab_2226_tblastn_001.txt", "blast-tab")
  >>> sorted(idx.keys())
  ['gi|11464971:4-101', 'gi|16080617|ref|NP_391444.1|']
  >>> idx["gi|16080617|ref|NP_391444.1|"]
  QueryResult(id='gi|16080617|ref|NP_391444.1|', 3 hits)
  >>> idx.close()
>>

  Or also with the format-specific keyword argument:
<<>>> idx = SearchIO.index("tab_2226_tblastn_005.txt", "blast-tab",
comments=True)
  >>> sorted(idx.keys())
  ['gi|11464971:4-101', 'gi|16080617|ref|NP_391444.1|', 'random_s00']
  >>> idx["gi|16080617|ref|NP_391444.1|"]
  QueryResult(id='gi|16080617|ref|NP_391444.1|', 3 hits)
  >>> idx.close()
>>

  Or with the key_function argument, as in Bio.SeqIO:
<<>>> key_function = lambda id: id.upper()    # capitalizes the keys
  >>> idx = SearchIO.index("tab_2226_tblastn_001.txt", "blast-tab",
key_function=key_function)
  >>> sorted(idx.keys())
  ['GI|11464971:4-101', 'GI|16080617|REF|NP_391444.1|']
  >>> idx["GI|16080617|REF|NP_391444.1|"]
  QueryResult(id='gi|16080617|ref|NP_391444.1|', 3 hits)
  >>> idx.close()
>>

  Bio.SearchIO.index_db works like as index, only it writes the query
offsets into an SQLite database file.


8.5 Writing and converting search output files
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=


  It is occasionally useful to be able to manipulate search results from
an output file and write it again to a new file. Bio.SearchIO provides a
write function that lets you do exactly this. It takes as its arguments
an iterable returning QueryResult objects, the output filename to write
to, the format name to write to, and optionally some format-specific
keyword arguments. It returns a four-item tuple, which denotes the
number or QueryResult, Hit, HSP, and HSPFragment objects that were
written.
<<>>> from Bio import SearchIO
  >>> qresults = SearchIO.parse("mirna.xml", "blast-xml")     # read XML
file
  >>> SearchIO.write(qresults, "results.tab", "blast-tab")    # write to
tabular file
  (3, 239, 277, 277)
>>

  You should note different file formats require different attributes of
the QueryResult, Hit, HSP and HSPFragment objects. If these attributes
are not present, writing won't work. In other words, you can't always
write to the output format that you want. For example, if you read a
BLAST XML file, you wouldn't be able to write the results to a PSL file
as PSL files require attributes not calculated by BLAST (e.g. the number
of repeat matches). You can always set these attributes manually, if you
really want to write to PSL, though.
  Like read, parse, index, and index_db, write also accepts
format-specific keyword arguments. Check out the documentation for a
complete list of formats Bio.SearchIO can write to and their arguments.
  Finally, Bio.SearchIO also provides a convert function, which is
simply a shortcut for Bio.SearchIO.parse and Bio.SearchIO.write. Using
the convert function, our example above would be:
<<>>> from Bio import SearchIO
  >>> SearchIO.convert("mirna.xml", "blast-xml", "results.tab",
"blast-tab")
  (3, 239, 277, 277)
>>

  As convert uses write, it is only limited to format conversions that
have all the required attributes. Here, the BLAST XML file provides all
the default values a BLAST tabular file requires, so it works just fine.
However, other format conversions are less likely to work since you need
to manually assign the required attributes first.   
-----------------------------------


 (1) https://github.com/biopython/biopython/blob/master/Doc/examples/my_
   blast.xml
 (2) https://raw.githubusercontent.com/biopython/biopython/master/Doc/ex
   amples/my_blat.psl
 (3) http://biopython.org/docs/1.79/api/Bio.SearchIO.BlastIO.html
 (4) http://biopython.org/docs/1.79/api/Bio.SearchIO.BlatIO.html
 (5) http://biopython.org/docs/1.79/api/Bio.SearchIO.html#module-Bio.Sea
   rchIO#module-Bio.SearchIO
 (6) http://biopython.org/docs/1.79/api/Bio.SearchIO.html#module-Bio.Sea
   rchIO


Chapter 9  Accessing NCBI's Entrez databases
********************************************

  Entrez (https://www.ncbi.nlm.nih.gov/Web/Search/entrezfs.html) is a
data retrieval system that provides users access to NCBI's databases
such as PubMed, GenBank, GEO, and many others. You can access Entrez
from a web browser to manually enter queries, or you can use Biopython's
Bio.Entrez module for programmatic access to Entrez. The latter allows
you for example to search PubMed or download GenBank records from within
a Python script.
  The Bio.Entrez module makes use of the Entrez Programming Utilities
(also known as EUtils), consisting of eight tools that are described in
detail on NCBI's page at https://www.ncbi.nlm.nih.gov/books/NBK25501/.
Each of these tools corresponds to one Python function in the Bio.Entrez
module, as described in the sections below. This module makes sure that
the correct URL is used for the queries, and that NCBI's guidelines for
responsible data access are being followed.
  The output returned by the Entrez Programming Utilities is typically
in XML format. To parse such output, you have several options: 

   1. Use Bio.Entrez's parser to parse the XML output into a Python
   object; 
   2. Use one of the XML parsers available in Python's standard library;

   3. Read the XML output as raw text, and parse it by string searching
   and manipulation. 
   See the Python documentation for a description of the XML parsers in
Python's standard library. Here, we discuss the parser in Biopython's
Bio.Entrez module. This parser can be used to parse data as provided
through Bio.Entrez's programmatic access functions to Entrez, but can
also be used to parse XML data from NCBI Entrez that are stored in a
file. In the latter case, the XML file should be opened in binary mode
(e.g. open("myfile.xml", "rb")) for the XML parser in Bio.Entrez to work
correctly.
  NCBI uses DTD (Document Type Definition) files to describe the
structure of the information contained in XML files. Most of the DTD
files used by NCBI are included in the Biopython distribution. The
Bio.Entrez parser makes use of the DTD files when parsing an XML file
returned by NCBI Entrez.
  Occasionally, you may find that the DTD file associated with a
specific XML file is missing in the Biopython distribution. In
particular, this may happen when NCBI updates its DTD files. If this
happens, Entrez.read will show a warning message with the name and URL
of the missing DTD file. The parser will proceed to access the missing
DTD file through the internet, allowing the parsing of the XML file to
continue. However, the parser is much faster if the DTD file is
available locally. For this purpose, please download the DTD file from
the URL in the warning message and place it in the directory
...site-packages/Bio/Entrez/DTDs, containing the other DTD files. If you
don't have write access to this directory, you can also place the DTD
file in ~/.biopython/Bio/Entrez/DTDs, where ~ represents your home
directory. Since this directory is read before the directory
...site-packages/Bio/Entrez/DTDs, you can also put newer versions of DTD
files there if the ones in ...site-packages/Bio/Entrez/DTDs become
outdated. Alternatively, if you installed Biopython from source, you can
add the DTD file to the source code's Bio/Entrez/DTDs directory, and
reinstall Biopython. This will install the new DTD file in the correct
location together with the other DTD files.
  The Entrez Programming Utilities can also generate output in other
formats, such as the Fasta or GenBank file formats for sequence
databases, or the MedLine format for the literature database, discussed
in Section ??.
  The functions in Bio.Entrez for programmatic access to Entrez return
data either in binary format or in text format, depending on the type of
data requested. In most cases, these functions return data in text
format by decoding the data obtained from NCBI Entrez to Python strings
under the assumption that the encoding is UTF-8. However, XML data are
returned in binary format. The reason for this is that the encoding is
specified in the XML document itself, which means that we won't know the
correct encoding to use until we start parsing the file. Bio.Entrez's
parser therefore accepts data in binary format, extracts the encoding
from the XML, and uses it to decode all text in the XML document to
Python strings, ensuring that all text (in particular in languages other
than English) are interpreted correctly. This is also the reason why you
should open an XML file a binary mode when you want to use Bio.Entrez's
parser to parse the file.


9.1 Entrez Guidelines
*=*=*=*=*=*=*=*=*=*=*

    Before using Biopython to access the NCBI's online resources (via
Bio.Entrez or some of the other modules), please read the NCBI's Entrez
User Requirements (1). If the NCBI finds you are abusing their systems,
they can and will ban your access!
  To paraphrase:


   - For any series of more than 100 requests, do this at weekends or
   outside USA peak times. This is up to you to obey. 
   - Use the https://eutils.ncbi.nlm.nih.gov address, not the standard
   NCBI Web address. Biopython uses this web address. 
   - If you are using a API key, you can make at most 10 queries per
   second, otherwise at most 3 queries per second. This is automatically
   enforced by Biopython. Include api_key="MyAPIkey" in the argument
   list or set it as a module level variable: 
   <<>>> from Bio import Entrez
     >>> Entrez.api_key = "MyAPIkey"
   >>

   - Use the optional email parameter so the NCBI can contact you if
   there is a problem. You can either explicitly set this as a parameter
   with each call to Entrez (e.g. include email="A.N.Other@example.com"
   in the argument list), or you can set a global email address:
   <<>>> from Bio import Entrez
     >>> Entrez.email = "A.N.Other@example.com"
   >>
  Bio.Entrez will then use this email address with each call to Entrez.
   The example.com address is a reserved domain name specifically for
   documentation (RFC 2606). Please DO NOT use a random email -- it's
   better not to give an email at all. The email parameter has been
   mandatory since June 1, 2010. In case of excessive usage, NCBI will
   attempt to contact a user at the e-mail address provided prior to
   blocking access to the E-utilities. 
   - If you are using Biopython within some larger software suite, use
   the tool parameter to specify this. You can either explicitly set the
   tool name as a parameter with each call to Entrez (e.g. include
   tool="MyLocalScript" in the argument list), or you can set a global
   tool name:
   <<>>> from Bio import Entrez
     >>> Entrez.tool = "MyLocalScript"
   >>
  The tool parameter will default to Biopython. 
   - For large queries, the NCBI also recommend using their session
   history feature (the WebEnv session cookie string, see Section ??).
   This is only slightly more complicated. 

  In conclusion, be sensible with your usage levels. If you plan to
download lots of data, consider other options. For example, if you want
easy access to all the human genes, consider fetching each chromosome by
FTP as a GenBank file, and importing these into your own BioSQL database
(see Section ??).


9.2 EInfo: Obtaining information about the Entrez databases
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*

    EInfo provides field index term counts, last update, and available
links for each of NCBI's databases. In addition, you can use EInfo to
obtain a list of all database names accessible through the Entrez
utilities: 
<<>>> from Bio import Entrez
  >>> Entrez.email = "A.N.Other@example.com"  # Always tell NCBI who you
are
  >>> handle = Entrez.einfo()
  >>> result = handle.read()
  >>> handle.close()
>>
   The variable result now contains a list of databases in XML format: 
<<>>> print(result)
  <?xml version="1.0"?>
  <!DOCTYPE eInfoResult PUBLIC "-//NLM//DTD eInfoResult, 11 May
2002//EN"
   "https://www.ncbi.nlm.nih.gov/entrez/query/DTD/eInfo_020511.dtd">
  <eInfoResult>
  <DbList>
          <DbName>pubmed</DbName>
          <DbName>protein</DbName>
          <DbName>nucleotide</DbName>
          <DbName>nuccore</DbName>
          <DbName>nucgss</DbName>
          <DbName>nucest</DbName>
          <DbName>structure</DbName>
          <DbName>genome</DbName>
          <DbName>books</DbName>
          <DbName>cancerchromosomes</DbName>
          <DbName>cdd</DbName>
          <DbName>gap</DbName>
          <DbName>domains</DbName>
          <DbName>gene</DbName>
          <DbName>genomeprj</DbName>
          <DbName>gensat</DbName>
          <DbName>geo</DbName>
          <DbName>gds</DbName>
          <DbName>homologene</DbName>
          <DbName>journals</DbName>
          <DbName>mesh</DbName>
          <DbName>ncbisearch</DbName>
          <DbName>nlmcatalog</DbName>
          <DbName>omia</DbName>
          <DbName>omim</DbName>
          <DbName>pmc</DbName>
          <DbName>popset</DbName>
          <DbName>probe</DbName>
          <DbName>proteinclusters</DbName>
          <DbName>pcassay</DbName>
          <DbName>pccompound</DbName>
          <DbName>pcsubstance</DbName>
          <DbName>snp</DbName>
          <DbName>taxonomy</DbName>
          <DbName>toolkit</DbName>
          <DbName>unigene</DbName>
          <DbName>unists</DbName>
  </DbList>
  </eInfoResult>
>>

  Since this is a fairly simple XML file, we could extract the
information it contains simply by string searching. Using Bio.Entrez's
parser instead, we can directly parse this XML file into a Python
object:
<<>>> from Bio import Entrez
  >>> handle = Entrez.einfo()
  >>> record = Entrez.read(handle)
>>
   Now record is a dictionary with exactly one key: 
<<>>> record.keys()
  dict_keys(['DbList'])
>>
   The values stored in this key is the list of database names shown in
the XML above: 
<<>>> record["DbList"]
  ['pubmed', 'protein', 'nucleotide', 'nuccore', 'nucgss', 'nucest',
   'structure', 'genome', 'books', 'cancerchromosomes', 'cdd', 'gap',
   'domains', 'gene', 'genomeprj', 'gensat', 'geo', 'gds', 'homologene',
   'journals', 'mesh', 'ncbisearch', 'nlmcatalog', 'omia', 'omim',
'pmc',
   'popset', 'probe', 'proteinclusters', 'pcassay', 'pccompound',
   'pcsubstance', 'snp', 'taxonomy', 'toolkit', 'unigene', 'unists']
>>

  For each of these databases, we can use EInfo again to obtain more
information:
<<>>> from Bio import Entrez
  >>> Entrez.email = "A.N.Other@example.com"  # Always tell NCBI who you
are
  >>> handle = Entrez.einfo(db="pubmed")
  >>> record = Entrez.read(handle)
  >>> record["DbInfo"]["Description"]
  'PubMed bibliographic record'
>>

<<>>> record["DbInfo"]["Count"]
  '17989604'
  >>> record["DbInfo"]["LastUpdate"]
  '2008/05/24 06:45'
>>
   Try record["DbInfo"].keys() for other information stored in this
record. One of the most useful is a list of possible search fields for
use with ESearch:
<<>>> for field in record["DbInfo"]["FieldList"]:
  ...     print("%(Name)s, %(FullName)s, %(Description)s" % field)
  ...
  ALL, All Fields, All terms from all searchable fields
  UID, UID, Unique number assigned to publication
  FILT, Filter, Limits the records
  TITL, Title, Words in title of publication
  WORD, Text Word, Free text associated with publication
  MESH, MeSH Terms, Medical Subject Headings assigned to publication
  MAJR, MeSH Major Topic, MeSH terms of major importance to publication
  AUTH, Author, Author(s) of publication
  JOUR, Journal, Journal abbreviation of publication
  AFFL, Affiliation, Author's institutional affiliation and address
  ...
>>

  That's a long list, but indirectly this tells you that for the PubMed
database, you can do things like Jones[AUTH] to search the author field,
or Sanger[AFFL] to restrict to authors at the Sanger Centre. This can be
very handy - especially if you are not so familiar with a particular
database.


9.3 ESearch: Searching the Entrez databases
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*

    To search any of these databases, we use Bio.Entrez.esearch(). For
example, let's search in PubMed for publications that include Biopython
in their title:
<<>>> from Bio import Entrez
  >>> Entrez.email = "A.N.Other@example.com"     # Always tell NCBI who
you are
  >>> handle = Entrez.esearch(db="pubmed", term="biopython[title]",
retmax="40" )
  >>> record = Entrez.read(handle)
  >>> "19304878" in record["IdList"]
  True
>>

<<>>> print(record["IdList"])
  ['22909249', '19304878']
>>
   In this output, you see PubMed IDs (including 19304878 which is the
PMID for the Biopython application note), which can be retrieved by
EFetch (see section ??).
  You can also use ESearch to search GenBank. Here we'll do a quick
search for the matK gene in Cypripedioideae orchids (see Section ??
about EInfo for one way to find out which fields you can search in each
Entrez database):
<<>>> handle = Entrez.esearch(db="nucleotide",
term="Cypripedioideae[Orgn] AND matK[Gene]", idtype="acc")
  >>> record = Entrez.read(handle)
  >>> record["Count"]
  '348'
  >>> record["IdList"]
  ['JQ660909.1', 'JQ660908.1', 'JQ660907.1', 'JQ660906.1', ...,
'JQ660890.1']
>>

  Each of the IDs (JQ660909.1, JQ660908.1, JQ660907.1, ...) is a GenBank
identifier (Accession number). See section ?? for information on how to
actually download these GenBank records.
  Note that instead of a species name like Cypripedioideae[Orgn], you
can restrict the search using an NCBI taxon identifier, here this would
be txid158330[Orgn]. This isn't currently documented on the ESearch help
page - the NCBI explained this in reply to an email query. You can often
deduce the search term formatting by playing with the Entrez web
interface. For example, including complete[prop] in a genome search
restricts to just completed genomes.
  As a final example, let's get a list of computational journal titles: 
<<>>> handle = Entrez.esearch(db="nlmcatalog",
term="computational[Journal]", retmax="20")
  >>> record = Entrez.read(handle)
  >>> print("{} computational journals found".format(record["Count"]))
  117 computational Journals found
  >>> print("The first 20 are\n{}".format(record["IdList"]))
  ['101660833', '101664671', '101661657', '101659814', '101657941',
   '101653734', '101669877', '101649614', '101647835', '101639023',
   '101627224', '101647801', '101589678', '101585369', '101645372',
   '101586429', '101582229', '101574747', '101564639', '101671907']
>>
   Again, we could use EFetch to obtain more information for each of
these journal IDs.
  ESearch has many useful options --- see the ESearch help page (2) for
more information.

9.4 EPost: Uploading a list of identifiers
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=

   EPost uploads a list of UIs for use in subsequent search strategies;
see the EPost help page (3) for more information. It is available from
Biopython through the Bio.Entrez.epost() function.
  To give an example of when this is useful, suppose you have a long
list of IDs you want to download using EFetch (maybe sequences, maybe
citations -- anything). When you make a request with EFetch your list of
IDs, the database etc, are all turned into a long URL sent to the
server. If your list of IDs is long, this URL gets long, and long URLs
can break (e.g. some proxies don't cope well).
  Instead, you can break this up into two steps, first uploading the
list of IDs using EPost (this uses an "HTML post" internally, rather
than an "HTML get", getting round the long URL problem). With the
history support, you can then refer to this long list of IDs, and
download the associated data with EFetch.
  Let's look at a simple example to see how EPost works -- uploading
some PubMed identifiers: 
<<>>> from Bio import Entrez
  >>> Entrez.email = "A.N.Other@example.com"     # Always tell NCBI who
you are
  >>> id_list = ["19304878", "18606172", "16403221", "16377612",
"14871861", "14630660"]
  >>> print(Entrez.epost("pubmed", id=",".join(id_list)).read())
  <?xml version="1.0"?>
  <!DOCTYPE ePostResult PUBLIC "-//NLM//DTD ePostResult, 11 May
2002//EN"
   "https://www.ncbi.nlm.nih.gov/entrez/query/DTD/ePost_020511.dtd">
  <ePostResult>
   <QueryKey>1</QueryKey>
   <WebEnv>NCID_01_206841095_130.14.22.101_9001_1242061629</WebEnv>
  </ePostResult>
>>
   The returned XML includes two important strings, QueryKey and WebEnv
which together define your history session. You would extract these
values for use with another Entrez call such as EFetch:
<<>>> from Bio import Entrez
  >>> Entrez.email = "A.N.Other@example.com"     # Always tell NCBI who
you are
  >>> id_list = ["19304878", "18606172", "16403221", "16377612",
"14871861", "14630660"]
  >>> search_results = Entrez.read(Entrez.epost("pubmed",
id=",".join(id_list)))
  >>> webenv = search_results["WebEnv"]
  >>> query_key = search_results["QueryKey"]
>>
   Section ?? shows how to use the history feature.

9.5 ESummary: Retrieving summaries from primary IDs
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*

   ESummary retrieves document summaries from a list of primary IDs (see
the ESummary help page (4) for more information). In Biopython, ESummary
is available as Bio.Entrez.esummary(). Using the search result above, we
can for example find out more about the journal with ID 30367:
<<>>> from Bio import Entrez
  >>> Entrez.email = "A.N.Other@example.com"  # Always tell NCBI who you
are
  >>> handle = Entrez.esummary(db="nlmcatalog", id="101660833")
  >>> record = Entrez.read(handle)
  >>> info = record[0]["TitleMainList"][0]
  >>> print("Journal info\nid: {}\nTitle: {}".format(record[0]["Id"],
info["Title"]))
  Journal info
  id: 101660833
  Title: IEEE transactions on computational imaging.
>>

9.6 EFetch: Downloading full records from Entrez
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=

  EFetch is what you use when you want to retrieve a full record from
Entrez. This covers several possible databases, as described on the main
EFetch Help page (5).
  For most of their databases, the NCBI support several different file
formats. Requesting a specific file format from Entrez using
Bio.Entrez.efetch() requires specifying the rettype and/or retmode
optional arguments. The different combinations are described for each
database type on the pages linked to on NCBI efetch webpage (6).
  One common usage is downloading sequences in the FASTA or
GenBank/GenPept plain text formats (which can then be parsed with
Bio.SeqIO, see Sections ?? and ??). From the Cypripedioideae example
above, we can download GenBank record EU490707 using Bio.Entrez.efetch:
<<>>> from Bio import Entrez
  >>> Entrez.email = "A.N.Other@example.com"  # Always tell NCBI who you
are
  >>> handle = Entrez.efetch(db="nucleotide", id="EU490707",
rettype="gb", retmode="text")
  >>> print(handle.read())
  LOCUS       EU490707                1302 bp    DNA     linear   PLN
26-JUL-2016
  DEFINITION  Selenipedium aequinoctiale maturase K (matK) gene, partial
cds;
              chloroplast.
  ACCESSION   EU490707
  VERSION     EU490707.1
  KEYWORDS    .
  SOURCE      chloroplast Selenipedium aequinoctiale
    ORGANISM  Selenipedium aequinoctiale
              Eukaryota; Viridiplantae; Streptophyta; Embryophyta;
Tracheophyta;
              Spermatophyta; Magnoliopsida; Liliopsida; Asparagales;
Orchidaceae;
              Cypripedioideae; Selenipedium.
  REFERENCE   1  (bases 1 to 1302)
    AUTHORS   Neubig,K.M., Whitten,W.M., Carlsward,B.S., Blanco,M.A.,
Endara,L.,
              Williams,N.H. and Moore,M.
    TITLE     Phylogenetic utility of ycf1 in orchids: a plastid gene
more
              variable than matK
    JOURNAL   Plant Syst. Evol. 277 (1-2), 75-84 (2009)
  REFERENCE   2  (bases 1 to 1302)
    AUTHORS   Neubig,K.M., Whitten,W.M., Carlsward,B.S., Blanco,M.A.,
              Endara,C.L., Williams,N.H. and Moore,M.J.
    TITLE     Direct Submission
    JOURNAL   Submitted (14-FEB-2008) Department of Botany, University
of
              Florida, 220 Bartram Hall, Gainesville, FL 32611-8526, USA
  FEATURES             Location/Qualifiers
       source          1..1302
                       /organism="Selenipedium aequinoctiale"
                       /organelle="plastid:chloroplast"
                       /mol_type="genomic DNA"
                       /specimen_voucher="FLAS:Blanco 2475"
                       /db_xref="taxon:256374"
       gene            <1..>1302
                       /gene="matK"
       CDS             <1..>1302
                       /gene="matK"
                       /codon_start=1
                       /transl_table=11
                       /product="maturase K"
                       /protein_id="ACC99456.1"

/translation="IFYEPVEIFGYDNKSSLVLVKRLITRMYQQNFLISSVNDSNQKG

FWGHKHFFSSHFSSQMVSEGFGVILEIPFSSQLVSSLEEKKIPKYQNLRSIHSIFPFL

EDKFLHLNYVSDLLIPHPIHLEILVQILQCRIKDVPSLHLLRLLFHEYHNLNSLITSK

KFIYAFSKRKKRFLWLLYNSYVYECEYLFQFLRKQSSYLRSTSSGVFLERTHLYVKIE

HLLVVCCNSFQRILCFLKDPFMHYVRYQGKAILASKGTLILMKKWKFHLVNFWQSYFH

FWSQPYRIHIKQLSNYSFSFLGYFSSVLENHLVVRNQMLENSFIINLLTKKFDTIAPV

ISLIGSLSKAQFCTVLGHPISKPIWTDFSDSDILDRFCRICRNLCRYHSGSSKKQVLY
                       RIKYILRLSCARTLARKHKSTVRTFMRRLGSGLLEEFFMEEE"
  ORIGIN      
          1 attttttacg aacctgtgga aatttttggt tatgacaata aatctagttt
agtacttgtg
         61 aaacgtttaa ttactcgaat gtatcaacag aattttttga tttcttcggt
taatgattct
        121 aaccaaaaag gattttgggg gcacaagcat tttttttctt ctcatttttc
ttctcaaatg
        181 gtatcagaag gttttggagt cattctggaa attccattct cgtcgcaatt
agtatcttct
        241 cttgaagaaa aaaaaatacc aaaatatcag aatttacgat ctattcattc
aatatttccc
        301 tttttagaag acaaattttt acatttgaat tatgtgtcag atctactaat
accccatccc
        361 atccatctgg aaatcttggt tcaaatcctt caatgccgga tcaaggatgt
tccttctttg
        421 catttattgc gattgctttt ccacgaatat cataatttga atagtctcat
tacttcaaag
        481 aaattcattt acgccttttc aaaaagaaag aaaagattcc tttggttact
atataattct
        541 tatgtatatg aatgcgaata tctattccag tttcttcgta aacagtcttc
ttatttacga
        601 tcaacatctt ctggagtctt tcttgagcga acacatttat atgtaaaaat
agaacatctt
        661 ctagtagtgt gttgtaattc ttttcagagg atcctatgct ttctcaagga
tcctttcatg
        721 cattatgttc gatatcaagg aaaagcaatt ctggcttcaa agggaactct
tattctgatg
        781 aagaaatgga aatttcatct tgtgaatttt tggcaatctt attttcactt
ttggtctcaa
        841 ccgtatagga ttcatataaa gcaattatcc aactattcct tctcttttct
ggggtatttt
        901 tcaagtgtac tagaaaatca tttggtagta agaaatcaaa tgctagagaa
ttcatttata
        961 ataaatcttc tgactaagaa attcgatacc atagccccag ttatttctct
tattggatca
       1021 ttgtcgaaag ctcaattttg tactgtattg ggtcatccta ttagtaaacc
gatctggacc
       1081 gatttctcgg attctgatat tcttgatcga ttttgccgga tatgtagaaa
tctttgtcgt
       1141 tatcacagcg gatcctcaaa aaaacaggtt ttgtatcgta taaaatatat
acttcgactt
       1201 tcgtgtgcta gaactttggc acggaaacat aaaagtacag tacgcacttt
tatgcgaaga
       1261 ttaggttcgg gattattaga agaattcttt atggaagaag aa
  //
  <BLANKLINE>
  <BLANKLINE>
>>

  Please be aware that as of October 2016 GI identifiers are
discontinued in favour of accession numbers. You can still fetch
sequences based on their GI, but new sequences are no longer given this
identifier. You should instead refer to them by the "Accession number"
as done in the example.
  The arguments rettype="gb" and retmode="text" let us download this
record in the GenBank format.
  Note that until Easter 2009, the Entrez EFetch API let you use
"genbank" as the return type, however the NCBI now insist on using the
official return types of "gb" or "gbwithparts" (or "gp" for proteins) as
described on online. Also note that until Feb 2012, the Entrez EFetch
API would default to returning plain text files, but now defaults to
XML.
  Alternatively, you could for example use rettype="fasta" to get the
Fasta-format; see the EFetch Sequences Help page (7) for other options.
Remember -- the available formats depend on which database you are
downloading from - see the main EFetch Help page (8).
  If you fetch the record in one of the formats accepted by Bio.SeqIO
(see Chapter ??), you could directly parse it into a SeqRecord:
<<>>> from Bio import SeqIO
  >>> from Bio import Entrez
  >>> Entrez.email = "A.N.Other@example.com"  # Always tell NCBI who you
are
  >>> handle = Entrez.efetch(db="nucleotide", id="EU490707",
rettype="gb", retmode="text")
  >>> record = SeqIO.read(handle, "genbank")
  >>> handle.close()
  >>> print(record.id)
  EU490707.1
  >>> print(record.name)
  EU490707
  >>> print(record.description)
  Selenipedium aequinoctiale maturase K (matK) gene, partial cds;
chloroplast
  >>> print(len(record.features))
  3
  >>> record.seq
  Seq('ATTTTTTACGAACCTGTGGAAATTTTTGGTTATGACAATAAATCTAGTTTAGTA...GAA')
>>

  Note that a more typical use would be to save the sequence data to a
local file, and then parse it with Bio.SeqIO. This can save you having
to re-download the same file repeatedly while working on your script,
and places less load on the NCBI's servers. For example:
<<import os
  from Bio import SeqIO
  from Bio import Entrez

  Entrez.email = "A.N.Other@example.com"  # Always tell NCBI who you are
  filename = "EU490707.gbk"
  if not os.path.isfile(filename):
      # Downloading...
      net_handle = Entrez.efetch(
          db="nucleotide", id="EU490707", rettype="gb", retmode="text"
      )
      out_handle = open(filename, "w")
      out_handle.write(net_handle.read())
      out_handle.close()
      net_handle.close()
      print("Saved")

  print("Parsing...")
  record = SeqIO.read(filename, "genbank")
  print(record)
>>

  To get the output in XML format, which you can parse using the
Bio.Entrez.read() function, use retmode="xml":
<<>>> from Bio import Entrez
  >>> Entrez.email = "A.N.Other@example.com"  # Always tell NCBI who you
are
  >>> handle = Entrez.efetch(db="nucleotide", id="EU490707",
retmode="xml")
  >>> record = Entrez.read(handle)
  >>> handle.close()
  >>> record[0]["GBSeq_definition"]
  'Selenipedium aequinoctiale maturase K (matK) gene, partial cds;
chloroplast'
  >>> record[0]["GBSeq_source"]
  'chloroplast Selenipedium aequinoctiale'
>>

  So, that dealt with sequences. For examples of parsing file formats
specific to the other databases (e.g. the MEDLINE format used in
PubMed), see Section ??.
  If you want to perform a search with Bio.Entrez.esearch(), and then
download the records with Bio.Entrez.efetch(), you should use the WebEnv
history feature -- see Section ??.

9.7 ELink: Searching for related items in NCBI Entrez
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*

  ELink, available from Biopython as Bio.Entrez.elink(), can be used to
find related items in the NCBI Entrez databases. For example, you can us
this to find nucleotide entries for an entry in the gene database, and
other cool stuff.
  Let's use ELink to find articles related to the Biopython application
note published in Bioinformatics in 2009. The PubMed ID of this article
is 19304878:
<<>>> from Bio import Entrez
  >>> Entrez.email = "A.N.Other@example.com"  # Always tell NCBI who you
are
  >>> pmid = "19304878"
  >>> record = Entrez.read(Entrez.elink(dbfrom="pubmed", id=pmid))
>>

  The record variable consists of a Python list, one for each database
in which we searched. Since we specified only one PubMed ID to search
for, record contains only one item. This item is a dictionary containing
information about our search term, as well as all the related items that
were found:
<<>>> record[0]["DbFrom"]
  'pubmed'
  >>> record[0]["IdList"]
  ['19304878']
>>

  The "LinkSetDb" key contains the search results, stored as a list
consisting of one item for each target database. In our search results,
we only find hits in the PubMed database (although sub-divided into
categories):
<<>>> len(record[0]["LinkSetDb"])
  8
>>
   The exact numbers should increase over time: 
<<>>> for linksetdb in record[0]["LinkSetDb"]:
  ...     print(linksetdb["DbTo"], linksetdb["LinkName"],
len(linksetdb["Link"]))
  ...
  pubmed pubmed_pubmed 284
  pubmed pubmed_pubmed_alsoviewed 7
  pubmed pubmed_pubmed_citedin 926
  pubmed pubmed_pubmed_combined 6
  pubmed pubmed_pubmed_five 6
  pubmed pubmed_pubmed_refs 17
  pubmed pubmed_pubmed_reviews 12
  pubmed pubmed_pubmed_reviews_five 6
>>

  The actual search results are stored as under the "Link" key.
  Let's now at the first search result: 
<<>>> record[0]["LinkSetDb"][0]["Link"][0]
  {'Id': '19304878'}
>>

  This is the article we searched for, which doesn't help us much, so
let's look at the second search result:
<<>>> record[0]["LinkSetDb"][0]["Link"][1]
  {'Id': '14630660'}
>>

  This paper, with PubMed ID 14630660, is about the Biopython PDB
parser.
  We can use a loop to print out all PubMed IDs: 
<<>>> for link in record[0]["LinkSetDb"][0]["Link"]:
  ...     print(link["Id"])
  19304878
  14630660
  18689808
  17121776
  16377612
  12368254
  ......
>>

  Now that was nice, but personally I am often more interested to find
out if a paper has been cited. Well, ELink can do that too -- at least
for journals in Pubmed Central (see Section ??).
  For help on ELink, see the ELink help page (9). There is an entire
sub-page just for the link names (10), describing how different
databases can be cross referenced.

9.8 EGQuery: Global Query - counts for search terms
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*

   EGQuery provides counts for a search term in each of the Entrez
databases (i.e. a global query). This is particularly useful to find out
how many items your search terms would find in each database without
actually performing lots of separate searches with ESearch (see the
example in ?? below).
  In this example, we use Bio.Entrez.egquery() to obtain the counts for
"Biopython":
<<>>> from Bio import Entrez
  >>> Entrez.email = "A.N.Other@example.com"  # Always tell NCBI who you
are
  >>> handle = Entrez.egquery(term="biopython")
  >>> record = Entrez.read(handle)
  >>> for row in record["eGQueryResult"]:
  ...     print(row["DbName"], row["Count"])
  ...
  pubmed 6
  pmc 62
  journals 0
  ...
>>
   See the EGQuery help page (11) for more information.

9.9 ESpell: Obtaining spelling suggestions
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=

   ESpell retrieves spelling suggestions. In this example, we use
Bio.Entrez.espell() to obtain the correct spelling of Biopython:
<<>>> from Bio import Entrez
  >>> Entrez.email = "A.N.Other@example.com"  # Always tell NCBI who you
are
  >>> handle = Entrez.espell(term="biopythooon")
  >>> record = Entrez.read(handle)
  >>> record["Query"]
  'biopythooon'
  >>> record["CorrectedQuery"]
  'biopython'
>>
   See the ESpell help page (12) for more information. The main use of
this is for GUI tools to provide automatic suggestions for search terms.

9.10 Parsing huge Entrez XML files
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=

  The Entrez.read function reads the entire XML file returned by Entrez
into a single Python object, which is kept in memory. To parse Entrez
XML files too large to fit in memory, you can use the function
Entrez.parse. This is a generator function that reads records in the XML
file one by one. This function is only useful if the XML file reflects a
Python list object (in other words, if Entrez.read on a computer with
infinite memory resources would return a Python list).
  For example, you can download the entire Entrez Gene database for a
given organism as a file from NCBI's ftp site. These files can be very
large. As an example, on September 4, 2009, the file
Homo_sapiens.ags.gz, containing the Entrez Gene database for human, had
a size of 116576 kB. This file, which is in the ASN format, can be
converted into an XML file using NCBI's gene2xml program (see NCBI's ftp
site for more information):
<<$ gene2xml -b T -i Homo_sapiens.ags -o Homo_sapiens.xml
>>

  The resulting XML file has a size of 6.1 GB. Attempting Entrez.read on
this file will result in a MemoryError on many computers.
  The XML file Homo_sapiens.xml consists of a list of Entrez gene
records, each corresponding to one Entrez gene in human. Entrez.parse
retrieves these gene records one by one. You can then print out or store
the relevant information in each record by iterating over the records.
For example, this script iterates over the Entrez gene records and
prints out the gene numbers and names for all current genes:
<<>>> from Bio import Entrez
  >>> Entrez.email = "A.N.Other@example.com"  # Always tell NCBI who you
are
  >>> handle = open("Homo_sapiens.xml", "b")
  >>> records = Entrez.parse(handle)
  >>> for record in records:
  ...     status =
record["Entrezgene_track-info"]["Gene-track"]["Gene-track_status"]
  ...     if status.attributes["value"]=="discontinued":
  ...         continue
  ...     geneid =
record["Entrezgene_track-info"]["Gene-track"]["Gene-track_geneid"]
  ...     genename =
record["Entrezgene_gene"]["Gene-ref"]["Gene-ref_locus"]
  ...     print(geneid, genename)
  ...
  1 A1BG
  2 A2M
  3 A2MP
  8 AA
  9 NAT1
  10 NAT2
  11 AACP
  12 SERPINA3
  13 AADAC
  14 AAMP
  15 AANAT
  16 AARS
  17 AAVS1
  ...
>>

9.11 HTML escape characters
*=*=*=*=*=*=*=*=*=*=*=*=*=*

  Pubmed records may contain HTML tags to indicate e.g. subscripts,
superscripts, or italic text, as well as mathematical symbols via
MathML. By default, the Bio.Entrez parser treats all text as plain text
without markup; for example, the fragment "P < 0.05" in the abstract of
a Pubmed record, which is encoded as 
<<P < 0.05
>>
   in the XML returned by Entrez, is converted to the Python string 
<<'P < 0.05'
>>
   by the Bio.Entrez parser. While this is more human-readable, it is
not valid HTML due to the less-than sign, and makes further processing
of the text e.g. by an HTML parser impractical. To ensure that all
strings returned by the parser are valid HTML, call Entrez.read or
Entrez.parse with the escape argument set to True: 
<<>>> record = Entrez.read(handle, escape=True)
>>
   The parser will then replace all characters disallowed in HTML by
their HTML-escaped equivalent; in the example above, the parser will
generate 
<<'P < 0.05'
>>
   which is a valid HTML fragment. By default, escape is False.

9.12 Handling errors
*=*=*=*=*=*=*=*=*=*=

  Three things can go wrong when parsing an XML file: 

   - The file may not be an XML file to begin with; 
   - The file may end prematurely or otherwise be corrupted; 
   - The file may be correct XML, but contain items that are not
   represented in the associated DTD. 

  The first case occurs if, for example, you try to parse a Fasta file
as if it were an XML file:
<<>>> from Bio import Entrez
  >>> handle = open("NC_005816.fna", "rb") # a Fasta file
  >>> record = Entrez.read(handle)
  Traceback (most recent call last):
    ...
  Bio.Entrez.Parser.NotXMLError: Failed to parse the XML data (syntax
error: line 1, column 0). Please make sure that the input data are in
XML format.
>>
   Here, the parser didn't find the <?xml ... tag with which an XML file
is supposed to start, and therefore decides (correctly) that the file is
not an XML file.
  When your file is in the XML format but is corrupted (for example, by
ending prematurely), the parser will raise a CorruptedXMLError. Here is
an example of an XML file that ends prematurely: 
<<<?xml version="1.0"?>
  <!DOCTYPE eInfoResult PUBLIC "-//NLM//DTD eInfoResult, 11 May
2002//EN"
"https://www.ncbi.nlm.nih.gov/entrez/query/DTD/eInfo_020511.dtd">
  <eInfoResult>
  <DbList>
          <DbName>pubmed</DbName>
          <DbName>protein</DbName>
          <DbName>nucleotide</DbName>
          <DbName>nuccore</DbName>
          <DbName>nucgss</DbName>
          <DbName>nucest</DbName>
          <DbName>structure</DbName>
          <DbName>genome</DbName>
          <DbName>books</DbName>
          <DbName>cancerchromosomes</DbName>
          <DbName>cdd</DbName>
>>
   which will generate the following traceback: 
<<>>> Entrez.read(handle)
  Traceback (most recent call last):
    ...
  Bio.Entrez.Parser.CorruptedXMLError: Failed to parse the XML data (no
element found: line 16, column 0). Please make sure that the input data
are not corrupted.
>>
   Note that the error message tells you at what point in the XML file
the error was detected.
  The third type of error occurs if the XML file contains tags that do
not have a description in the corresponding DTD file. This is an example
of such an XML file:
<<<?xml version="1.0"?>
  <!DOCTYPE eInfoResult PUBLIC "-//NLM//DTD eInfoResult, 11 May
2002//EN"
"https://www.ncbi.nlm.nih.gov/entrez/query/DTD/eInfo_020511.dtd">
  <eInfoResult>
          <DbInfo>
          <DbName>pubmed</DbName>
          <MenuName>PubMed</MenuName>
          <Description>PubMed bibliographic record</Description>
          <Count>20161961</Count>
          <LastUpdate>2010/09/10 04:52</LastUpdate>
          <FieldList>
                  <Field>
  ...
                  </Field>
          </FieldList>
          <DocsumList>
                  <Docsum>
                          <DsName>PubDate</DsName>
                          <DsType>4</DsType>
                          <DsTypeName>string</DsTypeName>
                  </Docsum>
                  <Docsum>
                          <DsName>EPubDate</DsName>
  ...
          </DbInfo>
  </eInfoResult>
>>

  In this file, for some reason the tag <DocsumList> (and several
others) are not listed in the DTD file eInfo_020511.dtd, which is
specified on the second line as the DTD for this XML file. By default,
the parser will stop and raise a ValidationError if it cannot find some
tag in the DTD:
<<>>> from Bio import Entrez
  >>> handle = open("einfo3.xml", "rb")
  >>> record = Entrez.read(handle)
  Traceback (most recent call last):
    ...
  Bio.Entrez.Parser.ValidationError: Failed to find tag 'DocsumList' in
the DTD. To skip all tags that are not represented in the DTD, please
call Bio.Entrez.read or Bio.Entrez.parse with validate=False.
>>
   Optionally, you can instruct the parser to skip such tags instead of
raising a ValidationError. This is done by calling Entrez.read or
Entrez.parse with the argument validate equal to False:
<<>>> from Bio import Entrez
  >>> handle = open("einfo3.xml", "rb")
  >>> record = Entrez.read(handle, validate=False)
  >>> handle.close()
>>
   Of course, the information contained in the XML tags that are not in
the DTD are not present in the record returned by Entrez.read.

9.13 Specialized parsers
*=*=*=*=*=*=*=*=*=*=*=*=

  The Bio.Entrez.read() function can parse most (if not all) XML output
returned by Entrez. Entrez typically allows you to retrieve records in
other formats, which may have some advantages compared to the XML format
in terms of readability (or download size).
  To request a specific file format from Entrez using
Bio.Entrez.efetch() requires specifying the rettype and/or retmode
optional arguments. The different combinations are described for each
database type on the NCBI efetch webpage (13).
  One obvious case is you may prefer to download sequences in the FASTA
or GenBank/GenPept plain text formats (which can then be parsed with
Bio.SeqIO, see Sections ?? and ??). For the literature databases,
Biopython contains a parser for the MEDLINE format used in PubMed.

9.13.1 Parsing Medline records
==============================
    You can find the Medline parser in Bio.Medline. Suppose we want to
parse the file pubmed_result1.txt, containing one Medline record. You
can find this file in Biopython's Tests\Medline directory. The file
looks like this:
<<PMID- 12230038
  OWN - NLM
  STAT- MEDLINE
  DA  - 20020916
  DCOM- 20030606
  LR  - 20041117
  PUBM- Print
  IS  - 1467-5463 (Print)
  VI  - 3
  IP  - 3
  DP  - 2002 Sep
  TI  - The Bio* toolkits--a brief overview.
  PG  - 296-302
  AB  - Bioinformatics research is often difficult to do with commercial
software. The
        Open Source BioPerl, BioPython and Biojava projects provide
toolkits with
  ...
>>
   We first open the file and then parse it:
<<>>> from Bio import Medline
  >>> with open("pubmed_result1.txt") as handle:
  ...    record = Medline.read(handle)
  ...
>>
   The record now contains the Medline record as a Python dictionary:
<<>>> record["PMID"]
  '12230038'
>>

<<>>> record["AB"]
  'Bioinformatics research is often difficult to do with commercial
software.
  The Open Source BioPerl, BioPython and Biojava projects provide
toolkits with
  multiple functionality that make it easier to create customised
pipelines or
  analysis. This review briefly compares the quirks of the underlying
languages
  and the functionality, documentation, utility and relative advantages
of the
  Bio counterparts, particularly from the point of view of the beginning
  biologist programmer.'
>>
   The key names used in a Medline record can be rather obscure; use 
<<>>> help(record)
>>
   for a brief summary.
  To parse a file containing multiple Medline records, you can use the
parse function instead:
<<>>> from Bio import Medline
  >>> with open("pubmed_result2.txt") as handle:
  ...     for record in Medline.parse(handle):
  ...         print(record["TI"])
  ...
  A high level interface to SCOP and ASTRAL implemented in python.
  GenomeDiagram: a python package for the visualization of large-scale
genomic data.
  Open source clustering software.
  PDB file parser and structure class implemented in Python.
>>

  Instead of parsing Medline records stored in files, you can also parse
Medline records downloaded by Bio.Entrez.efetch. For example, let's look
at all Medline records in PubMed related to Biopython: 
<<>>> from Bio import Entrez
  >>> Entrez.email = "A.N.Other@example.com"  # Always tell NCBI who you
are
  >>> handle = Entrez.esearch(db="pubmed", term="biopython")
  >>> record = Entrez.read(handle)
  >>> record["IdList"]
  ['19304878', '18606172', '16403221', '16377612', '14871861',
'14630660', '12230038']
>>
   We now use Bio.Entrez.efetch to download these Medline records: 
<<>>> idlist = record["IdList"]
  >>> handle = Entrez.efetch(db="pubmed", id=idlist, rettype="medline",
retmode="text")
>>
   Here, we specify rettype="medline", retmode="text" to obtain the
Medline records in plain-text Medline format. Now we use Bio.Medline to
parse these records: 
<<>>> from Bio import Medline
  >>> records = Medline.parse(handle)
  >>> for record in records:
  ...     print(record["AU"])
  ['Cock PJ', 'Antao T', 'Chang JT', 'Chapman BA', 'Cox CJ', 'Dalke A',
..., 'de Hoon MJ']
  ['Munteanu CR', 'Gonzalez-Diaz H', 'Magalhaes AL']
  ['Casbon JA', 'Crooks GE', 'Saqi MA']
  ['Pritchard L', 'White JA', 'Birch PR', 'Toth IK']
  ['de Hoon MJ', 'Imoto S', 'Nolan J', 'Miyano S']
  ['Hamelryck T', 'Manderick B']
  ['Mangalam H']
>>

  For comparison, here we show an example using the XML format: 
<<>>> handle = Entrez.efetch(db="pubmed", id=idlist, rettype="medline",
retmode="xml")
  >>> records = Entrez.read(handle)
  >>> for record in records["PubmedArticle"]:
  ...     print(record["MedlineCitation"]["Article"]["ArticleTitle"])
  Biopython: freely available Python tools for computational molecular
biology and
   bioinformatics.
  Enzymes/non-enzymes classification model complexity based on
composition, sequence,
   3D and topological indices.
  A high level interface to SCOP and ASTRAL implemented in python.
  GenomeDiagram: a python package for the visualization of large-scale
genomic data.
  Open source clustering software.
  PDB file parser and structure class implemented in Python.
  The Bio* toolkits--a brief overview.
>>

  Note that in both of these examples, for simplicity we have naively
combined ESearch and EFetch. In this situation, the NCBI would expect
you to use their history feature, as illustrated in Section ??.

9.13.2 Parsing GEO records
==========================

  GEO (Gene Expression Omnibus (14)) is a data repository of
high-throughput gene expression and hybridization array data. The
Bio.Geo module can be used to parse GEO-formatted data.
  The following code fragment shows how to parse the example GEO file
GSE16.txt into a record and print the record:
<<>>> from Bio import Geo
  >>> handle = open("GSE16.txt")
  >>> records = Geo.parse(handle)
  >>> for record in records:
  ...     print(record)
>>

  You can search the "gds" database (GEO datasets) with ESearch:
<<>>> from Bio import Entrez
  >>> Entrez.email = "A.N.Other@example.com"  # Always tell NCBI who you
are
  >>> handle = Entrez.esearch(db="gds", term="GSE16")
  >>> record = Entrez.read(handle)
  >>> handle.close()
  >>> record["Count"]
  '27'
>>

<<>>> record["IdList"]
  ['200000016', '100000028', ...]
>>

  From the Entrez website, UID "200000016" is GDS16 while the other hit
"100000028" is for the associated platform, GPL28. Unfortunately, at the
time of writing the NCBI don't seem to support downloading GEO files
using Entrez (not as XML, nor in the Simple Omnibus Format in Text
(SOFT) format).
  However, it is actually pretty straight forward to download the GEO
files by FTP from ftp://ftp.ncbi.nih.gov/pub/geo/ instead. In this case
you might want
ftp://ftp.ncbi.nih.gov/pub/geo/DATA/SOFT/by_series/GSE16/GSE16_family.so
ft.gz (a compressed file, see the Python module gzip).

9.13.3 Parsing UniGene records
==============================

  UniGene is an NCBI database of the transcriptome, with each UniGene
record showing the set of transcripts that are associated with a
particular gene in a specific organism. A typical UniGene record looks
like this:
<<ID          Hs.2
  TITLE       N-acetyltransferase 2 (arylamine N-acetyltransferase)
  GENE        NAT2
  CYTOBAND    8p22
  GENE_ID     10
  LOCUSLINK   10
  HOMOL       YES
  EXPRESS      bone| connective tissue| intestine| liver| liver tumor|
normal| soft tissue/muscle tissue tumor| adult
  RESTR_EXPR   adult
  CHROMOSOME  8
  STS         ACC=PMC310725P3 UNISTS=272646
  STS         ACC=WIAF-2120 UNISTS=44576
  STS         ACC=G59899 UNISTS=137181
  ...
  STS         ACC=GDB:187676 UNISTS=155563
  PROTSIM     ORG=10090; PROTGI=6754794; PROTID=NP_035004.1; PCT=76.55;
ALN=288
  PROTSIM     ORG=9796; PROTGI=149742490; PROTID=XP_001487907.1;
PCT=79.66; ALN=288
  PROTSIM     ORG=9986; PROTGI=126722851; PROTID=NP_001075655.1;
PCT=76.90; ALN=288
  ...
  PROTSIM     ORG=9598; PROTGI=114619004; PROTID=XP_519631.2; PCT=98.28;
ALN=288

  SCOUNT      38
  SEQUENCE    ACC=BC067218.1; NID=g45501306; PID=g45501307; SEQTYPE=mRNA
  SEQUENCE    ACC=NM_000015.2; NID=g116295259; PID=g116295260;
SEQTYPE=mRNA
  SEQUENCE    ACC=D90042.1; NID=g219415; PID=g219416; SEQTYPE=mRNA
  SEQUENCE    ACC=D90040.1; NID=g219411; PID=g219412; SEQTYPE=mRNA
  SEQUENCE    ACC=BC015878.1; NID=g16198419; PID=g16198420; SEQTYPE=mRNA
  SEQUENCE    ACC=CR407631.1; NID=g47115198; PID=g47115199; SEQTYPE=mRNA
  SEQUENCE    ACC=BG569293.1; NID=g13576946; CLONE=IMAGE:4722596;
END=5'; LID=6989; SEQTYPE=EST; TRACE=44157214
  ...
  SEQUENCE    ACC=AU099534.1; NID=g13550663; CLONE=HSI08034; END=5';
LID=8800; SEQTYPE=EST
  //
>>

  This particular record shows the set of transcripts (shown in the
SEQUENCE lines) that originate from the human gene NAT2, encoding en
N-acetyltransferase. The PROTSIM lines show proteins with significant
similarity to NAT2, whereas the STS lines show the corresponding
sequence-tagged sites in the genome.
  To parse UniGene files, use the Bio.UniGene module: 
<<>>> from Bio import UniGene
  >>> input = open("myunigenefile.data")
  >>> record = UniGene.read(input)
>>

  The record returned by UniGene.read is a Python object with attributes
corresponding to the fields in the UniGene record. For example, 
<<>>> record.ID
  "Hs.2"
  >>> record.title
  "N-acetyltransferase 2 (arylamine N-acetyltransferase)"
>>

  The EXPRESS and RESTR_EXPR lines are stored as Python lists of
strings: 
<<[
      "bone",
      "connective tissue",
      "intestine",
      "liver",
      "liver tumor",
      "normal",
      "soft tissue/muscle tissue tumor",
      "adult",
  ]
>>

  Specialized objects are returned for the STS, PROTSIM, and SEQUENCE
lines, storing the keys shown in each line as attributes: 
<<>>> record.sts[0].acc
  'PMC310725P3'
  >>> record.sts[0].unists
  '272646'
>>
   and similarly for the PROTSIM and SEQUENCE lines.
  To parse a file containing more than one UniGene record, use the parse
function in Bio.UniGene:
<<>>> from Bio import UniGene
  >>> input = open("unigenerecords.data")
  >>> records = UniGene.parse(input)
  >>> for record in records:
  ...     print(record.ID)
>>

9.14 Using a proxy
*=*=*=*=*=*=*=*=*=

  Normally you won't have to worry about using a proxy, but if this is
an issue on your network here is how to deal with it. Internally,
Bio.Entrez uses the standard Python library urllib for accessing the
NCBI servers. This will check an environment variable called http_proxy
to configure any simple proxy automatically. Unfortunately this module
does not support the use of proxies which require authentication.
  You may choose to set the http_proxy environment variable once (how
you do this will depend on your operating system). Alternatively you can
set this within Python at the start of your script, for example:
<<import os

  os.environ["http_proxy"] = "http://proxyhost.example.com:8080"
>>

  See the urllib documentation (15) for more details.

9.15 Examples
*=*=*=*=*=*=*

9.15.1 PubMed and Medline
=========================

  If you are in the medical field or interested in human issues (and
many times even if you are not!), PubMed
(https://www.ncbi.nlm.nih.gov/PubMed/) is an excellent source of all
kinds of goodies. So like other things, we'd like to be able to grab
information from it and use it in Python scripts.
  In this example, we will query PubMed for all articles having to do
with orchids (see section ?? for our motivation). We first check how
many of such articles there are:
<<>>> from Bio import Entrez
  >>> Entrez.email = "A.N.Other@example.com"  # Always tell NCBI who you
are
  >>> handle = Entrez.egquery(term="orchid")
  >>> record = Entrez.read(handle)
  >>> for row in record["eGQueryResult"]:
  ...     if row["DbName"]=="pubmed":
  ...         print(row["Count"])
  463
>>

  Now we use the Bio.Entrez.efetch function to download the PubMed IDs
of these 463 articles:
<<>>> from Bio import Entrez
  >>> Entrez.email = "A.N.Other@example.com"  # Always tell NCBI who you
are
  >>> handle = Entrez.esearch(db="pubmed", term="orchid", retmax=463)
  >>> record = Entrez.read(handle)
  >>> handle.close()
  >>> idlist = record["IdList"]
>>

  This returns a Python list containing all of the PubMed IDs of
articles related to orchids: 
<<>>> print(idlist)
  ['18680603', '18665331', '18661158', '18627489', '18627452',
'18612381',
  '18594007', '18591784', '18589523', '18579475', '18575811',
'18575690',
  ...
>>

  Now that we've got them, we obviously want to get the corresponding
Medline records and extract the information from them. Here, we'll
download the Medline records in the Medline flat-file format, and use
the Bio.Medline module to parse them:
<<>>> from Bio import Medline
  >>> handle = Entrez.efetch(db="pubmed", id=idlist, rettype="medline",
  ...                        retmode="text")
  >>> records = Medline.parse(handle)
>>

  NOTE - We've just done a separate search and fetch here, the NCBI much
prefer you to take advantage of their history support in this situation.
See Section ??.
  Keep in mind that records is an iterator, so you can iterate through
the records only once. If you want to save the records, you can convert
them to a list:
<<>>> records = list(records)
>>

  Let's now iterate over the records to print out some information about
each record: 
<<>>> for record in records:
  ...     print("title:", record.get("TI", "?"))
  ...     print("authors:", record.get("AU", "?"))
  ...     print("source:", record.get("SO", "?"))
  ...     print("")
  ...
>>

  The output for this looks like: 
<<title: Sex pheromone mimicry in the early spider orchid (ophrys
sphegodes):
  patterns of hydrocarbons as the key mechanism for pollination by
sexual
  deception [In Process Citation]
  authors: ['Schiestl FP', 'Ayasse M', 'Paulus HF', 'Lofstedt C',
'Hansson BS',
  'Ibarra F', 'Francke W']
  source: J Comp Physiol [A] 2000 Jun;186(6):567-74
>>

  Especially interesting to note is the list of authors, which is
returned as a standard Python list. This makes it easy to manipulate and
search using standard Python tools. For instance, we could loop through
a whole bunch of entries searching for a particular author with code
like the following: 
<<>>> search_author = "Waits T"
  >>> for record in records:
  ...     if not "AU" in record:
  ...         continue
  ...     if search_author in record["AU"]:
  ...         print("Author %s found: %s" % (search_author,
record["SO"]))
  ...
>>

  Hopefully this section gave you an idea of the power and flexibility
of the Entrez and Medline interfaces and how they can be used together.

9.15.2 Searching, downloading, and parsing Entrez Nucleotide records
====================================================================

  Here we'll show a simple example of performing a remote Entrez query.
In section ?? of the parsing examples, we talked about using NCBI's
Entrez website to search the NCBI nucleotide databases for info on
Cypripedioideae, our friends the lady slipper orchids. Now, we'll look
at how to automate that process using a Python script. In this example,
we'll just show how to connect, get the results, and parse them, with
the Entrez module doing all of the work.
  First, we use EGQuery to find out the number of results we will get
before actually downloading them. EGQuery will tell us how many search
results were found in each of the databases, but for this example we are
only interested in nucleotides: 
<<>>> from Bio import Entrez
  >>> Entrez.email = "A.N.Other@example.com"  # Always tell NCBI who you
are
  >>> handle = Entrez.egquery(term="Cypripedioideae")
  >>> record = Entrez.read(handle)
  >>> for row in record["eGQueryResult"]:
  ...     if row["DbName"]=="nuccore":
  ...         print(row["Count"])
  4457
>>

  So, we expect to find 4457 Entrez Nucleotide records (this increased
from 814 records in 2008; it is likely to continue to increase in the
future). If you find some ridiculously high number of hits, you may want
to reconsider if you really want to download all of them, which is our
next step.  Let's use the retmax argument to restrict the maximum number
of records retrieved to the number available in 2008:
<<>>> from Bio import Entrez
  >>> Entrez.email = "A.N.Other@example.com"  # Always tell NCBI who you
are
  >>> handle = Entrez.esearch(db="nucleotide", term="Cypripedioideae",
retmax=814, idtype="acc")
  >>> record = Entrez.read(handle)
  >>> handle.close()
>>

  Here, record is a Python dictionary containing the search results and
some auxiliary information. Just for information, let's look at what is
stored in this dictionary: 
<<>>> print(record.keys())
  ['Count', 'RetMax', 'IdList', 'TranslationSet', 'RetStart',
'QueryTranslation']
>>
   First, let's check how many results were found: 
<<>>> print(record["Count"])
  '4457'
>>
   You might have expected this to be 814, the maximum number of records
we asked to retrieve.  However, Count represents the total number of
records available for that search, not how many were retrieved. The
retrieved records are stored in record['IdList'], which should contain
the total number we asked for: 
<<>>> len(record["IdList"])
  814
>>
   Let's look at the first five results: 
<<>>> record["IdList"][:5]
  ['KX265015.1', 'KX265014.1', 'KX265013.1', 'KX265012.1', 'KX265011.1']
>>

   We can download these records using efetch. While you could download
these records one by one, to reduce the load on NCBI's servers, it is
better to fetch a bunch of records at the same time, shown below.
However, in this situation you should ideally be using the history
feature described later in Section ??.
<<>>> idlist = ",".join(record["IdList"][:5])
  >>> print(idlist)
  KX265015.1, KX265014.1, KX265013.1, KX265012.1, KX265011.1]
  >>> handle = Entrez.efetch(db="nucleotide", id=idlist, retmode="xml")
  >>> records = Entrez.read(handle)
  >>> len(records)
  5
>>
   Each of these records corresponds to one GenBank record. 
<<>>> print(records[0].keys())
  ['GBSeq_moltype', 'GBSeq_source', 'GBSeq_sequence',
   'GBSeq_primary-accession', 'GBSeq_definition',
'GBSeq_accession-version',
   'GBSeq_topology', 'GBSeq_length', 'GBSeq_feature-table',
   'GBSeq_create-date', 'GBSeq_other-seqids', 'GBSeq_division',
   'GBSeq_taxonomy', 'GBSeq_references', 'GBSeq_update-date',
   'GBSeq_organism', 'GBSeq_locus', 'GBSeq_strandedness']

  >>> print(records[0]["GBSeq_primary-accession"])
  DQ110336

  >>> print(records[0]["GBSeq_other-seqids"])
  ['gb|DQ110336.1|', 'gi|187237168']

  >>> print(records[0]["GBSeq_definition"])
  Cypripedium calceolus voucher Davis 03-03 A maturase (matR) gene,
partial cds;
  mitochondrial

  >>> print(records[0]["GBSeq_organism"])
  Cypripedium calceolus
>>

  You could use this to quickly set up searches -- but for heavy usage,
see Section ??.

9.15.3 Searching, downloading, and parsing GenBank records
==========================================================

  The GenBank record format is a very popular method of holding
information about sequences, sequence features, and other associated
sequence information. The format is a good way to get information from
the NCBI databases at https://www.ncbi.nlm.nih.gov/.
  In this example we'll show how to query the NCBI databases,to retrieve
the records from the query, and then parse them using Bio.SeqIO -
something touched on in Section ??. For simplicity, this example does
not take advantage of the WebEnv history feature -- see Section ?? for
this.
  First, we want to make a query and find out the ids of the records to
retrieve. Here we'll do a quick search for one of our favorite
organisms, Opuntia (prickly-pear cacti). We can do quick search and get
back the GIs (GenBank identifiers) for all of the corresponding records.
First we check how many records there are:
<<>>> from Bio import Entrez
  >>> Entrez.email = "A.N.Other@example.com"  # Always tell NCBI who you
are
  >>> handle = Entrez.egquery(term="Opuntia AND rpl16")
  >>> record = Entrez.read(handle)
  >>> for row in record["eGQueryResult"]:
  ...     if row["DbName"]=="nuccore":
  ...         print(row["Count"])
  ...
  9
>>
   Now we download the list of GenBank identifiers: 
<<>>> handle = Entrez.esearch(db="nuccore", term="Opuntia AND rpl16")
  >>> record = Entrez.read(handle)
  >>> gi_list = record["IdList"]
  >>> gi_list
  ['57240072', '57240071', '6273287', '6273291', '6273290', '6273289',
'6273286',
  '6273285', '6273284']
>>

  Now we use these GIs to download the GenBank records - note that with
older versions of Biopython you had to supply a comma separated list of
GI numbers to Entrez, as of Biopython 1.59 you can pass a list and this
is converted for you:
<<>>> gi_str = ",".join(gi_list)
  >>> handle = Entrez.efetch(db="nuccore", id=gi_str, rettype="gb",
retmode="text")
>>

  If you want to look at the raw GenBank files, you can read from this
handle and print out the result:
<<>>> text = handle.read()
  >>> print(text)
  LOCUS       AY851612                 892 bp    DNA     linear   PLN
10-APR-2007
  DEFINITION  Opuntia subulata rpl16 gene, intron; chloroplast.
  ACCESSION   AY851612
  VERSION     AY851612.1  GI:57240072
  KEYWORDS    .
  SOURCE      chloroplast Austrocylindropuntia subulata
    ORGANISM  Austrocylindropuntia subulata
              Eukaryota; Viridiplantae; Streptophyta; Embryophyta;
Tracheophyta;
              Spermatophyta; Magnoliophyta; eudicotyledons; core
eudicotyledons;
              Caryophyllales; Cactaceae; Opuntioideae;
Austrocylindropuntia.
  REFERENCE   1  (bases 1 to 892)
    AUTHORS   Butterworth,C.A. and Wallace,R.S.
  ...
>>

  In this case, we are just getting the raw records. To get the records
in a more Python-friendly form, we can use Bio.SeqIO to parse the
GenBank data into SeqRecord objects, including SeqFeature objects (see
Chapter ??):
<<>>> from Bio import SeqIO
  >>> handle = Entrez.efetch(db="nuccore", id=gi_str, rettype="gb",
retmode="text")
  >>> records = SeqIO.parse(handle, "gb")
>>

  We can now step through the records and look at the information we are
interested in: 
<<>>> for record in records:
  >>> ...    print("%s, length %i, with %i features"
  >>> ...           % (record.name, len(record), len(record.features)))
  AY851612, length 892, with 3 features
  AY851611, length 881, with 3 features
  AF191661, length 895, with 3 features
  AF191665, length 902, with 3 features
  AF191664, length 899, with 3 features
  AF191663, length 899, with 3 features
  AF191660, length 893, with 3 features
  AF191659, length 894, with 3 features
  AF191658, length 896, with 3 features
>>

  Using these automated query retrieval functionality is a big plus over
doing things by hand. Although the module should obey the NCBI's max
three queries per second rule, the NCBI have other recommendations like
avoiding peak hours. See Section ??. In particular, please note that for
simplicity, this example does not use the WebEnv history feature. You
should use this for any non-trivial search and download work, see
Section ??.
  Finally, if plan to repeat your analysis, rather than downloading the
files from the NCBI and parsing them immediately (as shown in this
example), you should just download the records once and save them to
your hard disk, and then parse the local file.

9.15.4 Finding the lineage of an organism
=========================================

  Staying with a plant example, let's now find the lineage of the
Cypripedioideae orchid family. First, we search the Taxonomy database
for Cypripedioideae, which yields exactly one NCBI taxonomy identifier:
<<>>> from Bio import Entrez
  >>> Entrez.email = "A.N.Other@example.com"  # Always tell NCBI who you
are
  >>> handle = Entrez.esearch(db="Taxonomy", term="Cypripedioideae")
  >>> record = Entrez.read(handle)
  >>> record["IdList"]
  ['158330']
  >>> record["IdList"][0]
  '158330'
>>
   Now, we use efetch to download this entry in the Taxonomy database,
and then parse it:
<<>>> handle = Entrez.efetch(db="Taxonomy", id="158330", retmode="xml")
  >>> records = Entrez.read(handle)
>>
   Again, this record stores lots of information: 
<<>>> records[0].keys()
  ['Lineage', 'Division', 'ParentTaxId', 'PubDate', 'LineageEx',
   'CreateDate', 'TaxId', 'Rank', 'GeneticCode', 'ScientificName',
   'MitoGeneticCode', 'UpdateDate']
>>
   We can get the lineage directly from this record: 
<<>>> records[0]["Lineage"]
  'cellular organisms; Eukaryota; Viridiplantae; Streptophyta;
Streptophytina;
   Embryophyta; Tracheophyta; Euphyllophyta; Spermatophyta;
Magnoliopsida;
   Liliopsida; Asparagales; Orchidaceae'
>>

  The record data contains much more than just the information shown
here - for example look under "LineageEx" instead of "Lineage" and
you'll get the NCBI taxon identifiers of the lineage entries too.

9.16 Using the history and WebEnv
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*

  Often you will want to make a series of linked queries. Most
typically, running a search, perhaps refining the search, and then
retrieving detailed search results. You can do this by making a series
of separate calls to Entrez. However, the NCBI prefer you to take
advantage of their history support - for example combining ESearch and
EFetch.
  Another typical use of the history support would be to combine EPost
and EFetch. You use EPost to upload a list of identifiers, which starts
a new history session. You then download the records with EFetch by
referring to the session (instead of the identifiers).

9.16.1 Searching for and downloading sequences using the history
================================================================
   Suppose we want to search and download all the Opuntia rpl16
nucleotide sequences, and store them in a FASTA file. As shown in
Section ??, we can naively combine Bio.Entrez.esearch() to get a list of
Accession numbers, and then call Bio.Entrez.efetch() to download them
all.
  However, the approved approach is to run the search with the history
feature. Then, we can fetch the results by reference to the search
results - which the NCBI can anticipate and cache.
  To do this, call Bio.Entrez.esearch() as normal, but with the
additional argument of usehistory="y",
<<>>> from Bio import Entrez
  >>> Entrez.email = "history.user@example.com"  # Always tell NCBI who
you are
  >>> search_handle = Entrez.esearch(db="nucleotide",term="Opuntia[orgn]
and rpl16",
  ...                                usehistory="y", idtype="acc")
  >>> search_results = Entrez.read(search_handle)
  >>> search_handle.close()
>>

  As before (see Section ??), the XML output includes the first retmax
search results, with retmax defaulting to 20:
<<>>> acc_list = search_results["IdList"]
  >>> count = int(search_results["Count"])
  >>> len(acc_list)
  20
>>

<<>>> count
  28
>>

  You also get given two additional pieces of information, the WebEnv
session cookie, and the QueryKey:
<<>>> webenv = search_results["WebEnv"]
  >>> query_key = search_results["QueryKey"]
>>

  Having stored these values in variables session_cookie and query_key
we can use them as parameters to Bio.Entrez.efetch() instead of giving
the GI numbers as identifiers.
  While for small searches you might be OK downloading everything at
once, it is better to download in batches. You use the retstart and
retmax parameters to specify which range of search results you want
returned (starting entry using zero-based counting, and maximum number
of results to return). Note that if Biopython encounters a transient
failure like a HTTP 500 response when communicating with NCBI, it will
automatically try again a couple of times. For example,
<<# This assumes you have already run a search as shown above,
  # and set the variables count, webenv, query_key

  batch_size = 3
  out_handle = open("orchid_rpl16.fasta", "w")
  for start in range(0, count, batch_size):
      end = min(count, start + batch_size)
      print("Going to download record %i to %i" % (start + 1, end))
      fetch_handle = Entrez.efetch(
          db="nucleotide",
          rettype="fasta",
          retmode="text",
          retstart=start,
          retmax=batch_size,
          webenv=webenv,
          query_key=query_key,
          idtype="acc",
      )
      data = fetch_handle.read()
      fetch_handle.close()
      out_handle.write(data)
  out_handle.close()
>>

  For illustrative purposes, this example downloaded the FASTA records
in batches of three. Unless you are downloading genomes or chromosomes,
you would normally pick a larger batch size.

9.16.2 Searching for and downloading abstracts using the history
================================================================
   Here is another history example, searching for papers published in
the last year about the Opuntia, and then downloading them into a file
in MedLine format:
<<from Bio import Entrez

  Entrez.email = "history.user@example.com"
  search_results = Entrez.read(
      Entrez.esearch(
          db="pubmed", term="Opuntia[ORGN]", reldate=365,
datetype="pdat", usehistory="y"
      )
  )
  count = int(search_results["Count"])
  print("Found %i results" % count)

  batch_size = 10
  out_handle = open("recent_orchid_papers.txt", "w")
  for start in range(0, count, batch_size):
      end = min(count, start + batch_size)
      print("Going to download record %i to %i" % (start + 1, end))
      fetch_handle = Entrez.efetch(
          db="pubmed",
          rettype="medline",
          retmode="text",
          retstart=start,
          retmax=batch_size,
          webenv=search_results["WebEnv"],
          query_key=search_results["QueryKey"],
      )
      data = fetch_handle.read()
      fetch_handle.close()
      out_handle.write(data)
  out_handle.close()
>>

  At the time of writing, this gave 28 matches - but because this is a
date dependent search, this will of course vary. As described in
Section ?? above, you can then use Bio.Medline to parse the saved
records.

9.16.3 Searching for citations
==============================

  Back in Section ?? we mentioned ELink can be used to search for
citations of a given paper. Unfortunately this only covers journals
indexed for PubMed Central (doing it for all the journals in PubMed
would mean a lot more work for the NIH). Let's try this for the
Biopython PDB parser paper, PubMed ID 14630660:
<<>>> from Bio import Entrez
  >>> Entrez.email = "A.N.Other@example.com"  # Always tell NCBI who you
are
  >>> pmid = "14630660"
  >>> results = Entrez.read(Entrez.elink(dbfrom="pubmed", db="pmc",
  ...                                    LinkName="pubmed_pmc_refs",
id=pmid))
  >>> pmc_ids = [link["Id"] for link in
results[0]["LinkSetDb"][0]["Link"]]
  >>> pmc_ids
  ['2744707', '2705363', '2682512', ..., '1190160']
>>

  Great - eleven articles. But why hasn't the Biopython application note
been found (PubMed ID 19304878)? Well, as you might have guessed from
the variable names, there are not actually PubMed IDs, but PubMed
Central IDs. Our application note is the third citing paper in that
list, PMCID 2682512.
  So, what if (like me) you'd rather get back a list of PubMed IDs? Well
we can call ELink again to translate them. This becomes a two step
process, so by now you should expect to use the history feature to
accomplish it (Section ??).
  But first, taking the more straightforward approach of making a second
(separate) call to ELink:
<<>>> results2 = Entrez.read(Entrez.elink(dbfrom="pmc", db="pubmed",
LinkName="pmc_pubmed",
  ...                                     id=",".join(pmc_ids)))
  >>> pubmed_ids = [link["Id"] for link in
results2[0]["LinkSetDb"][0]["Link"]]
  >>> pubmed_ids
  ['19698094', '19450287', '19304878', ..., '15985178']
>>

  This time you can immediately spot the Biopython application note as
the third hit (PubMed ID 19304878).
  Now, let's do that all again but with the history ...TODO.
  And finally, don't forget to include your own email address in the
Entrez calls.   
-----------------------------------

 (1) https://www.ncbi.nlm.nih.gov/books/NBK25497/
 (2) https://www.ncbi.nlm.nih.gov/books/NBK25499/#chapter4.ESearch
 (3) https://www.ncbi.nlm.nih.gov/books/NBK25499/#chapter4.EPost
 (4) https://www.ncbi.nlm.nih.gov/books/NBK25499/#chapter4.ESummary
 (5) https://www.ncbi.nlm.nih.gov/books/NBK3837/
 (6) https://www.ncbi.nlm.nih.gov/books/NBK25499/#chapter4.EFetch
 (7) https://www.ncbi.nlm.nih.gov/books/NBK25499/#chapter4.EFetch
 (8) https://www.ncbi.nlm.nih.gov/books/NBK25499/#chapter4.EFetch
 (9) https://www.ncbi.nlm.nih.gov/books/NBK25499/#chapter4.ELink
 (10)
   https://eutils.ncbi.nlm.nih.gov/corehtml/query/static/entrezlinks.htm
   l
 (11) https://www.ncbi.nlm.nih.gov/books/NBK25499/#chapter4.EGQuery
 (12) https://www.ncbi.nlm.nih.gov/books/NBK25499/#chapter4.ESpell
 (13) https://www.ncbi.nlm.nih.gov/books/NBK25499/#chapter4.EFetch
 (14) https://www.ncbi.nlm.nih.gov/geo/
 (15) https://docs.python.org/2/library/urllib.html

Chapter 10  Swiss-Prot and ExPASy
*********************************

10.1 Parsing Swiss-Prot files
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*

  Swiss-Prot (https://web.expasy.org/docs/swiss-prot_guideline.html) is
a hand-curated database of protein sequences. Biopython can parse the
"plain text" Swiss-Prot file format, which is still used for the UniProt
Knowledgebase which combined Swiss-Prot, TrEMBL and PIR-PSD.
  Although in the following we focus on the older human readable plain
text format, Bio.SeqIO can read both this and the newer UniProt XML file
format for annotated protein sequences.

10.1.1 Parsing Swiss-Prot records
=================================

  In Section ??, we described how to extract the sequence of a
Swiss-Prot record as a SeqRecord object. Alternatively, you can store
the Swiss-Prot record in a Bio.SwissProt.Record object, which in fact
stores the complete information contained in the Swiss-Prot record. In
this section, we describe how to extract Bio.SwissProt.Record objects
from a Swiss-Prot file.
  To parse a Swiss-Prot record, we first get a handle to a Swiss-Prot
record. There are several ways to do so, depending on where and how the
Swiss-Prot record is stored: 

   - Open a Swiss-Prot file locally: 
   <<>>> handle = open("myswissprotfile.dat")
   >>

   - Open a gzipped Swiss-Prot file: 
   <<>>> import gzip
     >>> handle = gzip.open("myswissprotfile.dat.gz", "rt")
   >>

   - Open a Swiss-Prot file over the internet: 
   <<>>> from urllib.request import urlopen
     >>> url =
   "https://raw.githubusercontent.com/biopython/biopython/master/Tests/S
   wissProt/F2CXE6.txt"
     >>> handle = urlopen(url)
   >>
  to open the file stored on the Internet before calling read.

   - Open a Swiss-Prot file over the internet from the ExPASy database
   (see section ??): 
   <<>>> from Bio import ExPASy
     >>> handle = ExPASy.get_sprot_raw(myaccessionnumber)
   >>

   The key point is that for the parser, it doesn't matter how the
handle was created, as long as it points to data in the Swiss-Prot
format. The parser will automatically decode the data as ASCII (the
encoding used by Swiss-Prot) if the handle was opened in binary mode.
  We can use Bio.SeqIO as described in Section ?? to get file format
agnostic SeqRecord objects. Alternatively, we can use Bio.SwissProt get
Bio.SwissProt.Record objects, which are a much closer match to the
underlying file format.
  To read one Swiss-Prot record from the handle, we use the function
read(): 
<<>>> from Bio import SwissProt
  >>> record = SwissProt.read(handle)
>>
   This function should be used if the handle points to exactly one
Swiss-Prot record. It raises a ValueError if no Swiss-Prot record was
found, and also if more than one record was found.
  We can now print out some information about this record: 
<<>>> print(record.description)
  SubName: Full=Plasma membrane intrinsic protein
{ECO:0000313|EMBL:BAN04711.1}; SubName: Full=Predicted protein
{ECO:0000313|EMBL:BAJ87517.1};
  >>> for ref in record.references:
  ...     print("authors:", ref.authors)
  ...     print("title:", ref.title)
  ... print(record.organism_classification)
  ...
  authors: Matsumoto T., Tanaka T., Sakai H., Amano N., Kanamori H.,
Kurita K., Kikuta A., Kamiya K., Yamamoto M., Ikawa H., Fujii N., Hori
K., Itoh T., Sato K.
  title: Comprehensive sequence analysis of 24,783 barley full-length
cDNAs derived from 12 clone libraries.
  ['Eukaryota', 'Viridiplantae', 'Streptophyta', 'Embryophyta',
'Tracheophyta', 'Spermatophyta', 'Magnoliophyta', 'Liliopsida',
'Poales', 'Poaceae', 'BEP clade', 'Pooideae', 'Triticeae', 'Hordeum']
  authors: Shibasaka M., Sasano S., Utsugi S., Katsuhara M.
  title: Functional characterization of a novel plasma membrane
intrinsic protein2 in barley.
  ['Eukaryota', 'Viridiplantae', 'Streptophyta', 'Embryophyta',
'Tracheophyta', 'Spermatophyta', 'Magnoliophyta', 'Liliopsida',
'Poales', 'Poaceae', 'BEP clade', 'Pooideae', 'Triticeae', 'Hordeum']
  authors: Shibasaka M., Katsuhara M., Sasano S.
  title: 
  ['Eukaryota', 'Viridiplantae', 'Streptophyta', 'Embryophyta',
'Tracheophyta', 'Spermatophyta', 'Magnoliophyta', 'Liliopsida',
'Poales', 'Poaceae', 'BEP clade', 'Pooideae', 'Triticeae', 'Hordeum']
>>

  To parse a file that contains more than one Swiss-Prot record, we use
the parse function instead. This function allows us to iterate over the
records in the file.
  For example, let's parse the full Swiss-Prot database and collect all
the descriptions. You can download this from the ExPAYs FTP site (1) as
a single gzipped-file uniprot_sprot.dat.gz (about 300MB). This is a
compressed file containing a single file, uniprot_sprot.dat (over
1.5GB).
  As described at the start of this section, you can use the Python
library gzip to open and uncompress a .gz file, like this:
<<>>> import gzip
  >>> handle = gzip.open("uniprot_sprot.dat.gz", "rt")
>>

  However, uncompressing a large file takes time, and each time you open
the file for reading in this way, it has to be decompressed on the fly.
So, if you can spare the disk space you'll save time in the long run if
you first decompress the file to disk, to get the uniprot_sprot.dat file
inside. Then you can open the file for reading as usual:
<<>>> handle = open("uniprot_sprot.dat")
>>

  As of June 2009, the full Swiss-Prot database downloaded from ExPASy
contained 468851 Swiss-Prot records. One concise way to build up a list
of the record descriptions is with a list comprehension: 
<<>>> from Bio import SwissProt
  >>> handle = open("uniprot_sprot.dat")
  >>> descriptions = [record.description for record in
SwissProt.parse(handle)]
  >>> len(descriptions)
  468851
  >>> descriptions[:5]
  ['RecName: Full=Protein MGF 100-1R;',
   'RecName: Full=Protein MGF 100-1R;',
   'RecName: Full=Protein MGF 100-1R;',
   'RecName: Full=Protein MGF 100-1R;',
   'RecName: Full=Protein MGF 100-2L;']

>>

  Or, using a for loop over the record iterator: 
<<>>> from Bio import SwissProt
  >>> descriptions = []
  >>> handle = open("uniprot_sprot.dat")
  >>> for record in SwissProt.parse(handle):
  ...     descriptions.append(record.description)
  ...
  >>> len(descriptions)
  468851
>>

  Because this is such a large input file, either way takes about eleven
minutes on my new desktop computer (using the uncompressed
uniprot_sprot.dat file as input).
  It is equally easy to extract any kind of information you'd like from
Swiss-Prot records. To see the members of a Swiss-Prot record, use 
<<>>> dir(record)
  ['__doc__', '__init__', '__module__', 'accessions',
'annotation_update',
  'comments', 'created', 'cross_references', 'data_class',
'description',
  'entry_name', 'features', 'gene_name', 'host_organism', 'keywords',
  'molecule_type', 'organelle', 'organism', 'organism_classification',
  'references', 'seqinfo', 'sequence', 'sequence_length',
  'sequence_update', 'taxonomy_id']
>>

10.1.2 Parsing the Swiss-Prot keyword and category list
=======================================================

  Swiss-Prot also distributes a file keywlist.txt, which lists the
keywords and categories used in Swiss-Prot. The file contains entries in
the following form:
<<ID   2Fe-2S.
  AC   KW-0001
  DE   Protein which contains at least one 2Fe-2S iron-sulfur cluster: 2
iron
  DE   atoms complexed to 2 inorganic sulfides and 4 sulfur atoms of
  DE   cysteines from the protein.
  SY   Fe2S2; [2Fe-2S] cluster; [Fe2S2] cluster; Fe2/S2 (inorganic)
cluster;
  SY   Di-mu-sulfido-diiron; 2 iron, 2 sulfur cluster binding.
  GO   GO:0051537; 2 iron, 2 sulfur cluster binding
  HI   Ligand: Iron; Iron-sulfur; 2Fe-2S.
  HI   Ligand: Metal-binding; 2Fe-2S.
  CA   Ligand.
  //
  ID   3D-structure.
  AC   KW-0002
  DE   Protein, or part of a protein, whose three-dimensional structure
has
  DE   been resolved experimentally (for example by X-ray
crystallography or
  DE   NMR spectroscopy) and whose coordinates are available in the PDB
  DE   database. Can also be used for theoretical models.
  HI   Technical term: 3D-structure.
  CA   Technical term.
  //
  ID   3Fe-4S.
  ...
>>

  The entries in this file can be parsed by the parse function in the
Bio.SwissProt.KeyWList module. Each entry is then stored as a
Bio.SwissProt.KeyWList.Record, which is a Python dictionary.
<<>>> from Bio.SwissProt import KeyWList
  >>> handle = open("keywlist.txt")
  >>> records = KeyWList.parse(handle)
  >>> for record in records:
  ...     print(record["ID"])
  ...     print(record["DE"])
>>

  This prints 
<<2Fe-2S.
  Protein which contains at least one 2Fe-2S iron-sulfur cluster: 2 iron
atoms
  complexed to 2 inorganic sulfides and 4 sulfur atoms of cysteines from
the
  protein.
  ...
>>

10.2 Parsing Prosite records
*=*=*=*=*=*=*=*=*=*=*=*=*=*=

  Prosite is a database containing protein domains, protein families,
functional sites, as well as the patterns and profiles to recognize
them. Prosite was developed in parallel with Swiss-Prot. In Biopython, a
Prosite record is represented by the Bio.ExPASy.Prosite.Record class,
whose members correspond to the different fields in a Prosite record.
  In general, a Prosite file can contain more than one Prosite records.
For example, the full set of Prosite records, which can be downloaded as
a single file (prosite.dat) from the ExPASy FTP site (2), contains 2073
records (version 20.24 released on 4 December 2007). To parse such a
file, we again make use of an iterator:
<<>>> from Bio.ExPASy import Prosite
  >>> handle = open("myprositefile.dat")
  >>> records = Prosite.parse(handle)
>>

  We can now take the records one at a time and print out some
information. For example, using the file containing the complete Prosite
database, we'd find 
<<>>> from Bio.ExPASy import Prosite
  >>> handle = open("prosite.dat")
  >>> records = Prosite.parse(handle)
  >>> record = next(records)
  >>> record.accession
  'PS00001'
  >>> record.name
  'ASN_GLYCOSYLATION'
  >>> record.pdoc
  'PDOC00001'
  >>> record = next(records)
  >>> record.accession
  'PS00004'
  >>> record.name
  'CAMP_PHOSPHO_SITE'
  >>> record.pdoc
  'PDOC00004'
  >>> record = next(records)
  >>> record.accession
  'PS00005'
  >>> record.name
  'PKC_PHOSPHO_SITE'
  >>> record.pdoc
  'PDOC00005'
>>
   and so on. If you're interested in how many Prosite records there
are, you could use 
<<>>> from Bio.ExPASy import Prosite
  >>> handle = open("prosite.dat")
  >>> records = Prosite.parse(handle)
  >>> n = 0
  >>> for record in records: n+=1
  ...
  >>> n
  2073
>>

  To read exactly one Prosite from the handle, you can use the read
function: 
<<>>> from Bio.ExPASy import Prosite
  >>> handle = open("mysingleprositerecord.dat")
  >>> record = Prosite.read(handle)
>>
   This function raises a ValueError if no Prosite record is found, and
also if more than one Prosite record is found.

10.3 Parsing Prosite documentation records
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=

  In the Prosite example above, the record.pdoc accession numbers
'PDOC00001', 'PDOC00004', 'PDOC00005' and so on refer to Prosite
documentation. The Prosite documentation records are available from
ExPASy as individual files, and as one file (prosite.doc) containing all
Prosite documentation records.
  We use the parser in Bio.ExPASy.Prodoc to parse Prosite documentation
records. For example, to create a list of all accession numbers of
Prosite documentation record, you can use
<<>>> from Bio.ExPASy import Prodoc
  >>> handle = open("prosite.doc")
  >>> records = Prodoc.parse(handle)
  >>> accessions = [record.accession for record in records]
>>

  Again a read() function is provided to read exactly one Prosite
documentation record from the handle.

10.4 Parsing Enzyme records
*=*=*=*=*=*=*=*=*=*=*=*=*=*

  ExPASy's Enzyme database is a repository of information on enzyme
nomenclature. A typical Enzyme record looks as follows:
<<ID   3.1.1.34
  DE   Lipoprotein lipase.
  AN   Clearing factor lipase.
  AN   Diacylglycerol lipase.
  AN   Diglyceride lipase.
  CA   Triacylglycerol + H(2)O = diacylglycerol + a carboxylate.
  CC   -!- Hydrolyzes triacylglycerols in chylomicrons and very
low-density
  CC       lipoproteins (VLDL).
  CC   -!- Also hydrolyzes diacylglycerol.
  PR   PROSITE; PDOC00110;
  DR   P11151, LIPL_BOVIN ;  P11153, LIPL_CAVPO ;  P11602, LIPL_CHICK ;
  DR   P55031, LIPL_FELCA ;  P06858, LIPL_HUMAN ;  P11152, LIPL_MOUSE ;
  DR   O46647, LIPL_MUSVI ;  P49060, LIPL_PAPAN ;  P49923, LIPL_PIG   ;
  DR   Q06000, LIPL_RAT   ;  Q29524, LIPL_SHEEP ;
  //
>>

  In this example, the first line shows the EC (Enzyme Commission)
number of lipoprotein lipase (second line). Alternative names of
lipoprotein lipase are "clearing factor lipase", "diacylglycerol
lipase", and "diglyceride lipase" (lines 3 through 5). The line starting
with "CA" shows the catalytic activity of this enzyme. Comment lines
start with "CC". The "PR" line shows references to the Prosite
Documentation records, and the "DR" lines show references to Swiss-Prot
records. Not of these entries are necessarily present in an Enzyme
record.
  In Biopython, an Enzyme record is represented by the
Bio.ExPASy.Enzyme.Record class. This record derives from a Python
dictionary and has keys corresponding to the two-letter codes used in
Enzyme files. To read an Enzyme file containing one Enzyme record, use
the read function in Bio.ExPASy.Enzyme:
<<>>> from Bio.ExPASy import Enzyme
  >>> with open("lipoprotein.txt") as handle:
  ...     record = Enzyme.read(handle)
  ...
  >>> record["ID"]
  '3.1.1.34'
  >>> record["DE"]
  'Lipoprotein lipase.'
  >>> record["AN"]
  ['Clearing factor lipase.', 'Diacylglycerol lipase.', 'Diglyceride
lipase.']
  >>> record["CA"]
  'Triacylglycerol + H(2)O = diacylglycerol + a carboxylate.'
  >>> record["PR"]
  ['PDOC00110']
>>

<<>>> record["CC"]
  ['Hydrolyzes triacylglycerols in chylomicrons and very low-density
lipoproteins
  (VLDL).', 'Also hydrolyzes diacylglycerol.']
  >>> record["DR"]
  [['P11151', 'LIPL_BOVIN'], ['P11153', 'LIPL_CAVPO'], ['P11602',
'LIPL_CHICK'],
  ['P55031', 'LIPL_FELCA'], ['P06858', 'LIPL_HUMAN'], ['P11152',
'LIPL_MOUSE'],
  ['O46647', 'LIPL_MUSVI'], ['P49060', 'LIPL_PAPAN'], ['P49923',
'LIPL_PIG'],
  ['Q06000', 'LIPL_RAT'], ['Q29524', 'LIPL_SHEEP']]
>>
   The read function raises a ValueError if no Enzyme record is found,
and also if more than one Enzyme record is found.
  The full set of Enzyme records can be downloaded as a single file
(enzyme.dat) from the ExPASy FTP site (3), containing 4877 records
(release of 3 March 2009). To parse such a file containing multiple
Enzyme records, use the parse function in Bio.ExPASy.Enzyme to obtain an
iterator:
<<>>> from Bio.ExPASy import Enzyme
  >>> handle = open("enzyme.dat")
  >>> records = Enzyme.parse(handle)
>>

  We can now iterate over the records one at a time. For example, we can
make a list of all EC numbers for which an Enzyme record is available: 
<<>>> ecnumbers = [record["ID"] for record in records]
>>

10.5 Accessing the ExPASy server
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=

  Swiss-Prot, Prosite, and Prosite documentation records can be
downloaded from the ExPASy web server at https://www.expasy.org. Four
kinds of queries are available from ExPASy: 

 get_prodoc_entry To download a Prosite documentation record in HTML
   format 
 get_prosite_entry To download a Prosite record in HTML format 
 get_prosite_raw To download a Prosite or Prosite documentation record
   in raw format 
 get_sprot_raw To download a Swiss-Prot record in raw format 
   To access this web server from a Python script, we use the Bio.ExPASy
module.

10.5.1 Retrieving a Swiss-Prot record
=====================================

  Let's say we are looking at chalcone synthases for Orchids (see
section ?? for some justification for looking for interesting things
about orchids). Chalcone synthase is involved in flavanoid biosynthesis
in plants, and flavanoids make lots of cool things like pigment colors
and UV protectants.
  If you do a search on Swiss-Prot, you can find three orchid proteins
for Chalcone Synthase, id numbers O23729, O23730, O23731. Now, let's
write a script which grabs these, and parses out some interesting
information.
  First, we grab the records, using the get_sprot_raw() function of
Bio.ExPASy. This function is very nice since you can feed it an id and
get back a handle to a raw text record (no HTML to mess with!). We can
the use Bio.SwissProt.read to pull out the Swiss-Prot record, or
Bio.SeqIO.read to get a SeqRecord. The following code accomplishes what
I just wrote:
<<>>> from Bio import ExPASy
  >>> from Bio import SwissProt

  >>> accessions = ["O23729", "O23730", "O23731"]
  >>> records = []

  >>> for accession in accessions:
  ...     handle = ExPASy.get_sprot_raw(accession)
  ...     record = SwissProt.read(handle)
  ...     records.append(record)
>>

  If the accession number you provided to ExPASy.get_sprot_raw does not
exist, then SwissProt.read(handle) will raise a ValueError. You can
catch ValueException exceptions to detect invalid accession numbers:
<<>>> for accession in accessions:
  ...     handle = ExPASy.get_sprot_raw(accession)
  ...     try:
  ...         record = SwissProt.read(handle)
  ...     except ValueException:
  ...         print("WARNING: Accession %s not found" % accession)
  ...     records.append(record)
>>

10.5.2 Searching Swiss-Prot
===========================

  Now, you may remark that I knew the records' accession numbers
beforehand. Indeed, get_sprot_raw() needs either the entry name or an
accession number. When you don't have them handy, right now you could
use https://www.uniprot.org/ but we do not have a Python wrapper for
searching this from a script. Perhaps you could contribute here?

10.5.3 Retrieving Prosite and Prosite documentation records
===========================================================

  Prosite and Prosite documentation records can be retrieved either in
HTML format, or in raw format. To parse Prosite and Prosite
documentation records with Biopython, you should retrieve the records in
raw format. For other purposes, however, you may be interested in these
records in HTML format.
  To retrieve a Prosite or Prosite documentation record in raw format,
use get_prosite_raw(). For example, to download a Prosite record and
print it out in raw text format, use
<<>>> from Bio import ExPASy
  >>> handle = ExPASy.get_prosite_raw("PS00001")
  >>> text = handle.read()
  >>> print(text)
>>

  To retrieve a Prosite record and parse it into a Bio.Prosite.Record
object, use
<<>>> from Bio import ExPASy
  >>> from Bio import Prosite
  >>> handle = ExPASy.get_prosite_raw("PS00001")
  >>> record = Prosite.read(handle)
>>

  The same function can be used to retrieve a Prosite documentation
record and parse it into a Bio.ExPASy.Prodoc.Record object:
<<>>> from Bio import ExPASy
  >>> from Bio.ExPASy import Prodoc
  >>> handle = ExPASy.get_prosite_raw("PDOC00001")
  >>> record = Prodoc.read(handle)
>>

  For non-existing accession numbers, ExPASy.get_prosite_raw returns a
handle to an empty string. When faced with an empty string, Prosite.read
and Prodoc.read will raise a ValueError. You can catch these exceptions
to detect invalid accession numbers.
  The functions get_prosite_entry() and get_prodoc_entry() are used to
download Prosite and Prosite documentation records in HTML format. To
create a web page showing one Prosite record, you can use
<<>>> from Bio import ExPASy
  >>> handle = ExPASy.get_prosite_entry("PS00001")
  >>> html = handle.read()
  >>> with open("myprositerecord.html", "w") as out_handle:
  ...     out_handle.write(html)
  ...
>>

  and similarly for a Prosite documentation record:
<<>>> from Bio import ExPASy
  >>> handle = ExPASy.get_prodoc_entry("PDOC00001")
  >>> html = handle.read()
  >>> with open("myprodocrecord.html", "w") as out_handle:
  ...     out_handle.write(html)
  ...
>>

  For these functions, an invalid accession number returns an error
message in HTML format.

10.6 Scanning the Prosite database
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=

  ScanProsite (4) allows you to scan protein sequences online against
the Prosite database by providing a UniProt or PDB sequence identifier
or the sequence itself. For more information about ScanProsite, please
see the ScanProsite documentation (5) as well as the documentation for
programmatic access of ScanProsite (6).
  You can use Biopython's Bio.ExPASy.ScanProsite module to scan the
Prosite database from Python. This module both helps you to access
ScanProsite programmatically, and to parse the results returned by
ScanProsite. To scan for Prosite patterns in the following protein
sequence:
<<MEHKEVVLLLLLFLKSGQGEPLDDYVNTQGASLFSVTKKQLGAGSIEECAAKCEEDEEFT
  CRAFQYHSKEQQCVIMAENRKSSIIIRMRDVVLFEKKVYLSECKTGNGKNYRGTMSKTKN
>>

  you can use the following code:
<<>>> sequence =
"MEHKEVVLLLLLFLKSGQGEPLDDYVNTQGASLFSVTKKQLGAGSIEECAAKCEEDEEFT
  CRAFQYHSKEQQCVIMAENRKSSIIIRMRDVVLFEKKVYLSECKTGNGKNYRGTMSKTKN"
  >>> from Bio.ExPASy import ScanProsite
  >>> handle = ScanProsite.scan(seq=sequence)
>>

  By executing handle.read(), you can obtain the search results in raw
XML format. Instead, let's use Bio.ExPASy.ScanProsite.read to parse the
raw XML into a Python object:
<<>>> result = ScanProsite.read(handle)
  >>> type(result)
  <class 'Bio.ExPASy.ScanProsite.Record'>
>>

  A Bio.ExPASy.ScanProsite.Record object is derived from a list, with
each element in the list storing one ScanProsite hit. This object also
stores the number of hits, as well as the number of search sequences, as
returned by ScanProsite. This ScanProsite search resulted in six hits:
<<>>> result.n_seq
  1
  >>> result.n_match
  6
  >>> len(result)
  6
  >>> result[0]
  {'signature_ac': u'PS50948', 'level': u'0', 'stop': 98, 'sequence_ac':
u'USERSEQ1', 'start': 16, 'score': u'8.873'}
  >>> result[1]
  {'start': 37, 'stop': 39, 'sequence_ac': u'USERSEQ1', 'signature_ac':
u'PS00005'}
  >>> result[2]
  {'start': 45, 'stop': 48, 'sequence_ac': u'USERSEQ1', 'signature_ac':
u'PS00006'}
  >>> result[3]
  {'start': 60, 'stop': 62, 'sequence_ac': u'USERSEQ1', 'signature_ac':
u'PS00005'}
  >>> result[4]
  {'start': 80, 'stop': 83, 'sequence_ac': u'USERSEQ1', 'signature_ac':
u'PS00004'}
  >>> result[5]
  {'start': 106, 'stop': 111, 'sequence_ac': u'USERSEQ1',
'signature_ac': u'PS00008'}
>>

  Other ScanProsite parameters can be passed as keyword arguments; see
the documentation for programmatic access of ScanProsite (7) for more
information. As an example, passing lowscore=1 to include matches with
low level scores lets use find one additional hit:
<<>>> handle = ScanProsite.scan(seq=sequence, lowscore=1)
  >>> result = ScanProsite.read(handle)
  >>> result.n_match
  7
>>

-----------------------------------

 (1) ftp://ftp.expasy.org/databases/uniprot/current_release/knowledgebas
   e/complete/uniprot_sprot.dat.gz
 (2) ftp://ftp.expasy.org/databases/prosite/prosite.dat
 (3) ftp://ftp.expasy.org/databases/enzyme/enzyme.dat
 (4) https://prosite.expasy.org/prosite.html
 (5) https://prosite.expasy.org/prosite_doc.html
 (6) https://prosite.expasy.org/scanprosite/scanprosite_doc.html#rest
 (7) https://prosite.expasy.org/scanprosite/scanprosite_doc.html#rest

Chapter 11  Going 3D: The PDB module
************************************

  Bio.PDB is a Biopython module that focuses on working with crystal
structures of biological macromolecules. Among other things, Bio.PDB
includes a PDBParser class that produces a Structure object, which can
be used to access the atomic data in the file in a convenient manner.
There is limited support for parsing the information contained in the
PDB header. PDB file format is no longer being modified or extended to
support new content and PDBx/mmCIF became the standard PDB archive
format in 2014. All the Worldwide Protein Data Bank (wwPDB) sites uses
the macromolecular Crystallographic Information File (mmCIF) data
dictionaries to describe the information content of PDB entries. mmCIF
uses a flexible and extensible key-value pair format for representing
macromolecular structural data and imposes no limitations for the number
of atoms, residues or chains that can be represented in a single PDB
entry (no split entries!).

11.1 Reading and writing crystal structure files
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=

11.1.1 Reading an mmCIF file
============================

  First create an MMCIFParser object:
<<>>> from Bio.PDB.MMCIFParser import MMCIFParser
  >>> parser = MMCIFParser()
>>
   Then use this parser to create a structure object from the mmCIF
file: 
<<>>> structure = parser.get_structure("1fat", "1fat.cif")
>>

  To have some more low level access to an mmCIF file, you can use the
MMCIF2Dict class to create a Python dictionary that maps all mmCIF tags
in an mmCIF file to their values. Whether there are multiple values
(like in the case of tag _atom_site.Cartn_y, which holds the y
coordinates of all atoms) or a single value (like the initial deposition
date), the tag is mapped to a list of values. The dictionary is created
from the mmCIF file as follows:
<<>>> from Bio.PDB.MMCIF2Dict import MMCIF2Dict
  >>> mmcif_dict = MMCIF2Dict("1FAT.cif")
>>

  Example: get the solvent content from an mmCIF file: 
<<>>> sc = mmcif_dict["_exptl_crystal.density_percent_sol"]
>>

  Example: get the list of the y coordinates of all atoms 
<<>>> y_list = mmcif_dict["_atom_site.Cartn_y"]
>>

11.1.2 Reading files in the MMTF format
=======================================

  You can use the direct MMTFParser to read a structure from a file: 
<<>>> from Bio.PDB.mmtf import MMTFParser
  >>> structure = MMTFParser.get_structure("PDB/4CUP.mmtf")
>>

  Or you can use the same class to get a structure by its PDB ID: 
<<>>> structure = MMTFParser.get_structure_from_url("4CUP")
>>

  This gives you a Structure object as if read from a PDB or mmCIF file.
  You can also have access to the underlying data using the external
MMTF library which Biopython is using internally: 
<<>>> from mmtf import fetch
  >>> decoded_data = fetch("4CUP")
>>
   For example you can access just the X-coordinate. 
<<>>> print(decoded_data.x_coord_list)
  ...
>>

11.1.3 Reading a PDB file
=========================

  First we create a PDBParser object:
<<>>> from Bio.PDB.PDBParser import PDBParser
  >>> parser = PDBParser(PERMISSIVE=1)
>>

  The PERMISSIVE flag indicates that a number of common problems (see
??) associated with PDB files will be ignored (but note that some atoms
and/or residues will be missing). If the flag is not present a
PDBConstructionException will be generated if any problems are detected
during the parse operation.
  The Structure object is then produced by letting the PDBParser object
parse a PDB file (the PDB file in this case is called pdb1fat.ent, 1fat
is a user defined name for the structure):
<<>>> structure_id = "1fat"
  >>> filename = "pdb1fat.ent"
  >>> structure = parser.get_structure(structure_id, filename)
>>

  You can extract the header and trailer (simple lists of strings) of
the PDB file from the PDBParser object with the get_header and
get_trailer methods. Note however that many PDB files contain headers
with incomplete or erroneous information. Many of the errors have been
fixed in the equivalent mmCIF files. Hence, if you are interested in the
header information, it is a good idea to extract information from mmCIF
files using the MMCIF2Dict tool described above, instead of parsing the
PDB header. 
  Now that is clarified, let's return to parsing the PDB header. The
structure object has an attribute called header which is a Python
dictionary that maps header records to their values.
  Example:
<<>>> resolution = structure.header["resolution"]
  >>> keywords = structure.header["keywords"]
>>
   The available keys are name, head, deposition_date,  release_date,
structure_method, resolution,  structure_reference (which maps to a list
of references), journal_reference, author, compound (which maps to a
dictionary with various information about the crystallized compound),
has_missing_residues, missing_residues, and astral  (which maps to
dictionary with additional information about the domain if present).
  has_missing_residues maps to a bool that is True if at least one
non-empty REMARK 465 header line was found. In this case you should
assume that the molecule used in the experiment has some  residues for
which no ATOM coordinates could be determined.  missing_residues maps to
a list of dictionaries with information about the missing residues. The
list of missing residues will be empty or incomplete if the PDB header
does not follow the template from the PDB specification.
  The dictionary can also be created without creating a Structure
object, ie. directly from the PDB file:
<<>>> from Bio.PDB import parse_pdb_header
  >>> with open(filename, "r") as handle:
  ...     header_dict = parse_pdb_header(handle)
  ...
>>

11.1.4 Reading a PQR file
=========================

  In order to parse a PQR file, proceed in a similar manner as in the
case of PDB files:
  Create a PDBParser object, using the is_pqr flag:
<<>>> from Bio.PDB.PDBParser import PDBParser
  >>> pqr_parser = PDBParser(PERMISSIVE=1, is_pqr=True)
>>

  The is_pqr flag set to True indicates that the file to be parsed is a
PQR file,  and that the parser should read the atomic charge and radius
fields for each atom entry. Following the same procedure as for PQR
files, a Structure object is then produced, and the PQR file is parsed.
<<>>> structure_id = "1fat"
  >>> filename = "pdb1fat.ent"
  >>> structure = parser.get_structure(structure_id, filename,
is_pqr=True)
>>

11.1.5 Reading files in the PDB XML format
==========================================

  That's not yet supported, but we are definitely planning to support
that in the future (it's not a lot of work). Contact the Biopython
developers via the mailing list if you need this.

11.1.6 Writing mmCIF files
==========================

  The MMCIFIO class can be used to write structures to the mmCIF file
format:
<<>>> io = MMCIFIO()
  >>> io.set_structure(s)
  >>> io.save("out.cif")
>>
   The Select class can be used in a similar way to PDBIO below. mmCIF
dictionaries read using MMCIF2Dict can also be written:
<<>>> io = MMCIFIO()
  >>> io.set_dict(d)
  >>> io.save("out.cif")
>>

11.1.7 Writing PDB files
========================

  Use the PDBIO class for this. It's easy to write out specific parts of
a structure too, of course.
  Example: saving a structure
<<>>> io = PDBIO()
  >>> io.set_structure(s)
  >>> io.save("out.pdb")
>>
   If you want to write out a part of the structure, make use of the
Select class (also in PDBIO). Select has four methods:

   - accept_model(model) 
   - accept_chain(chain) 
   - accept_residue(residue) 
   - accept_atom(atom) 
   By default, every method returns 1 (which means the
model/chain/residue/atom is included in the output). By subclassing
Select and returning 0 when appropriate you can exclude models, chains,
etc. from the output. Cumbersome maybe, but very powerful. The following
code only writes out glycine residues:
<<>>> class GlySelect(Select):
  ...     def accept_residue(self, residue):
  ...         if residue.get_name()=="GLY":
  ...             return True
  ...         else:
  ...             return False
  ...
  >>> io = PDBIO()
  >>> io.set_structure(s)
  >>> io.save("gly_only.pdb", GlySelect())
>>
   If this is all too complicated for you, the Dice module contains a
handy extract function that writes out all residues in a chain between a
start and end residue.

11.1.8 Writing PQR files
========================

  Use the PDBIO class as you would for a PDB file, with the flag
is_pqr=True.  The PDBIO methods can be used in the case of PQR files as
well.
  Example: writing a PQR file
<<>>> io = PDBIO(is_pqr=True)
  >>> io.set_structure(s)
  >>> io.save("out.pdb")
>>

11.1.9 Writing MMTF files
=========================

  To write structures to the MMTF file format:
<<>>> from Bio.PDB.mmtf import MMTFIO
  >>> io = MMTFIO()
  >>> io.set_structure(s)
  >>> io.save("out.mmtf")
>>

  The Select class can be used as above. Note that the bonding
information, secondary structure assignment and some other information
contained in standard MMTF files is not written out as it is not easy to
determine from the structure object. In addition, molecules that are
grouped into the same entity in standard MMTF files are treated as
separate entities by MMTFIO.

11.2 Structure representation
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*

  The overall layout of a Structure object follows the so-called SMCRA
(Structure/Model/Chain/Residue/Atom) architecture:

   - A structure consists of models 
   - A model consists of chains 
   - A chain consists of residues 
   - A residue consists of atoms 
   This is the way many structural biologists/bioinformaticians think
about structure, and provides a simple but efficient way to deal with
structure. Additional stuff is essentially added when needed. A UML
diagram of the Structure object (forget about the Disordered classes for
now) is shown in Fig. ??. Such a data structure is not necessarily best
suited for the representation of the macromolecular content of a
structure, but it is absolutely necessary for a good interpretation of
the data present in a file that describes the structure (typically a PDB
or MMCIF file). If this hierarchy cannot represent the contents of a
structure file, it is fairly certain that the file contains an error or
at least does not describe the structure unambiguously. If a SMCRA data
structure cannot be generated, there is reason to suspect a problem.
Parsing a PDB file can thus be used to detect likely problems. We will
give several examples of this in section ??.
   --------------------------------------------------------------------
     *images/smcra.png*   
     Figure 11.1: UML diagram of SMCRA architecture of the Structure
   class used to represent a macromolecular structure. Full lines with
   diamonds denote aggregation, full lines with arrows denote
   referencing, full lines with triangles denote inheritance and dashed
   lines with triangles denote interface realization.

   --------------------------------------------------------------------

  Structure, Model, Chain and Residue are all subclasses of the Entity
base class. The Atom class only (partly) implements the Entity interface
(because an Atom does not have children).
  For each Entity subclass, you can extract a child by using a unique id
for that child as a key (e.g. you can extract an Atom object from a
Residue object by using an atom name string as a key, you can extract a
Chain object from a Model object by using its chain identifier as a
key).
  Disordered atoms and residues are represented by DisorderedAtom and
DisorderedResidue classes, which are both subclasses of the
DisorderedEntityWrapper base class. They hide the complexity associated
with disorder and behave exactly as Atom and Residue objects.
  In general, a child Entity object (i.e. Atom, Residue, Chain, Model)
can be extracted from its parent (i.e. Residue, Chain, Model, Structure,
respectively) by using an id as a key.
<<>>> child_entity = parent_entity[child_id]
>>

  You can also get a list of all child Entities of a parent Entity
object. Note that this list is sorted in a specific way (e.g. according
to chain identifier for Chain objects in a Model object).
<<>>> child_list = parent_entity.get_list()
>>

  You can also get the parent from a child: 
<<>>> parent_entity = child_entity.get_parent()
>>

  At all levels of the SMCRA hierarchy, you can also extract a full id.
The full id is a tuple containing all id's starting from the top object
(Structure) down to the current object. A full id for a Residue object
e.g. is something like:
<<>>> full_id = residue.get_full_id()
  >>> print(full_id)
  ("1abc", 0, "A", ("", 10, "A"))
>>

  This corresponds to:

   - The Structure with id "1abc" 
   - The Model with id 0 
   - The Chain with id "A" 
   - The Residue with id ("", 10, "A") 
   The Residue id indicates that the residue is not a hetero-residue
(nor a water) because it has a blank hetero field, that its sequence
identifier is 10 and that its insertion code is "A".
  To get the entity's id, use the get_id method: 
<<>>> entity.get_id()
>>
   You can check if the entity has a child with a given id by using the
has_id method: 
<<>>> entity.has_id(entity_id)
>>
   The length of an entity is equal to its number of children: 
<<>>> nr_children = len(entity)
>>

  It is possible to delete, rename, add, etc. child entities from a
parent entity, but this does not include any sanity checks (e.g. it is
possible to add two residues with the same id to one chain). This really
should be done via a nice Decorator class that includes integrity
checking, but you can take a look at the code (Entity.py) if you want to
use the raw interface.

11.2.1 Structure
================

  The Structure object is at the top of the hierarchy. Its id is a user
given string. The Structure contains a number of Model children. Most
crystal structures (but not all) contain a single model, while NMR
structures typically consist of several models. Disorder in crystal
structures of large parts of molecules can also result in several
models.

11.2.2 Model
============

  The id of the Model object is an integer, which is derived from the
position of the model in the parsed file (they are automatically
numbered starting from 0). Crystal structures generally have only one
model (with id 0), while NMR files usually have several models. Whereas
many PDB parsers assume that there is only one model, the Structure
class in Bio.PDB is designed such that it can easily handle PDB files
with more than one model.
  As an example, to get the first model from a Structure object, use 
<<>>> first_model = structure[0]
>>

  The Model object stores a list of Chain children.

11.2.3 Chain
============

  The id of a Chain object is derived from the chain identifier in the
PDB/mmCIF file, and is a single character (typically a letter). Each
Chain in a Model object has a unique id. As an example, to get the Chain
object with identifier "A" from a Model object, use 
<<>>> chain_A = model["A"]
>>

  The Chain object stores a list of Residue children.

11.2.4 Residue
==============

  A residue id is a tuple with three elements:

   - The hetero-field (hetfield): this is 

      - 'W' in the case of a water molecule; 
      - 'H_' followed by the residue name for other hetero residues
      (e.g. 'H_GLC' in the case of a glucose molecule); 
      - blank for standard amino and nucleic acids. 
  This scheme is adopted for reasons described in section ??. 
   - The sequence identifier (resseq), an integer describing the
   position of the residue in the chain (e.g., 100); 
   - The insertion code (icode); a string, e.g. 'A'. The insertion code
   is sometimes used to preserve a certain desirable residue numbering
   scheme. A Ser 80 insertion mutant (inserted e.g. between a Thr 80 and
   an Asn 81 residue) could e.g. have sequence identifiers and insertion
   codes as follows: Thr 80 A, Ser 80 B, Asn 81. In this way the residue
   numbering scheme stays in tune with that of the wild type structure. 
   The id of the above glucose residue would thus be ('H_GLC', 100,
'A'). If the hetero-flag and insertion code are blank, the sequence
identifier alone can be used:
<<# Full id
  >>> residue=chain[(" ", 100, " ")]
  # Shortcut id
  >>> residue=chain[100]
>>
   The reason for the hetero-flag is that many, many PDB files use the
same sequence identifier for an amino acid and a hetero-residue or a
water, which would create obvious problems if the hetero-flag was not
used.
  Unsurprisingly, a Residue object stores a set of Atom children. It
also contains a string that specifies the residue name (e.g. "ASN") and
the segment identifier of the residue (well known to X-PLOR users, but
not used in the construction of the SMCRA data structure).
  Let's look at some examples. Asn 10 with a blank insertion code would
have residue id (' ', 10, ' '). Water 10 would have residue id ('W', 10,
' '). A glucose molecule (a hetero residue with residue name GLC) with
sequence identifier 10 would have residue id ('H_GLC', 10, ' '). In this
way, the three residues (with the same insertion code and sequence
identifier) can be part of the same chain because their residue id's are
distinct.
  In most cases, the hetflag and insertion code fields will be blank,
e.g. (' ', 10, ' '). In these cases, the sequence identifier can be used
as a shortcut for the full id:
<<# use full id
  >>> res10 = chain[(" ", 10, " ")]
  # use shortcut
  >>> res10 = chain[10]
>>

  Each Residue object in a Chain object should have a unique id.
However, disordered residues are dealt with in a special way, as
described in section ??.
  A Residue object has a number of additional methods:
<<>>> residue.get_resname()    # returns the residue name, e.g. "ASN"
  >>> residue.is_disordered()  # returns 1 if the residue has disordered
atoms
  >>> residue.get_segid()      # returns the SEGID, e.g. "CHN1"
  >>> residue.has_id(name)     # test if a residue has a certain atom
>>

  You can use is_aa(residue) to test if a Residue object is an amino
acid.


11.2.5 Atom
===========

  The Atom object stores the data associated with an atom, and has no
children. The id of an atom is its atom name (e.g. "OG" for the side
chain oxygen of a Ser residue). An Atom id needs to be unique in a
Residue. Again, an exception is made for disordered atoms, as described
in section ??.
  The atom id is simply the atom name (eg. 'CA'). In practice, the atom
name is created by stripping all spaces from the atom name in the PDB
file.
  However, in PDB files, a space can be part of an atom name. Often,
calcium atoms are called 'CA..' in order to distinguish them from Calpha
atoms (which are called '.CA.'). In cases were stripping the spaces
would create problems (ie. two atoms called 'CA' in the same residue)
the spaces are kept.
  In a PDB file, an atom name consists of 4 chars, typically with
leading and trailing spaces. Often these spaces can be removed for ease
of use (e.g. an amino acid C alpha  atom is labeled ".CA." in a PDB
file, where the dots represent spaces). To generate an atom name (and
thus an atom id) the spaces are removed, unless this would result in a
name collision in a Residue (i.e. two Atom objects with the same atom
name and id). In the latter case, the atom name including spaces is
tried. This situation can e.g. happen when one residue contains atoms
with names ".CA." and "CA..", although this is not very likely.
  The atomic data stored includes the atom name, the atomic coordinates
(including standard deviation if present), the B factor (including
anisotropic B factors and standard deviation if present), the altloc
specifier and the full atom name including spaces. Less used items like
the atom element number or the atomic charge sometimes specified in a
PDB file are not stored.
  To manipulate the atomic coordinates, use the transform method of the
Atom object. Use the set_coord method to specify the atomic coordinates
directly.
  An Atom object has the following additional methods:
<<>>> a.get_name()       # atom name (spaces stripped, e.g. "CA")
  >>> a.get_id()         # id (equals atom name)
  >>> a.get_coord()      # atomic coordinates
  >>> a.get_vector()     # atomic coordinates as Vector object
  >>> a.get_bfactor()    # isotropic B factor
  >>> a.get_occupancy()  # occupancy
  >>> a.get_altloc()     # alternative location specifier
  >>> a.get_sigatm()     # standard deviation of atomic parameters
  >>> a.get_siguij()     # standard deviation of anisotropic B factor
  >>> a.get_anisou()     # anisotropic B factor
  >>> a.get_fullname()   # atom name (with spaces, e.g. ".CA.")
>>

  To represent the atom coordinates, siguij, anisotropic B factor and
sigatm Numpy arrays are used.
  The get_vector method returns a Vector object representation of the
coordinates of the Atom object, allowing you to do vector operations on
atomic coordinates. Vector implements the full set of 3D vector
operations, matrix multiplication (left and right) and some advanced
rotation-related operations as well.
  As an example of the capabilities of Bio.PDB's Vector module, suppose
that you would like to find the position of a Gly residue's Cbeta atom,
if it had one. Rotating the N atom of the Gly residue along the Calpha-C
bond over -120 degrees roughly puts it in the position of a virtual
Cbeta atom. Here's how to do it, making use of the rotaxis method (which
can be used to construct a rotation around a certain axis) of the Vector
module:
<<# get atom coordinates as vectors
  >>> n = residue["N"].get_vector()
  >>> c = residue["C"].get_vector()
  >>> ca = residue["CA"].get_vector()
  # center at origin
  >>> n = n - ca
  >>> c = c - ca
  # find rotation matrix that rotates n
  # -120 degrees along the ca-c vector
  >>> rot = rotaxis(-pi * 120.0/180.0, c)
  # apply rotation to ca-n vector
  >>> cb_at_origin = n.left_multiply(rot)
  # put on top of ca atom
  >>> cb = cb_at_origin+ca
>>
   This example shows that it's possible to do some quite nontrivial
vector operations on atomic data, which can be quite useful. In addition
to all the usual vector operations (cross (use **), and dot (use *)
product, angle, norm, etc.) and the above mentioned rotaxis function,
the Vector module also has methods to rotate (rotmat) or reflect
(refmat) one vector on top of another.


11.2.6 Extracting a specific Atom/Residue/Chain/Model from a Structure
======================================================================

  These are some examples:
<<>>> model = structure[0]
  >>> chain = model["A"]
  >>> residue = chain[100]
  >>> atom = residue["CA"]
>>
   Note that you can use a shortcut:
<<>>> atom = structure[0]["A"][100]["CA"]
>>



11.3 Disorder
*=*=*=*=*=*=*


  Bio.PDB can handle both disordered atoms and point mutations (i.e. a
Gly and an Ala residue in the same position).


11.3.1 General approach
=======================

  Disorder should be dealt with from two points of view: the atom and
the residue points of view. In general, we have tried to encapsulate all
the complexity that arises from disorder. If you just want to loop over
all Calpha atoms, you do not care that some residues have a disordered
side chain. On the other hand it should also be possible to represent
disorder completely in the data structure. Therefore, disordered atoms
or residues are stored in special objects that behave as if there is no
disorder. This is done by only representing a subset of the disordered
atoms or residues. Which subset is picked (e.g. which of the two
disordered OG side chain atom positions of a Ser residue is used) can be
specified by the user.


11.3.2 Disordered atoms
=======================

  Disordered atoms are represented by ordinary Atom objects, but all
Atom objects that represent the same physical atom are stored in a
DisorderedAtom object (see Fig. ??). Each Atom object in a
DisorderedAtom object can be uniquely indexed using its altloc
specifier. The DisorderedAtom object forwards all uncaught method calls
to the selected Atom object, by default the one that represents the atom
with the highest occupancy. The user can of course change the selected
Atom object, making use of its altloc specifier. In this way atom
disorder is represented correctly without much additional complexity. In
other words, if you are not interested in atom disorder, you will not be
bothered by it.
  Each disordered atom has a characteristic altloc identifier. You can
specify that a DisorderedAtom object should behave like the Atom object
associated with a specific altloc identifier:
<<>>> atom.disordered_select("A") # select altloc A atom
  >>> print(atom.get_altloc())
  "A"
  >>> atom.disordered_select("B") # select altloc B atom
  >>> print(atom.get_altloc())
  "B"
>>



11.3.3 Disordered residues
==========================



Common case
-----------

  The most common case is a residue that contains one or more disordered
atoms. This is evidently solved by using DisorderedAtom objects to
represent the disordered atoms, and storing the DisorderedAtom object in
a Residue object just like ordinary Atom objects. The DisorderedAtom
will behave exactly like an ordinary atom (in fact the atom with the
highest occupancy) by forwarding all uncaught method calls to one of the
Atom objects (the selected Atom object) it contains.


Point mutations
---------------

  A special case arises when disorder is due to a point mutation, i.e.
when two or more point mutants of a polypeptide are present in the
crystal. An example of this can be found in PDB structure 1EN2.
  Since these residues belong to a different residue type (e.g. let's
say Ser 60 and Cys 60) they should not be stored in a single Residue
object as in the common case. In this case, each residue is represented
by one Residue object, and both Residue objects are stored in a single
DisorderedResidue object (see Fig. ??).
  The DisorderedResidue object forwards all uncaught methods to the
selected Residue object (by default the last Residue object added), and
thus behaves like an ordinary residue. Each Residue object in a
DisorderedResidue object can be uniquely identified by its residue name.
In the above example, residue Ser 60 would have id "SER" in the
DisorderedResidue object, while residue Cys 60 would have id "CYS". The
user can select the active Residue object in a DisorderedResidue object
via this id.
  Example: suppose that a chain has a point mutation at position 10,
consisting of a Ser and a Cys residue. Make sure that residue 10 of this
chain behaves as the Cys residue. 
<<>>> residue = chain[10]
  >>> residue.disordered_select("CYS")
>>
   In addition, you can get a list of all Atom objects (ie. all
DisorderedAtom objects are 'unpacked' to their individual Atom objects)
using the get_unpacked_list method of a (Disordered)Residue object.


11.4 Hetero residues
*=*=*=*=*=*=*=*=*=*=




11.4.1 Associated problems
==========================

  A common problem with hetero residues is that several hetero and
non-hetero residues present in the same chain share the same sequence
identifier (and insertion code). Therefore, to generate a unique id for
each hetero residue, waters and other hetero residues are treated in a
different way.
  Remember that Residue object have the tuple (hetfield, resseq, icode)
as id. The hetfield is blank (" ") for amino and nucleic acids, and a
string for waters and other hetero residues. The content of the hetfield
is explained below.


11.4.2 Water residues
=====================

  The hetfield string of a water residue consists of the letter "W". So
a typical residue id for a water is ("W", 1, " ").


11.4.3 Other hetero residues
============================

  The hetfield string for other hetero residues starts with "H_"
followed by the residue name. A glucose molecule e.g. with residue name
"GLC" would have hetfield "H_GLC". Its residue id could e.g. be
("H_GLC", 1, " ").


11.5 Navigating through a Structure object
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=




Parse a PDB file, and extract some Model, Chain, Residue and Atom
=================================================================
objects
=======

<<>>> from Bio.PDB.PDBParser import PDBParser
  >>> parser = PDBParser()
  >>> structure = parser.get_structure("test", "1fat.pdb")
  >>> model = structure[0]
  >>> chain = model["A"]
  >>> residue = chain[1]
  >>> atom = residue["CA"]
>>



Iterating through all atoms of a structure
==========================================

<<>>> p = PDBParser()
  >>> structure = p.get_structure("X", "pdb1fat.ent")
  >>> for model in structure:
  ...     for chain in model:
  ...         for residue in chain:
  ...             for atom in residue:
  ...                 print(atom)
  ...
>>

  There is a shortcut if you want to iterate over all atoms in a
structure: 
<<>>> atoms = structure.get_atoms()
  >>> for atom in atoms:
  ...     print(atom)
  ...
>>

  Similarly, to iterate over all atoms in a chain, use 
<<>>> atoms = chain.get_atoms()
  >>> for atom in atoms:
  ...     print(atom)
  ...
>>



Iterating over all residues of a model
======================================

  or if you want to iterate over all residues in a model: 
<<>>> residues = model.get_residues()
  >>> for residue in residues:
  ...     print(residue)
  ...
>>

  You can also use the Selection.unfold_entities function to get all
residues from a structure: 
<<>>> res_list = Selection.unfold_entities(structure, "R")
>>
   or to get all atoms from a chain: 
<<>>> atom_list = Selection.unfold_entities(chain, "A")
>>
   Obviously, A=atom, R=residue, C=chain, M=model, S=structure. You can
use this to go up in the hierarchy, e.g. to get a list of (unique)
Residue or Chain parents from a list of Atoms:
<<>>> residue_list = Selection.unfold_entities(atom_list, "R")
  >>> chain_list = Selection.unfold_entities(atom_list, "C")
>>
   For more info, see the API documentation.


Extract a hetero residue from a chain (e.g. a glucose (GLC) moiety with
=======================================================================
resseq 10)
==========

<<>>> residue_id = ("H_GLC", 10, " ")
  >>> residue = chain[residue_id]
>>



Print all hetero residues in chain
==================================

<<>>> for residue in chain.get_list():
  ...    residue_id = residue.get_id()
  ...    hetfield = residue_id[0]
  ...    if hetfield[0]=="H":
  ...        print(residue_id)
  ...
>>



Print out the coordinates of all CA atoms in a structure with B factor
======================================================================
greater than 50
===============

<<>>> for model in structure.get_list():
  ...     for chain in model.get_list():
  ...         for residue in chain.get_list():
  ...             if residue.has_id("CA"):
  ...                 ca = residue["CA"]
  ...                 if ca.get_bfactor() > 50.0:
  ...                     print(ca.get_coord())
  ...
>>



Print out all the residues that contain disordered atoms
========================================================

<<>>> for model in structure.get_list():
  ...     for chain in model.get_list():
  ...         for residue in chain.get_list():
  ...             if residue.is_disordered():
  ...                 resseq = residue.get_id()[1]
  ...                 resname = residue.get_resname()
  ...                 model_id = model.get_id()
  ...                 chain_id = chain.get_id()
  ...                 print(model_id, chain_id, resname, resseq)
  ...
>>



Loop over all disordered atoms, and select all atoms with altloc A (if
======================================================================
present)
========
   This will make sure that the SMCRA data structure will behave as if
only the atoms with altloc A are present.
<<>>> for model in structure.get_list():
  ...     for chain in model.get_list():
  ...         for residue in chain.get_list():
  ...             if residue.is_disordered():
  ...                 for atom in residue.get_list():
  ...                     if atom.is_disordered():
  ...                         if atom.disordered_has_id("A"):
  ...                             atom.disordered_select("A")
  ...
>>



Extracting polypeptides from a Structure object
===============================================

  To extract polypeptides from a structure, construct a list of
Polypeptide objects from a Structure object using PolypeptideBuilder as
follows:
<<>>> model_nr = 1
  >>> polypeptide_list = build_peptides(structure, model_nr)
  >>> for polypeptide in polypeptide_list:
  ...     print(polypeptide)
  ...
>>

  A Polypeptide object is simply a UserList of Residue objects, and is
always created from a single Model (in this case model 1). You can use
the resulting Polypeptide object to get the sequence as a Seq object or
to get a list of Calpha atoms as well. Polypeptides can be built using a
C-N or a Calpha-Calpha distance criterion.
  Example:
<<# Using C-N
  >>> ppb=PPBuilder()
  >>> for pp in ppb.build_peptides(structure):
  ...     print(pp.get_sequence())
  ...
  # Using CA-CA
  >>> ppb=CaPPBuilder()
  >>> for pp in ppb.build_peptides(structure):
  ...     print(pp.get_sequence())
  ...
>>
   Note that in the above case only model 0 of the structure is
considered by PolypeptideBuilder. However, it is possible to use
PolypeptideBuilder to build Polypeptide objects from Model and Chain
objects as well.


Obtaining the sequence of a structure
=====================================

  The first thing to do is to extract all polypeptides from the
structure (as above). The sequence of each polypeptide can then easily
be obtained from the Polypeptide objects. The sequence is represented as
a Biopython Seq object.
  Example:
<<>>> seq = polypeptide.get_sequence()
  >>> seq
  Seq('SNDIYFNFQRFNETNLILQRDASVSSSGQLRLTNLN')
>>



11.6 Analyzing structures
*=*=*=*=*=*=*=*=*=*=*=*=*




11.6.1 Measuring distances
==========================
   The minus operator for atoms has been overloaded to return the
distance between two atoms. 
<<# Get some atoms
  >>> ca1 = residue1["CA"]
  >>> ca2 = residue2["CA"]
  # Simply subtract the atoms to get their distance
  >>> distance = ca1-ca2
>>



11.6.2 Measuring angles
=======================
   Use the vector representation of the atomic coordinates, and the
calc_angle function from the Vector module: 
<<>>> vector1 = atom1.get_vector()
  >>> vector2 = atom2.get_vector()
  >>> vector3 = atom3.get_vector()
  >>> angle = calc_angle(vector1, vector2, vector3)
>>



11.6.3 Measuring torsion angles
===============================
   Use the vector representation of the atomic coordinates, and the
calc_dihedral function from the Vector module: 
<<>>> vector1 = atom1.get_vector()
  >>> vector2 = atom2.get_vector()
  >>> vector3 = atom3.get_vector()
  >>> vector4 = atom4.get_vector()
  >>> angle = calc_dihedral(vector1, vector2, vector3, vector4)
>>



11.6.4 Internal coordinates for standard residues
=================================================
   The internal_coords module is provided to facilitate working with
canonical bond lengths, angles and torsion angles for a standard
protein. atom_to_internal_coordinates() for  Structure, Model, and Chain
entities will extend Chain and Residue classes with internal_coord
attributes referencing  IC_Chain and IC_Residue classes respectively.
IC_Residue provides get_angle() and get_length() to query the computed
values with various specifiers and synonyms: 
<<>>> model.atom_to_internal_coordinates()
  >>> for r in model.get_residues():
  ...     if r.internal_coord:
  ...         print(
  ...             r,
  ...             r.internal_coord.get_angle("psi"),
  ...             r.internal_coord.get_angle("phi"),
  ...             r.internal_coord.get_angle("omega"),  # or "omg"
  ...             r.internal_coord.get_angle("chi2"),
  ...             r.internal_coord.get_angle("CB:CA:C"),
  ...             (r.internal_coord.get_length("-1C:0N")  # i-1 to i
peptide bond
  ...               if r.internal_coord.rprev else "None")
  ...         )
>>
   Note that only angles, dihedral angles and residue configurations
specified in ic_data.py are computed,  however these data structures can
be extended to add support for HETATMS as needed.
  Corresponding set_angle() and set_length() routines are also provided,
and the atom coordinates  may be updated using
internal_to_atom_coordinates().
  Missing atoms will cause problems for rebuilding structures from
internal coordinates, however chain breaks and disordered residues and
atoms as described above are handled correctly.
structure_rebuild_test(entity)  will compare a structure to a copy built
from internal coordinates and return a report of success or failure as a
dictionary.
  An entity specification consisting of only internal coordinates (and
optional positioning information) may be exported as a .pic file with
write_PIC(). This format uses 3- and 4-tuples of AtomKeys to specify
3-atom hedra and 4-atom dihedra geometries. AtomKeys consist of up to
six fields, capturing residue position, insertion  code, residue name,
atom name, altloc and occupancy. A .pic file includes sufficient
information to regenerate the ATOM records of a .pdb file.
  The internal_to_atom_coords() assembly algorithm is also implemented
in OpenSCAD (http://www.openscad.org/), a language for describing 3D
solid CAD models. write_SCAD() can thus generate a protein model
suitable for rendering  on a 3D printer, however the printing process is
non-trivial and it is likely that you will want to modify the .scad file
 for your own purposes, just as you might select different rendering
options for a 3D protein visualizer. A modified example  is available at
https://www.thingiverse.com/thing:3957471.
  A few control attributes are available in the internal_coords classes
to modify or filter data as internal coordinates are calculated. These
are listed in Table ??,
   --------------------------------------------------------------------
    	
   ---------------------------------------------------------------------
   -----------------------------
   |  		Class   |   Attribute   |         Default         |             
      Effect                  |
   ---------------------------------------------------------------------
   -----------------------------
   ---------------------------------------------------------------------
   -----------------------------
   | 		AtomKey  |      d2h      |          False          |      Convert
   D atoms to H if True       |
   ---------------------------------------------------------------------
   -----------------------------
   | 		IC_Chain |MaxPeptideBond |           1.4           |    Max C-N
   length w/o chain break;      |
   |     		     |               |                         |    make
   large to link over missing      |
   |     		     |               |                         |        
   residues for 3D models          |
   ---------------------------------------------------------------------
   -----------------------------
   |		IC_Residue| accept_atoms  |mainchain, hydrogen atoms| override to
   remove some or all H's, D's |
   ---------------------------------------------------------------------
   -----------------------------
   |     		     |accept_resnames|      CYG, YCM, UNK      | 3-letter
   names for HETATMs to process,  |
   |     		     |               |                         |backbone only
   unless added to ic_data.py |
   ---------------------------------------------------------------------
   -----------------------------
   |     		     |   gly_Cbeta   |          False          |  override to
   generate Gly Cbeta atoms   |
   |     		     |               |                         |       based
   on database averages        |
   ---------------------------------------------------------------------
   -----------------------------
   |     	      |                                                       

    	
     Table 11.1: Control attributes in Bio.PDB.internal_coords.
    	 
   --------------------------------------------------------------------



11.6.5 Determining atom-atom contacts
=====================================

  Use NeighborSearch to perform neighbor lookup. The neighbor lookup is
done using a KD tree module written in C (see the KDTree class in module
Bio.PDB.kdtrees), making it very fast. It also includes a fast method to
find all point pairs within a certain distance of each other.


11.6.6 Superimposing two structures
===================================

  Use a Superimposer object to superimpose two coordinate sets. This
object calculates the rotation and translation matrix that rotates two
lists of atoms on top of each other in such a way that their RMSD is
minimized. Of course, the two lists need to contain the same number of
atoms. The Superimposer object can also apply the rotation/translation
to a list of atoms. The rotation and translation are stored as a tuple
in the rotran attribute of the Superimposer object (note that the
rotation is right multiplying!). The RMSD is stored in the rmsd
attribute.
  The algorithm used by Superimposer comes from [, Golub & Van Loan] and
makes use of singular value decomposition (this is implemented in the
general Bio.SVDSuperimposer module).
  Example:
<<>>> sup = Superimposer()
  # Specify the atom lists
  # 'fixed' and 'moving' are lists of Atom objects
  # The moving atoms will be put on the fixed atoms
  >>> sup.set_atoms(fixed, moving)
  # Print rotation/translation/rmsd
  >>> print(sup.rotran)
  >>> print(sup.rms)
  # Apply rotation/translation to the moving atoms
  >>> sup.apply(moving)
>>

  To superimpose two structures based on their active sites, use the
active site atoms to calculate the rotation/translation matrices (as
above), and apply these to the whole molecule.


11.6.7 Mapping the residues of two related structures onto each other
=====================================================================

  First, create an alignment file in FASTA format, then use the
StructureAlignment class. This class can also be used for alignments
with more than two structures.


11.6.8 Calculating the Half Sphere Exposure
===========================================

  Half Sphere Exposure (HSE) is a new, 2D measure of solvent exposure
[]. Basically, it counts the number of Calpha atoms around a residue in
the direction of its side chain, and in the opposite direction (within a
radius of 13 AA). Despite its simplicity, it outperforms many other
measures of solvent exposure.
  HSE comes in two flavors: HSEalpha and HSEbeta. The former only uses
the Calpha atom positions, while the latter uses the Calpha and Cbeta
atom positions. The HSE measure is calculated by the HSExposure class,
which can also calculate the contact number. The latter class has
methods which return dictionaries that map a Residue object to its
corresponding HSEalpha, HSEbeta and contact number values.
  Example:
<<>>> model = structure[0]
  >>> hse = HSExposure()
  # Calculate HSEalpha
  >>> exp_ca = hse.calc_hs_exposure(model, option="CA3")
  # Calculate HSEbeta
  >>> exp_cb=hse.calc_hs_exposure(model, option="CB")
  # Calculate classical coordination number
  >>> exp_fs = hse.calc_fs_exposure(model)
  # Print HSEalpha for a residue
  >>> print(exp_ca[some_residue])
>>



11.6.9 Determining the secondary structure
==========================================

  For this functionality, you need to install DSSP (and obtain a license
for it --- free for academic use, see
https://swift.cmbi.umcn.nl/gv/dssp/). Then use the DSSP class, which
maps Residue objects to their secondary structure (and accessible
surface area). The DSSP codes are listed in Table ??. Note that DSSP
(the program, and thus by consequence the class) cannot handle multiple
models!
   --------------------------------------------------------------------

   ------------------------------------
   |Code|    Secondary structure      |
   ------------------------------------
   ------------------------------------
   | H  |        alpha-helix          |
   ------------------------------------
   | B  |Isolated beta-bridge residue |
   ------------------------------------
   | E  |           Strand            |
   ------------------------------------
   | G  |         3-10 helix          |
   ------------------------------------
   | I  |          Pi-helix           |
   ------------------------------------
   | T  |            Turn             |
   ------------------------------------
   | S  |            Bend             |
   ------------------------------------
   | -  |            Other            |
   ------------------------------------

     Table 11.2: DSSP codes in Bio.PDB.

   --------------------------------------------------------------------

  The DSSP class can also be used to calculate the accessible surface
area of a residue. But see also section ??.


11.6.10 Calculating the residue depth
=====================================

  Residue depth is the average distance of a residue's atoms from the
solvent accessible surface. It's a fairly new and very powerful
parameterization of solvent accessibility. For this functionality, you
need to install Michel Sanner's MSMS program
(https://www.scripps.edu/sanner/html/msms_home.html). Then use the
ResidueDepth class. This class behaves as a dictionary which maps
Residue objects to corresponding (residue depth, Calpha depth) tuples.
The Calpha depth is the distance of a residue's Calpha atom to the
solvent accessible surface.
  Example:
<<>>> model = structure[0]
  >>> rd = ResidueDepth(model, pdb_file)
  >>> residue_depth, ca_depth=rd[some_residue]
>>
   You can also get access to the molecular surface itself (via the
get_surface function), in the form of a Numeric Python array with the
surface points.


11.7 Common problems in PDB files
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*


  It is well known that many PDB files contain semantic errors (not the
structures themselves, but their representation in PDB files). Bio.PDB
tries to handle this in two ways. The PDBParser object can behave in two
ways: a restrictive way and a permissive way, which is the default.
  Example:
<<# Permissive parser
  >>> parser = PDBParser(PERMISSIVE=1)
  >>> parser = PDBParser() # The same (default)
  # Strict parser
  >>> strict_parser = PDBParser(PERMISSIVE=0)
>>
   In the permissive state (DEFAULT), PDB files that obviously contain
errors are "corrected" (i.e. some residues or atoms are left out). These
errors include:


   - Multiple residues with the same identifier 
   - Multiple atoms with the same identifier (taking into account the
   altloc identifier) 
   These errors indicate real problems in the PDB file (for details see
[, Hamelryck and Manderick, 2003]). In the restrictive state, PDB files
with errors cause an exception to occur. This is useful to find errors
in PDB files.
  Some errors however are automatically corrected. Normally each
disordered atom should have a non-blank altloc identifier. However,
there are many structures that do not follow this convention, and have a
blank and a non-blank identifier for two disordered positions of the
same atom. This is automatically interpreted in the right way.
  Sometimes a structure contains a list of residues belonging to chain
A, followed by residues belonging to chain B, and again followed by
residues belonging to chain A, i.e. the chains are 'broken'. This is
also correctly interpreted.


11.7.1 Examples
===============

  The PDBParser/Structure class was tested on about 800 structures (each
belonging to a unique SCOP superfamily). This takes about 20 minutes, or
on average 1.5 seconds per structure. Parsing the structure of the large
ribosomal subunit (1FKK), which contains about 64000 atoms, takes 10
seconds on a 1000 MHz PC.
  Three exceptions were generated in cases where an unambiguous data
structure could not be built. In all three cases, the likely cause is an
error in the PDB file that should be corrected. Generating an exception
in these cases is much better than running the chance of incorrectly
describing the structure in a data structure.


11.7.1.1 Duplicate residues
---------------------------

  One structure contains two amino acid residues in one chain with the
same sequence identifier (resseq 3) and icode. Upon inspection it was
found that this chain contains the residues Thr A3, ..., Gly A202, Leu
A3, Glu A204. Clearly, Leu A3 should be Leu A203. A couple of similar
situations exist for structure 1FFK (which e.g. contains Gly B64, Met
B65, Glu B65, Thr B67, i.e. residue Glu B65 should be Glu B66).


11.7.1.2 Duplicate atoms
------------------------

  Structure 1EJG contains a Ser/Pro point mutation in chain A at
position 22. In turn, Ser 22 contains some disordered atoms. As
expected, all atoms belonging to Ser 22 have a non-blank altloc
specifier (B or C). All atoms of Pro 22 have altloc A, except the N atom
which has a blank altloc. This generates an exception, because all atoms
belonging to two residues at a point mutation should have non-blank
altloc. It turns out that this atom is probably shared by Ser and Pro
22, as Ser 22 misses the N atom. Again, this points to a problem in the
file: the N atom should be present in both the Ser and the Pro residue,
in both cases associated with a suitable altloc identifier.


11.7.2 Automatic correction
===========================

  Some errors are quite common and can be easily corrected without much
risk of making a wrong interpretation. These cases are listed below.


11.7.2.1 A blank altloc for a disordered atom
---------------------------------------------

  Normally each disordered atom should have a non-blank altloc
identifier. However, there are many structures that do not follow this
convention, and have a blank and a non-blank identifier for two
disordered positions of the same atom. This is automatically interpreted
in the right way.


11.7.2.2 Broken chains
----------------------

  Sometimes a structure contains a list of residues belonging to chain
A, followed by residues belonging to chain B, and again followed by
residues belonging to chain A, i.e. the chains are "broken". This is
correctly interpreted.


11.7.3 Fatal errors
===================

  Sometimes a PDB file cannot be unambiguously interpreted. Rather than
guessing and risking a mistake, an exception is generated, and the user
is expected to correct the PDB file. These cases are listed below.


11.7.3.1 Duplicate residues
---------------------------

  All residues in a chain should have a unique id. This id is generated
based on:


   - The sequence identifier (resseq). 
   - The insertion code (icode). 
   - The hetfield string ("W" for waters and "H_" followed by the
   residue name for other hetero residues) 
   - The residue names of the residues in the case of point mutations
   (to store the Residue objects in a DisorderedResidue object). 
   If this does not lead to a unique id something is quite likely wrong,
and an exception is generated.


11.7.3.2 Duplicate atoms
------------------------

  All atoms in a residue should have a unique id. This id is generated
based on:


   - The atom name (without spaces, or with spaces if a problem arises).

   - The altloc specifier. 
   If this does not lead to a unique id something is quite likely wrong,
and an exception is generated.


11.8 Accessing the Protein Data Bank
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=




11.8.1 Downloading structures from the Protein Data Bank
========================================================

  Structures can be downloaded from the PDB (Protein Data Bank) by using
the retrieve_pdb_file method on a PDBList object. The argument for this
method is the PDB identifier of the structure.
<<>>> pdbl = PDBList()
  >>> pdbl.retrieve_pdb_file("1FAT")
>>

  The PDBList class can also be used as a command-line tool: 
<<python PDBList.py 1fat
>>
   The downloaded file will be called pdb1fat.ent and stored in the
current working directory. Note that the retrieve_pdb_file method also
has an optional argument pdir that specifies a specific directory in
which to store the downloaded PDB files.
  The retrieve_pdb_file method also has some options to specify the
compression format used for the download, and the program used for local
decompression (default .Z format and gunzip). In addition, the PDB ftp
site can be specified upon creation of the PDBList object. By default,
the server of the Worldwide Protein Data Bank
(ftp://ftp.wwpdb.org/pub/pdb/data/structures/divided/pdb/) is used. See
the API documentation for more details. Thanks again to Kristian Rother
for donating this module.


11.8.2 Downloading the entire PDB
=================================

  The following commands will store all PDB files in the /data/pdb
directory:
<<python PDBList.py all /data/pdb

  python PDBList.py all /data/pdb -d
>>
   The API method for this is called download_entire_pdb. Adding the -d
option will store all files in the same directory. Otherwise, they are
sorted into PDB-style subdirectories according to their PDB ID's.
Depending on the traffic, a complete download will take 2-4 days.


11.8.3 Keeping a local copy of the PDB up to date
=================================================

  This can also be done using the PDBList object. One simply creates a
PDBList object (specifying the directory where the local copy of the PDB
is present) and calls the update_pdb method:
<<>>> pl = PDBList(pdb="/data/pdb")
  >>> pl.update_pdb()
>>
   One can of course make a weekly cronjob out of this to keep the local
copy automatically up-to-date. The PDB ftp site can also be specified
(see API documentation).
  PDBList has some additional methods that can be of use. The
get_all_obsolete method can be used to get a list of all obsolete PDB
entries. The changed_this_week method can be used to obtain the entries
that were added, modified or obsoleted during the current week. For more
info on the possibilities of PDBList, see the API documentation.


11.9 General questions
*=*=*=*=*=*=*=*=*=*=*=




11.9.1 How well tested is Bio.PDB?
==================================

  Pretty well, actually. Bio.PDB has been extensively tested on nearly
5500 structures from the PDB - all structures seemed to be parsed
correctly. More details can be found in the Bio.PDB Bioinformatics
article. Bio.PDB has been used/is being used in many research projects
as a reliable tool. In fact, I'm using Bio.PDB almost daily for research
purposes and continue working on improving it and adding new features.


11.9.2 How fast is it?
======================

  The PDBParser performance was tested on about 800 structures (each
belonging to a unique SCOP superfamily). This takes about 20 minutes, or
on average 1.5 seconds per structure. Parsing the structure of the large
ribosomal subunit (1FKK), which contains about 64000 atoms, takes 10
seconds on a 1000 MHz PC. In short: it's more than fast enough for many
applications.


11.9.3 Is there support for molecular graphics?
===============================================

  Not directly, mostly since there are quite a few Python based/Python
aware solutions already, that can potentially be used with Bio.PDB. My
choice is Pymol, BTW (I've used this successfully with Bio.PDB, and
there will probably be specific PyMol modules in Bio.PDB soon/some day).
Python based/aware molecular graphics solutions include:


   - PyMol: https://pymol.org/ 
   - Chimera: https://www.cgl.ucsf.edu/chimera/ 
   - PMV: http://www.scripps.edu/~sanner/python/ 
   - Coot: https://www2.mrc-lmb.cam.ac.uk/personal/pemsley/coot/ 
   - CCP4mg: http://www.ccp4.ac.uk/MG/ 
   - mmLib: http://pymmlib.sourceforge.net/ 
   - VMD: https://www.ks.uiuc.edu/Research/vmd/ 
   - MMTK: http://dirac.cnrs-orleans.fr/MMTK/ 



11.9.4 Who's using Bio.PDB?
===========================

  Bio.PDB was used in the construction of DISEMBL, a web server that
predicts disordered regions in proteins (http://dis.embl.de/). Bio.PDB
has also been used to perform a large scale search for active sites
similarities between protein structures in the PDB [, Hamelryck, 2003],
and to develop a new algorithm that identifies linear secondary
structure elements [, Majumdar et al., 2005].
  Judging from requests for features and information, Bio.PDB is also
used by several LPCs (Large Pharmaceutical Companies :-).


Chapter 12  Bio.PopGen: Population genetics
*******************************************

  Bio.PopGen is a Biopython module supporting population genetics,
available in Biopython 1.44 onwards. The objective for the module is to
support widely used data formats, applications and databases.


12.1 GenePop
*=*=*=*=*=*=


  GenePop (http://genepop.curtin.edu.au/) is a popular population
genetics software package supporting Hardy-Weinberg tests, linkage
desiquilibrium, population diferentiation, basic statistics, F_st and
migration estimates, among others. GenePop does not supply sequence
based statistics as it doesn't handle sequence data. The GenePop file
format is supported by a wide range of other population genetic software
applications, thus making it a relevant format in the population
genetics field.
  Bio.PopGen provides a parser and generator of GenePop file format.
Utilities to manipulate the content of a record are also provided. Here
is an example on how to read a GenePop file (you can find example
GenePop data files in the Test/PopGen directory of Biopython):
<<from Bio.PopGen import GenePop

  with open("example.gen") as handle:
      rec = GenePop.read(handle)
>>

  This will read a file called example.gen and parse it. If you do print
rec, the record will be output again, in GenePop format.
  The most important information in rec will be the loci names and
population information (but there is more -- use help(GenePop.Record) to
check the API documentation). Loci names can be found on rec.loci_list.
Population information can be found on rec.populations. Populations is a
list with one element per population. Each element is itself a list of
individuals, each individual is a pair composed by individual name and a
list of alleles (2 per marker), here is an example for rec.populations:
<<[
      [
          ("Ind1", [(1, 2), (3, 3), (200, 201)]),
          ("Ind2", [(2, None), (3, 3), (None, None)]),
      ],
      [("Other1", [(1, 1), (4, 3), (200, 200)]),],
  ]
>>

  So we have two populations, the first with two individuals, the second
with only one. The first individual of the first population is called
Ind1, allelic information for each of the 3 loci follows. Please note
that for any locus, information might be missing (see as an example,
Ind2 above).
  A few utility functions to manipulate GenePop records are made
available, here is an example:
<<from Bio.PopGen import GenePop

  # Imagine that you have loaded rec, as per the code snippet above...

  rec.remove_population(pos)
  # Removes a population from a record, pos is the population position
in
  # rec.populations, remember that it starts on position 0.
  # rec is altered.

  rec.remove_locus_by_position(pos)
  # Removes a locus by its position, pos is the locus position in
  #  rec.loci_list, remember that it starts on position 0.
  #  rec is altered.

  rec.remove_locus_by_name(name)
  # Removes a locus by its name, name is the locus name as in
  # rec.loci_list. If the name doesn't exist the function fails
  # silently.
  # rec is altered.

  rec_loci = rec.split_in_loci()
  # Splits a record in loci, that is, for each loci, it creates a new
  # record, with a single loci and all populations.
  # The result is returned in a dictionary, being each key the locus
name.
  # The value is the GenePop record.
  # rec is not altered.

  rec_pops = rec.split_in_pops(pop_names)
  # Splits a record in populations, that is, for each population, it
creates
  # a new record, with a single population and all loci.
  # The result is returned in a dictionary, being each key
  # the population name. As population names are not available in
GenePop,
  # they are passed in array (pop_names).
  # The value of each dictionary entry is the GenePop record.
  # rec is not altered.
>>

  GenePop does not support population names, a limitation which can be
cumbersome at times. Functionality to enable population names is
currently being planned for Biopython. These extensions won't break
compatibility in any way with the standard format. In the medium term,
we would also like to support the GenePop web service.

Chapter 13  Phylogenetics with Bio.Phylo
****************************************

  The Bio.Phylo module was introduced in Biopython 1.54. Following the
lead of SeqIO and AlignIO, it aims to provide a common way to work with
phylogenetic trees independently of the source data format, as well as a
consistent API for I/O operations.
  Bio.Phylo is described in an open-access journal article [, Talevich
et al., 2012], which you might also find helpful.


13.1 Demo: What's in a Tree?
*=*=*=*=*=*=*=*=*=*=*=*=*=*=


  To get acquainted with the module, let's start with a tree that we've
already constructed, and inspect it a few different ways. Then we'll
colorize the branches, to use a special phyloXML feature, and finally
save it.
  Create a simple Newick file named simple.dnd using your favorite text
editor, or use simple.dnd (1) provided with the Biopython source code:
<<(((A,B),(C,D)),(E,F,G));
>>

  This tree has no branch lengths, only a topology and labelled
terminals. (If you have a real tree file available, you can follow this
demo using that instead.)
  Launch the Python interpreter of your choice:
<<$ ipython -pylab
>>

  For interactive work, launching the IPython interpreter with the
-pylab flag enables matplotlib integration, so graphics will pop up
automatically. We'll use that during this demo.
  Now, within Python, read the tree file, giving the file name and the
name of the format.
<<>>> from Bio import Phylo
  >>> tree = Phylo.read("simple.dnd", "newick")
>>

  Printing the tree object as a string gives us a look at the entire
object hierarchy.
<<>>> print(tree)
  Tree(rooted=False, weight=1.0)
      Clade()
          Clade()
              Clade()
                  Clade(name='A')
                  Clade(name='B')
              Clade()
                  Clade(name='C')
                  Clade(name='D')
          Clade()
              Clade(name='E')
              Clade(name='F')
              Clade(name='G')
>>

  The Tree object contains global information about the tree, such as
whether it's rooted or unrooted. It has one root clade, and under that,
it's nested lists of clades all the way down to the tips.
  The function draw_ascii creates a simple ASCII-art (plain text)
dendrogram. This is a convenient visualization for interactive
exploration, in case better graphical tools aren't available.
<<>>> from Bio import Phylo
  >>> tree = Phylo.read("simple.dnd", "newick")
  >>> Phylo.draw_ascii(tree)

________________________ A
                             ________________________|
                            |                       
|________________________ B
    ________________________|
   |                        |                        
________________________ C
   |                        |________________________|
  _|                                                
|________________________ D
   |
   |                         ________________________ E
   |                        |
   |________________________|________________________ F
                            |
                            |________________________ G
  <BLANKLINE>
>>

  If you have matplotlib or pylab installed, you can create a graphic
using the draw function (see Fig. ??):
<<>>> tree.rooted = True
  >>> Phylo.draw(tree)
>>

   *images/phylo-simple-draw.png*  



13.1.1 Coloring branches within a tree
======================================

  The function draw supports the display of different colors and branch
widths in a tree. As of Biopython 1.59, the color and width attributes
are available on the basic Clade object and there's nothing extra
required to use them. Both attributes refer to the branch leading the
given clade, and apply recursively, so all descendent branches will also
inherit the assigned width and color values during display.
  In earlier versions of Biopython, these were special features of
PhyloXML trees, and using the attributes required first converting the
tree to a subclass of the basic tree object called Phylogeny, from the
Bio.Phylo.PhyloXML module.
  In Biopython 1.55 and later, this is a convenient tree method:
<<>>> tree = tree.as_phyloxml()
>>

  In Biopython 1.54, you can accomplish the same thing with one extra
import:
<<>>> from Bio.Phylo.PhyloXML import Phylogeny
  >>> tree = Phylogeny.from_tree(tree)
>>

  Note that the file formats Newick and Nexus don't support branch
colors or widths, so if you use these attributes in Bio.Phylo, you will
only be able to save the values in PhyloXML format. (You can still save
a tree as Newick or Nexus, but the color and width values will be
skipped in the output file.)
  Now we can begin assigning colors. First, we'll color the root clade
gray. We can do that by assigning the 24-bit color value as an RGB
triple, an HTML-style hex string, or the name of one of the predefined
colors.
<<>>> tree.root.color = (128, 128, 128)
>>

  Or:
<<>>> tree.root.color = "#808080"
>>

  Or:
<<>>> tree.root.color = "gray"
>>

  Colors for a clade are treated as cascading down through the entire
clade, so when we colorize the root here, it turns the whole tree gray.
We can override that by assigning a different color lower down on the
tree.
  Let's target the most recent common ancestor (MRCA) of the nodes named
"E" and "F". The common_ancestor method returns a reference to that
clade in the original tree, so when we color that clade "salmon", the
color will show up in the original tree.
<<>>> mrca = tree.common_ancestor({"name": "E"}, {"name": "F"})
  >>> mrca.color = "salmon"
>>

  If we happened to know exactly where a certain clade is in the tree,
in terms of nested list entries, we can jump directly to that position
in the tree by indexing it. Here, the index [0,1] refers to the second
child of the first child of the root.
<<>>> tree.clade[0, 1].color = "blue"
>>

  Finally, show our work (see Fig. ??):
<<>>> Phylo.draw(tree)
>>

   *images/phylo-color-draw.png*  

  Note that a clade's color includes the branch leading to that clade,
as well as its descendents. The common ancestor of E and F turns out to
be just under the root, and with this coloring we can see exactly where
the root of the tree is.
  My, we've accomplished a lot! Let's take a break here and save our
work. Call the write function with a file name or handle --- here we use
standard output, to see what would be written --- and the format
phyloxml. PhyloXML saves the colors we assigned, so you can open this
phyloXML file in another tree viewer like Archaeopteryx, and the colors
will show up there, too.
<<>>> import sys
  >>> Phylo.write(tree, sys.stdout, "phyloxml")

  <phy:phyloxml xmlns:phy="http://www.phyloxml.org">
    <phy:phylogeny rooted="true">
      <phy:clade>
        <phy:branch_length>1.0</phy:branch_length>
        <phy:color>
          <phy:red>128</phy:red>
          <phy:green>128</phy:green>
          <phy:blue>128</phy:blue>
        </phy:color>
        <phy:clade>
          <phy:branch_length>1.0</phy:branch_length>
          <phy:clade>
            <phy:branch_length>1.0</phy:branch_length>
            <phy:clade>
              <phy:name>A</phy:name>
              ...
>>

  The rest of this chapter covers the core functionality of Bio.Phylo in
greater detail. For more examples of using Bio.Phylo, see the cookbook
page on Biopython.org:
  http://biopython.org/wiki/Phylo_cookbook


13.2 I/O functions
*=*=*=*=*=*=*=*=*=


  Like SeqIO and AlignIO, Phylo handles file input and output through
four functions: parse, read, write and convert, all of which support the
tree file formats Newick, NEXUS, phyloXML and NeXML, as well as the
Comparative Data Analysis Ontology (CDAO).
  The read function parses a single tree in the given file and returns
it. Careful; it will raise an error if the file contains more than one
tree, or no trees.
<<>>> from Bio import Phylo
  >>> tree = Phylo.read("Tests/Nexus/int_node_labels.nwk", "newick")
  >>> print(tree)
>>

  (Example files are available in the Tests/Nexus/ and Tests/PhyloXML/
directories of the Biopython distribution.)
  To handle multiple (or an unknown number of) trees, use the parse
function iterates through each of the trees in the given file:
<<>>> trees = Phylo.parse("../../Tests/PhyloXML/phyloxml_examples.xml",
"phyloxml")
  >>> for tree in trees:
  ...     print(tree)
>>

  Write a tree or iterable of trees back to file with the write
function:
<<>>> trees =
list(Phylo.parse("../../Tests/PhyloXML/phyloxml_examples.xml",
"phyloxml"))
  >>> tree1 = trees[0]
  >>> others = trees[1:]
  >>> Phylo.write(tree1, "tree1.nwk", "newick")
  1
  >>> Phylo.write(others, "other_trees.nwk", "newick")
  12
>>

  Convert files between any of the supported formats with the convert
function:
<<>>> Phylo.convert("tree1.nwk", "newick", "tree1.xml", "nexml")
  1
  >>> Phylo.convert("other_trees.xml", "phyloxml", "other_trees.nex",
"nexus")
  12
>>

  To use strings as input or output instead of actual files, use
StringIO as you would with SeqIO and AlignIO:
<<>>> from Bio import Phylo
  >>> from io import StringIO
  >>> handle = StringIO("(((A,B),(C,D)),(E,F,G));")
  >>> tree = Phylo.read(handle, "newick")
>>



13.3 View and export trees
*=*=*=*=*=*=*=*=*=*=*=*=*=


  The simplest way to get an overview of a Tree object is to print it:
<<>>> from Bio import Phylo
  >>> tree = Phylo.read("PhyloXML/example.xml", "phyloxml")
  >>> print(tree)
  Phylogeny(description='phyloXML allows to use either a "branch_length"
attribute...', name='example from Prof. Joe Felsenstein's book
"Inferring Phyl...', rooted=True)
      Clade()
          Clade(branch_length=0.06)
              Clade(branch_length=0.102, name='A')
              Clade(branch_length=0.23, name='B')
          Clade(branch_length=0.4, name='C')
>>

  This is essentially an outline of the object hierarchy Biopython uses
to represent a tree. But more likely, you'd want to see a drawing of the
tree. There are three functions to do this.
  As we saw in the demo, draw_ascii prints an ascii-art drawing of the
tree (a rooted phylogram) to standard output, or an open file handle if
given. Not all of the available information about the tree is shown, but
it provides a way to quickly view the tree without relying on any
external dependencies.
<<>>> tree = Phylo.read("example.xml", "phyloxml")
  >>> Phylo.draw_ascii(tree)
               __________________ A
    __________|
  _|          |___________________________________________ B
   |
   |____________________________________________________________________
_______ C

>>

  The draw function draws a more attractive image using the matplotlib
library. See the API documentation for details on the arguments it
accepts to customize the output.
<<>>> tree = Phylo.read("example.xml", "phyloxml")
  >>> Phylo.draw(tree, branch_labels=lambda c: c.branch_length)
>>

   *images/phylo-draw-example.png*  

  See the Phylo page on the Biopython wiki
(http://biopython.org/wiki/Phylo) for descriptions and examples of the
more advanced functionality in draw_ascii, draw_graphviz and
to_networkx.


13.4 Using Tree and Clade objects
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*


  The Tree objects produced by parse and read are containers for
recursive sub-trees, attached to the Tree object at the root attribute
(whether or not the phylogenic tree is actually considered rooted). A
Tree has globally applied information for the phylogeny, such as
rootedness, and a reference to a single Clade; a Clade has node- and
clade-specific information, such as branch length, and a list of its own
descendent Clade instances, attached at the clades attribute.
  So there is a distinction between tree and tree.root. In practice,
though, you rarely need to worry about it. To smooth over the
difference, both Tree and Clade inherit from TreeMixin, which contains
the implementations for methods that would be commonly used to search,
inspect or modify a tree or any of its clades. This means that almost
all of the methods supported by tree are also available on tree.root and
any clade below it. (Clade also has a root property, which returns the
clade object itself.)


13.4.1 Search and traversal methods
===================================

  For convenience, we provide a couple of simplified methods that return
all external or internal nodes directly as a list:


 get_terminals  makes a list of all of this tree's terminal (leaf)
   nodes. 
 get_nonterminals  makes a list of all of this tree's nonterminal
   (internal) nodes. 

  These both wrap a method with full control over tree traversal,
find_clades. Two more traversal methods, find_elements and find_any,
rely on the same core functionality and accept the same arguments, which
we'll call a "target specification" for lack of a better description.
These specify which objects in the tree will be matched and returned
during iteration. The first argument can be any of the following types:


   - A TreeElement instance, which tree elements will match by identity
   --- so searching with a Clade instance as the target will find that
   clade in the tree;

   - A string, which matches tree elements' string representation --- in
   particular, a clade's name (added in Biopython 1.56);

   - A class or type, where every tree element of the same type (or
   sub-type) will be matched;

   - A dictionary where keys are tree element attributes and values are
   matched to the corresponding attribute of each tree element. This one
   gets even more elaborate:


      - If an int is given, it matches numerically equal attributes,
      e.g. 1 will match 1 or 1.0

      - If a boolean is given (True or False), the corresponding
      attribute value is evaluated as a boolean and checked for the same

      - None matches None

      - If a string is given, the value is treated as a regular
      expression (which must match the whole string in the corresponding
      element attribute, not just a prefix). A given string without
      special regex characters will match string attributes exactly, so
      if you don't use regexes, don't worry about it. For example, in a
      tree with clade names Foo1, Foo2 and Foo3,
      tree.find_clades({"name": "Foo1"}) matches Foo1, {"name": "Foo.*"}
      matches all three clades, and {"name": "Foo"} doesn't match
      anything.

 Since floating-point arithmetic can produce some strange behavior, we
   don't support matching floats directly. Instead, use the boolean True
   to match every element with a nonzero value in the specified
   attribute, then filter on that attribute manually with an inequality
   (or exact number, if you like living dangerously).
 If the dictionary contains multiple entries, a matching element must
   match each of the given attribute values --- think "and", not "or".

   - A function taking a single argument (it will be applied to each
   element in the tree), returning True or False. For convenience,
   LookupError, AttributeError and ValueError are silenced, so this
   provides another safe way to search for floating-point values in the
   tree, or some more complex characteristic.

  After the target, there are two optional keyword arguments:


 terminal  --- A boolean value to select for or against terminal clades
   (a.k.a. leaf nodes): True searches for only terminal clades, False
   for non-terminal (internal) clades, and the default, None, searches
   both terminal and non-terminal clades, as well as any tree elements
   lacking the is_terminal method.

 order  --- Tree traversal order: "preorder" (default) is depth-first
   search, "postorder" is DFS with child nodes preceding parents, and
   "level" is breadth-first search.

  Finally, the methods accept arbitrary keyword arguments which are
treated the same way as a dictionary target specification: keys indicate
the name of the element attribute to search for, and the argument value
(string, integer, None or boolean) is compared to the value of each
attribute found. If no keyword arguments are given, then any TreeElement
types are matched. The code for this is generally shorter than passing a
dictionary as the target specification: tree.find_clades({"name":
"Foo1"}) can be shortened to tree.find_clades(name="Foo1").
  (In Biopython 1.56 or later, this can be even shorter:
tree.find_clades("Foo1"))
  Now that we've mastered target specifications, here are the methods
used to traverse a tree:


 find_clades  Find each clade containing a matching element. That is,
   find each element as with find_elements, but return the corresponding
   clade object. (This is usually what you want.)
 The result is an iterable through all matching objects, searching
   depth-first by default. This is not necessarily the same order as the
   elements appear in the Newick, Nexus or XML source file!

 find_elements  Find all tree elements matching the given attributes,
   and return the matching elements themselves. Simple Newick trees
   don't have complex sub-elements, so this behaves the same as
   find_clades on them. PhyloXML trees often do have complex objects
   attached to clades, so this method is useful for extracting those.

 find_any  Return the first element found by find_elements(), or None.
   This is also useful for checking whether any matching element exists
   in the tree, and can be used in a conditional.

  Two more methods help navigating between nodes in the tree:


 get_path  List the clades directly between the tree root (or current
   clade) and the given target. Returns a list of all clade objects
   along this path, ending with the given target, but excluding the root
   clade.

 trace  List of all clade object between two targets in this tree.
   Excluding start, including finish.



13.4.2 Information methods
==========================

  These methods provide information about the whole tree (or any clade).


 common_ancestor  Find the most recent common ancestor of all the given
   targets. (This will be a Clade object). If no target is given,
   returns the root of the current clade (the one this method is called
   from); if 1 target is given, this returns the target itself. However,
   if any of the specified targets are not found in the current tree (or
   clade), an exception is raised.

 count_terminals  Counts the number of terminal (leaf) nodes within the
   tree.

 depths  Create a mapping of tree clades to depths. The result is a
   dictionary where the keys are all of the Clade instances in the tree,
   and the values are the distance from the root to each clade
   (including terminals). By default the distance is the cumulative
   branch length leading to the clade, but with the
   unit_branch_lengths=True option, only the number of branches (levels
   in the tree) is counted.

 distance  Calculate the sum of the branch lengths between two targets.
   If only one target is specified, the other is the root of this tree.

 total_branch_length  Calculate the sum of all the branch lengths in
   this tree. This is usually just called the "length" of the tree in
   phylogenetics, but we use a more explicit name to avoid confusion
   with Python terminology.

  The rest of these methods are boolean checks:


 is_bifurcating  True if the tree is strictly bifurcating; i.e. all
   nodes have either 2 or 0 children (internal or external,
   respectively). The root may have 3 descendents and still be
   considered part of a bifurcating tree.

 is_monophyletic  Test if all of the given targets comprise a complete
   subclade --- i.e., there exists a clade such that its terminals are
   the same set as the given targets. The targets should be terminals of
   the tree. For convenience, this method returns the common ancestor
   (MCRA) of the targets if they are monophyletic (instead of the value
   True), and False otherwise.

 is_parent_of  True if target is a descendent of this tree --- not
   required to be a direct descendent. To check direct descendents of a
   clade, simply use list membership testing: if subclade in clade: ...

 is_preterminal  True if all direct descendents are terminal; False if
   any direct descendent is not terminal.



13.4.3 Modification methods
===========================

  These methods modify the tree in-place. If you want to keep the
original tree intact, make a complete copy of the tree first, using
Python's copy module:
<<tree = Phylo.read("example.xml", "phyloxml")
  import copy

  newtree = copy.deepcopy(tree)
>>



 collapse  Deletes the target from the tree, relinking its children to
   its parent.

 collapse_all  Collapse all the descendents of this tree, leaving only
   terminals. Branch lengths are preserved, i.e. the distance to each
   terminal stays the same. With a target specification (see above),
   collapses only the internal nodes matching the specification.

 ladderize  Sort clades in-place according to the number of terminal
   nodes. Deepest clades are placed last by default. Use reverse=True to
   sort clades deepest-to-shallowest.

 prune  Prunes a terminal clade from the tree. If taxon is from a
   bifurcation, the connecting node will be collapsed and its branch
   length added to remaining terminal node. This might no longer be a
   meaningful value.

 root_with_outgroup  Reroot this tree with the outgroup clade containing
   the given targets, i.e. the common ancestor of the outgroup. This
   method is only available on Tree objects, not Clades.
 If the outgroup is identical to self.root, no change occurs. If the
   outgroup clade is terminal (e.g. a single terminal node is given as
   the outgroup), a new bifurcating root clade is created with a
   0-length branch to the given outgroup. Otherwise, the internal node
   at the base of the outgroup becomes a trifurcating root for the whole
   tree. If the original root was bifurcating, it is dropped from the
   tree.
 In all cases, the total branch length of the tree stays the same.

 root_at_midpoint  Reroot this tree at the calculated midpoint between
   the two most distant tips of the tree. (This uses root_with_outgroup
   under the hood.)

 split  Generate n (default 2) new descendants. In a species tree, this
   is a speciation event. New clades have the given branch_length and
   the same name as this clade's root plus an integer suffix (counting
   from 0) --- for example, splitting a clade named "A" produces the
   sub-clades "A0" and "A1".

  See the Phylo page on the Biopython wiki
(http://biopython.org/wiki/Phylo) for more examples of using the
available methods.


13.4.4 Features of PhyloXML trees
=================================

  The phyloXML file format includes fields for annotating trees with
additional data types and visual cues.
  See the PhyloXML page on the Biopython wiki
(http://biopython.org/wiki/PhyloXML) for descriptions and examples of
using the additional annotation features provided by PhyloXML.


13.5 Running external applications
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=


  While Bio.Phylo doesn't infer trees from alignments itself, there are
third-party programs available that do. These are supported through the
module Bio.Phylo.Applications, using the same general framework as
Bio.Emboss.Applications, Bio.Align.Applications and others.
  WARNING: We have decided to drop these command line wrappers in a
future Biopython release. We will be updating this documentation to
instead build the command line directly, and invoke it with the
subprocess module.
  Biopython 1.58 introduced a wrapper for PhyML
(http://www.atgc-montpellier.fr/phyml/). The program accepts an input
alignment in phylip-relaxed format (that's Phylip format, but without
the 10-character limit on taxon names) and a variety of options. A quick
example:
<<>>> from Bio import Phylo
  >>> from Bio.Phylo.Applications import PhymlCommandline
  >>> cmd = PhymlCommandline(input="Tests/Phylip/random.phy")
  >>> out_log, err_log = cmd()
>>

  This generates a tree file and a stats file with the names
[input filename]_phyml_tree.txt and [input filename]_phyml_stats.txt.
The tree file is in Newick format:
<<>>> tree = Phylo.read("Tests/Phylip/random.phy_phyml_tree.txt",
"newick")
  >>> Phylo.draw_ascii(tree)
>>

  A similar wrapper for RAxML
(https://sco.h-its.org/exelixis/software.html) was added in Biopython
1.60, and FastTree (http://www.microbesonline.org/fasttree/) in
Biopython 1.62.
  Note that some popular Phylip programs, including dnaml and protml,
are already available through the EMBOSS wrappers in
Bio.Emboss.Applications if you have the Phylip extensions to EMBOSS
installed on your system. See Section ?? for some examples and clues on
how to use programs like these.


13.6 PAML integration
*=*=*=*=*=*=*=*=*=*=*


  Biopython 1.58 brought support for PAML
(http://abacus.gene.ucl.ac.uk/software/paml.html), a suite of programs
for phylogenetic analysis by maximum likelihood. Currently the programs
codeml, baseml and yn00 are implemented. Due to PAML's usage of control
files rather than command line arguments to control runtime options,
usage of this wrapper strays from the format of other application
wrappers in Biopython.
  A typical workflow would be to initialize a PAML object, specifying an
alignment file, a tree file, an output file and a working directory.
Next, runtime options are set via the set_options() method or by reading
an existing control file. Finally, the program is run via the run()
method and the output file is automatically parsed to a results
dictionary.
  Here is an example of typical usage of codeml: 
<<>>> from Bio.Phylo.PAML import codeml
  >>> cml = codeml.Codeml()
  >>> cml.alignment = "Tests/PAML/alignment.phylip"
  >>> cml.tree = "Tests/PAML/species.tree"
  >>> cml.out_file = "results.out"
  >>> cml.working_dir = "./scratch"
  >>> cml.set_options(seqtype=1,
  ...         verbose=0,
  ...         noisy=0,
  ...         RateAncestor=0,
  ...         model=0,
  ...         NSsites=[0, 1, 2],
  ...         CodonFreq=2,
  ...         cleandata=1,
  ...         fix_alpha=1,
  ...         kappa=4.54006)
  >>> results = cml.run()
  >>> ns_sites = results.get("NSsites")
  >>> m0 = ns_sites.get(0)
  >>> m0_params = m0.get("parameters")
  >>> print(m0_params.get("omega"))
>>

  Existing output files may be parsed as well using a module's read()
function: 
<<>>> results =
codeml.read("Tests/PAML/Results/codeml/codeml_NSsites_all.out")
  >>> print(results.get("lnL max"))
>>

  Detailed documentation for this new module currently lives on the
Biopython wiki: http://biopython.org/wiki/PAML


13.7 Future plans
*=*=*=*=*=*=*=*=*


  Bio.Phylo is under active development. Here are some features we might
add in future releases:


 New methods  Generally useful functions for operating on Tree or Clade
   objects appear on the Biopython wiki first, so that casual users can
   test them and decide if they're useful before we add them to
   Bio.Phylo:
 http://biopython.org/wiki/Phylo_cookbook

 Bio.Nexus port  Much of this module was written during Google Summer of
   Code 2009, under the auspices of NESCent, as a project to implement
   Python support for the phyloXML data format (see ??). Support for
   Newick and Nexus formats was added by porting part of the existing
   Bio.Nexus module to the new classes used by Bio.Phylo.
 Currently, Bio.Nexus contains some useful features that have not yet
   been ported to Bio.Phylo classes --- notably, calculating a consensus
   tree. If you find some functionality lacking in Bio.Phylo, try poking
   through Bio.Nexus to see if it's there instead.

  We're open to any suggestions for improving the functionality and
usability of this module; just let us know on the mailing list or our
bug database.
  Finally, if you need additional functionality not yet included in the
Phylo module, check if it's available in another of the high-quality
Python libraries for phylogenetics such as DendroPy
(https://dendropy.org/) or PyCogent (http://pycogent.org/). Since these
libraries also support standard file formats for phylogenetic trees, you
can easily transfer data between libraries by writing to a temporary
file or StringIO object.   
-----------------------------------


 (1) https://raw.githubusercontent.com/biopython/biopython/master/Doc/ex
   amples/simple.dnd


Chapter 14  Sequence motif analysis using Bio.motifs
****************************************************

  This chapter gives an overview of the functionality of the Bio.motifs
package included in Biopython. It is intended for people who are
involved in the analysis of sequence motifs, so I'll assume that you are
familiar with basic notions of motif analysis. In case something is
unclear, please look at Section ?? for some relevant links.
  Most of this chapter describes the new Bio.motifs package included in
Biopython 1.61 onwards, which is replacing the older Bio.Motif package
introduced with Biopython 1.50, which was in turn based on two older
former Biopython modules, Bio.AlignAce and Bio.MEME. It provides most of
their functionality with a unified motif object implementation.
  Speaking of other libraries, if you are reading this you might be
interested in TAMO (1), another python library designed to deal with
sequence motifs. It supports more de-novo motif finders, but it is not a
part of Biopython and has some restrictions on commercial use.


14.1 Motif objects
*=*=*=*=*=*=*=*=*=


  Since we are interested in motif analysis, we need to take a look at
Motif objects in the first place. For that we need to import the
Bio.motifs library:
<<>>> from Bio import motifs
>>

  and we can start creating our first motif objects. We can either
create a Motif object from a list of instances of the motif, or we can
obtain a Motif object by parsing a file from a motif database or motif
finding software.


14.1.1 Creating a motif from instances
======================================

  Suppose we have these instances of a DNA motif:
<<>>> from Bio.Seq import Seq
  >>> instances = [Seq("TACAA"),
  ...              Seq("TACGC"),
  ...              Seq("TACAC"),
  ...              Seq("TACCC"),
  ...              Seq("AACCC"),
  ...              Seq("AATGC"),
  ...              Seq("AATGC"),
  ...             ]
>>
   then we can create a Motif object as follows:
<<>>> m = motifs.create(instances)
>>
   The instances are saved in an attribute m.instances, which is
essentially a Python list with some added functionality, as described
below. Printing out the Motif object shows the instances from which it
was constructed:
<<>>> print(m)
  TACAA
  TACGC
  TACAC
  TACCC
  AACCC
  AATGC
  AATGC
  <BLANKLINE>
>>
   The length of the motif is defined as the sequence length, which
should be the same for all instances:
<<>>> len(m)
  5
>>
   The Motif object has an attribute .counts containing the counts of
each nucleotide at each position. Printing this counts matrix shows it
in an easily readable format:
<<>>> print(m.counts)
          0      1      2      3      4
  A:   3.00   7.00   0.00   2.00   1.00
  C:   0.00   0.00   5.00   2.00   6.00
  G:   0.00   0.00   0.00   3.00   0.00
  T:   4.00   0.00   2.00   0.00   0.00
  <BLANKLINE>
>>

  You can access these counts as a dictionary:
<<>>> m.counts["A"]
  [3, 7, 0, 2, 1]
>>
   but you can also think of it as a 2D array with the nucleotide as the
first dimension and the position as the second dimension:
<<>>> m.counts["T", 0]
  4
  >>> m.counts["T", 2]
  2
  >>> m.counts["T", 3]
  0
>>
   You can also directly access columns of the counts matrix 
<<>>> m.counts[:, 3]
  {'A': 2, 'C': 2, 'T': 0, 'G': 3}
>>
   Instead of the nucleotide itself, you can also use the index of the
nucleotide in the alphabet of the motif:
<<>>> m.alphabet
  'ACGT'
  >>> m.counts["A",:]
  (3, 7, 0, 2, 1)
  >>> m.counts[0,:]
  (3, 7, 0, 2, 1)
>>
   The motif has an associated consensus sequence, defined as the
sequence of letters along the positions of the motif for which the
largest value in the corresponding columns of the .counts matrix is
obtained:
<<>>> m.consensus
  Seq('TACGC')
>>
   as well as an anticonsensus sequence, corresponding to the smallest
values in the columns of the .counts matrix:
<<>>> m.anticonsensus
  Seq('CCATG')
>>
   Note that there is some ambiguity in the definition of the consensus
and anticonsensus sequence if in some columns multiple nucleotides have
the maximum or minimum count.
  You can also ask for a degenerate consensus sequence, in which
ambiguous nucleotides are used for positions where there are multiple
nucleotides with high counts:
<<>>> m.degenerate_consensus
  Seq('WACVC')
>>
   Here, W and R follow the IUPAC nucleotide ambiguity codes: W is
either A or T, and V is A, C, or G []. The degenerate consensus sequence
is constructed following the rules specified by Cavener [].
  We can also get the reverse complement of a motif:
<<>>> r = m.reverse_complement()
  >>> r.consensus
  Seq('GCGTA')
  >>> r.degenerate_consensus
  Seq('GBGTW')
  >>> print(r)
  TTGTA
  GCGTA
  GTGTA
  GGGTA
  GGGTT
  GCATT
  GCATT
  <BLANKLINE>
>>

  The reverse complement and the degenerate consensus sequence are only
defined for DNA motifs.


14.1.2 Creating a sequence logo
===============================
   If we have internet access, we can create a weblogo (2): 
<<>>> m.weblogo("mymotif.png")
>>
   We should get our logo saved as a PNG in the specified file.


14.2 Reading motifs
*=*=*=*=*=*=*=*=*=*


  Creating motifs from instances by hand is a bit boring, so it's useful
to have some I/O functions for reading and writing motifs. There are not
any really well established standards for storing motifs, but there are
a couple of formats that are more used than others.


14.2.1 JASPAR
=============
   One of the most popular motif databases is JASPAR (3). In addition to
the motif sequence information, the JASPAR database stores a lot of
meta-information for each motif. The module Bio.motifs contains a
specialized class jaspar.Motif in which this meta-information is
represented as attributes: 

   - matrix_id - the unique JASPAR motif ID, e.g. 'MA0004.1' 
   - name - the name of the TF, e.g. 'Arnt' 
   - collection - the JASPAR collection to which the motif belongs, e.g.
   'CORE' 
   - tf_class - the structural class of this TF, e.g. 'Zipper-Type' 
   - tf_family - the family to which this TF belongs, e.g.
   'Helix-Loop-Helix' 
   - species - the species to which this TF belongs, may have multiple
   values, these are specified as taxonomy IDs, e.g. 10090 
   - tax_group - the taxonomic supergroup to which this motif belongs,
   e.g. 'vertebrates' 
   - acc - the accession number of the TF protein, e.g. 'P53762' 
   - data_type - the type of data used to construct this motif, e.g.
   'SELEX' 
   - medline - the Pubmed ID of literature supporting this motif, may be
   multiple values, e.g. 7592839 
   - pazar_id - external reference to the TF in the PAZAR database, e.g.
   'TF0000003' 
   - comment - free form text containing notes about the construction of
   the motif 

  The jaspar.Motif class inherits from the generic Motif class and
therefore provides all the facilities of any of the motif formats ---
reading motifs, writing motifs, scanning sequences for motif instances
etc.
  JASPAR stores motifs in several different ways including three
different flat file formats and as an SQL database. All of these formats
facilitate the construction of a counts matrix. However, the amount of
meta information described above that is available varies with the
format.


The JASPAR sites format
-----------------------

  The first of the three flat file formats contains a list of instances.
As an example, these are the beginning and ending lines of the JASPAR
Arnt.sites file showing known binding sites of the mouse
helix-loop-helix transcription factor Arnt. 
<<>MA0004 ARNT 1
  CACGTGatgtcctc
  >MA0004 ARNT 2
  CACGTGggaggtac
  >MA0004 ARNT 3
  CACGTGccgcgcgc
  ...
  >MA0004 ARNT 18
  AACGTGacagccctcc
  >MA0004 ARNT 19
  AACGTGcacatcgtcc
  >MA0004 ARNT 20
  aggaatCGCGTGc
>>
   The parts of the sequence in capital letters are the motif instances
that were found to align to each other.
  We can create a Motif object from these instances as follows:
<<>>> from Bio import motifs
  >>> with open("Arnt.sites") as handle:
  ...     arnt = motifs.read(handle, "sites")
  ...
>>
   The instances from which this motif was created is stored in the
.instances property:
<<>>> print(arnt.instances[:3])
  [Seq('CACGTG'), Seq('CACGTG'), Seq('CACGTG')]
  >>> for instance in arnt.instances:
  ...     print(instance)
  ...
  CACGTG
  CACGTG
  CACGTG
  CACGTG
  CACGTG
  CACGTG
  CACGTG
  CACGTG
  CACGTG
  CACGTG
  CACGTG
  CACGTG
  CACGTG
  CACGTG
  CACGTG
  AACGTG
  AACGTG
  AACGTG
  AACGTG
  CGCGTG
>>
   The counts matrix of this motif is automatically calculated from the
instances:
<<>>> print(arnt.counts)
          0      1      2      3      4      5
  A:   4.00  19.00   0.00   0.00   0.00   0.00
  C:  16.00   0.00  20.00   0.00   0.00   0.00
  G:   0.00   1.00   0.00  20.00   0.00  20.00
  T:   0.00   0.00   0.00   0.00  20.00   0.00
  <BLANKLINE>
>>
   This format does not store any meta information.


The JASPAR pfm format
---------------------

  JASPAR also makes motifs available directly as a count matrix, without
the instances from which it was created. This pfm format only stores the
counts matrix for a single motif. For example, this is the JASPAR file
SRF.pfm containing the counts matrix for the human SRF transcription
factor: 
<< 2 9 0 1 32 3 46 1 43 15 2 2
   1 33 45 45 1 1 0 0 0 1 0 1
  39 2 1 0 0 0 0 0 0 0 44 43
   4 2 0 0 13 42 0 45 3 30 0 0
>>
   We can create a motif for this count matrix as follows:
<<>>> with open("SRF.pfm") as handle:
  ...     srf = motifs.read(handle, "pfm")
  ...
  >>> print(srf.counts)
          0      1      2      3      4      5      6      7      8     
9     10     11
  A:   2.00   9.00   0.00   1.00  32.00   3.00  46.00   1.00  43.00 
15.00   2.00   2.00
  C:   1.00  33.00  45.00  45.00   1.00   1.00   0.00   0.00   0.00  
1.00   0.00   1.00
  G:  39.00   2.00   1.00   0.00   0.00   0.00   0.00   0.00   0.00  
0.00  44.00  43.00
  T:   4.00   2.00   0.00   0.00  13.00  42.00   0.00  45.00   3.00 
30.00   0.00   0.00
  <BLANKLINE>
>>
   As this motif was created from the counts matrix directly, it has no
instances associated with it:
<<>>> print(srf.instances)
  None
>>
   We can now ask for the consensus sequence of these two motifs:
<<>>> print(arnt.counts.consensus)
  CACGTG
  >>> print(srf.counts.consensus)
  GCCCATATATGG
>>

  As with the instances file, no meta information is stored in this
format.


The JASPAR format jaspar
------------------------

  The jaspar file format allows multiple motifs to be specified in a
single file. In this format each of the motif records consist of a
header line followed by four lines defining the counts matrix. The
header line begins with a > character (similar to the Fasta file format)
and is followed by the unique JASPAR matrix ID and the TF name. The
following example shows a jaspar formatted file containing the three
motifs Arnt, RUNX1 and MEF2A: 
<<>MA0004.1 Arnt
  A  [ 4 19  0  0  0  0 ]
  C  [16  0 20  0  0  0 ]
  G  [ 0  1  0 20  0 20 ]
  T  [ 0  0  0  0 20  0 ]
  >MA0002.1 RUNX1
  A  [10 12  4  1  2  2  0  0  0  8 13 ]
  C  [ 2  2  7  1  0  8  0  0  1  2  2 ]
  G  [ 3  1  1  0 23  0 26 26  0  0  4 ]
  T  [11 11 14 24  1 16  0  0 25 16  7 ]
  >MA0052.1 MEF2A
  A  [ 1  0 57  2  9  6 37  2 56  6 ]
  C  [50  0  1  1  0  0  0  0  0  0 ]
  G  [ 0  0  0  0  0  0  0  0  2 50 ]
  T  [ 7 58  0 55 49 52 21 56  0  2 ]
>>
   The motifs are read as follows: 
<<>>> fh = open("jaspar_motifs.txt")
  >>> for m in motifs.parse(fh, "jaspar"))
  ...     print(m)
  TF name  Arnt
  Matrix ID MA0004.1
  Matrix:
          0      1      2      3      4      5
  A:   4.00  19.00   0.00   0.00   0.00   0.00
  C:  16.00   0.00  20.00   0.00   0.00   0.00
  G:   0.00   1.00   0.00  20.00   0.00  20.00
  T:   0.00   0.00   0.00   0.00  20.00   0.00



  TF name  RUNX1
  Matrix ID MA0002.1
  Matrix:
          0      1      2      3      4      5      6      7      8     
9     10
  A:  10.00  12.00   4.00   1.00   2.00   2.00   0.00   0.00   0.00  
8.00  13.00
  C:   2.00   2.00   7.00   1.00   0.00   8.00   0.00   0.00   1.00  
2.00   2.00
  G:   3.00   1.00   1.00   0.00  23.00   0.00  26.00  26.00   0.00  
0.00   4.00
  T:  11.00  11.00  14.00  24.00   1.00  16.00   0.00   0.00  25.00 
16.00   7.00



  TF name  MEF2A
  Matrix ID MA0052.1
  Matrix:
          0      1      2      3      4      5      6      7      8     
9
  A:   1.00   0.00  57.00   2.00   9.00   6.00  37.00   2.00  56.00  
6.00
  C:  50.00   0.00   1.00   1.00   0.00   0.00   0.00   0.00   0.00  
0.00
  G:   0.00   0.00   0.00   0.00   0.00   0.00   0.00   0.00   2.00 
50.00
  T:   7.00  58.00   0.00  55.00  49.00  52.00  21.00  56.00   0.00  
2.00
>>

  Note that printing a JASPAR motif yields both the counts data and the
available meta-information.


Accessing the JASPAR database
-----------------------------

  In addition to parsing these flat file formats, we can also retrieve
motifs from a JASPAR SQL database. Unlike the flat file formats, a
JASPAR database allows storing of all possible meta information defined
in the JASPAR Motif class. It is beyond the scope of this document to
describe how to set up a JASPAR database (please see the main JASPAR (4)
website). Motifs are read from a JASPAR database using the
Bio.motifs.jaspar.db module. First connect to the JASPAR database using
the JASPAR5 class which models the the latest JASPAR schema: 
<<>>> from Bio.motifs.jaspar.db import JASPAR5
  >>>
  >>> JASPAR_DB_HOST = <hostname>
  >>> JASPAR_DB_NAME = <db_name>
  >>> JASPAR_DB_USER = <user>
  >>> JASPAR_DB_PASS = <passord>
  >>>
  >>> jdb = JASPAR5(
  ...     host=JASPAR_DB_HOST,
  ...     name=JASPAR_DB_NAME,
  ...     user=JASPAR_DB_USER,
  ...     password=JASPAR_DB_PASS
  ... )
>>

  Now we can fetch a single motif by its unique JASPAR ID with the
fetch_motif_by_id method. Note that a JASPAR ID conists of a base ID and
a version number separated by a decimal point, e.g. 'MA0004.1'. The
fetch_motif_by_id method allows you to use either the fully specified ID
or just the base ID. If only the base ID is provided, the latest version
of the motif is returned. 
<<>>> arnt = jdb.fetch_motif_by_id("MA0004")
>>
   Printing the motif reveals that the JASPAR SQL database stores much
more meta-information than the flat files: 
<<>>> print(arnt)
  TF name Arnt
  Matrix ID MA0004.1
  Collection CORE
  TF class Zipper-Type
  TF family Helix-Loop-Helix
  Species 10090
  Taxonomic group vertebrates
  Accession ['P53762']
  Data type used SELEX
  Medline 7592839
  PAZAR ID TF0000003
  Comments -
  Matrix:
   0      1      2      3      4      5
  A:   4.00  19.00   0.00   0.00   0.00   0.00
  C:  16.00   0.00  20.00   0.00   0.00   0.00
  G:   0.00   1.00   0.00  20.00   0.00  20.00
  T:   0.00   0.00   0.00   0.00  20.00   0.00


>>

  We can also fetch motifs by name. The name must be an exact match
(partial matches or database wildcards are not currently supported).
Note that as the name is not guaranteed to be unique, the
fetch_motifs_by_name method actually returns a list. 
<<>>> motifs = jdb.fetch_motifs_by_name("Arnt")
  >>> print(motifs[0])
  TF name Arnt
  Matrix ID MA0004.1
  Collection CORE
  TF class Zipper-Type
  TF family Helix-Loop-Helix
  Species 10090
  Taxonomic group vertebrates
  Accession ['P53762']
  Data type used SELEX
  Medline 7592839
  PAZAR ID TF0000003
  Comments -
  Matrix:
   0      1      2      3      4      5
  A:   4.00  19.00   0.00   0.00   0.00   0.00
  C:  16.00   0.00  20.00   0.00   0.00   0.00
  G:   0.00   1.00   0.00  20.00   0.00  20.00
  T:   0.00   0.00   0.00   0.00  20.00   0.00


>>

  The fetch_motifs method allows you to fetch motifs which match a
specified set of criteria. These criteria include any of the above
described meta information as well as certain matrix properties such as
the minimum information content (min_ic in the example below), the
minimum length of the matrix or the minimum number of sites used to
construct the matrix. Only motifs which pass ALL the specified criteria
are returned. Note that selection criteria which correspond to meta
information which allow for multiple values may be specified as either a
single value or a list of values, e.g. tax_group and tf_family in the
example below. 
<<>>> motifs = jdb.fetch_motifs(
  ...     collection = "CORE",
  ...     tax_group = ["vertebrates", "insects"],
  ...     tf_class = "Winged Helix-Turn-Helix",
  ...     tf_family = ["Forkhead", "Ets"],
  ...     min_ic = 12
  ... )
  >>> for motif in motifs:
  ...     pass # do something with the motif
>>



Compatibility with Perl TFBS modules
------------------------------------

  An important thing to note is that the JASPAR Motif class was designed
to be compatible with the popular Perl TFBS modules (5). Therefore some
specifics about the choice of defaults for background and pseudocounts
as well as how information content is computed and sequences searched
for instances is based on this compatibility criteria. These choices are
noted in the specific subsections below.


   - Choice of background:
 The Perl TFBS modules appear to allow a choice of custom background
   probabilities (although the documentation states that uniform
   background is assumed). However the default is to use a uniform
   background. Therefore it is recommended that you use a uniform
   background for computing the position-specific scoring matrix (PSSM).
   This is the default when using the Biopython motifs module. 
   - Choice of pseudocounts:
 By default, the Perl TFBS modules use a pseudocount equal to (N)^1/2 *
   bg[nucleotide], where N represents the total number of sequences used
   to construct the matrix. To apply this same pseudocount formula, set
   the motif pseudocounts attribute using the
   jaspar.calculate\_pseudcounts() function: 
   <<>>> motif.pseudocounts =
   motifs.jaspar.calculate_pseudocounts(motif)
   >>
  Note that it is possible for the counts matrix to have an unequal
   number of sequences making up the columns. The pseudocount
   computation uses the average number of sequences making up the
   matrix. However, when normalize is called on the counts matrix, each
   count value in a column is divided by the total number of sequences
   making up that specific column, not by the average number of
   sequences. This differs from the Perl TFBS modules because the
   normalization is not done as a separate step and so the average
   number of sequences is used throughout the computation of the pssm.
   Therefore, for matrices with unequal column counts, the PSSM computed
   by the motifs module will differ somewhat from the pssm computed by
   the Perl TFBS modules. 
   - Computation of matrix information content:
 The information content (IC) or specificity of a matrix is computed
   using the mean method of the PositionSpecificScoringMatrix class.
   However of note, in the Perl TFBS modules the default behaviour is to
   compute the IC without first applying pseudocounts, even though by
   default the PSSMs are computed using pseudocounts as described above.

   - Searching for instances:
 Searching for instances with the Perl TFBS motifs was usually performed
   using a relative score threshold, i.e. a score in the range 0 to 1.
   In order to compute the absolute PSSM score corresponding to a
   relative score one can use the equation: 
   <<>>> abs_score =  (pssm.max - pssm.min) * rel_score + pssm.min
   >>
  To convert the absolute score of an instance back to a relative score,
   one can use the equation: 
   <<>>> rel_score = (abs_score - pssm.min) / (pssm.max - pssm.min)
   >>
  For example, using the Arnt motif before, let's search a sequence with
   a relative score threshold of 0.8. 
   <<>>> test_seq=Seq("TAAGCGTGCACGCGCAACACGTGCATTA")
     >>> arnt.pseudocounts = motifs.jaspar.calculate_pseudocounts(arnt)
     >>> pssm = arnt.pssm
     >>> max_score = pssm.max
     >>> min_score = pssm.min
     >>> abs_score_threshold = (max_score - min_score) * 0.8 + min_score
     >>> for position, score in pssm.search(test_seq,

   threshold=abs_score_threshold):
     ...     rel_score = (score - min_score) / (max_score - min_score)
     ...     print("Position %d: score = %5.3f, rel. score = %5.3f" % (
                 position, score, rel_score))
     ...
     Position 2: score = 5.362, rel. score = 0.801
     Position 8: score = 6.112, rel. score = 0.831
     Position -20: score = 7.103, rel. score = 0.870
     Position 17: score = 10.351, rel. score = 1.000
     Position -11: score = 10.351, rel. score = 1.000
   >>




14.2.2 MEME
===========

  MEME [] is a tool for discovering motifs in a group of related DNA or
protein sequences. It takes as input a group of DNA or protein sequences
and outputs as many motifs as requested. Therefore, in contrast to
JASPAR files, MEME output files typically contain multiple motifs. This
is an example.
  At the top of an output file generated by MEME shows some background
information about the MEME and the version of MEME used: 
<<**********************************************************************
**********
  MEME - Motif discovery tool
  **********************************************************************
**********
  MEME version 3.0 (Release date: 2004/08/18 09:07:01)
  ...
>>
   Further down, the input set of training sequences is recapitulated: 
<<**********************************************************************
**********
  TRAINING SET
  **********************************************************************
**********
  DATAFILE= INO_up800.s
  ALPHABET= ACGT
  Sequence name            Weight Length  Sequence name           
Weight Length
  -------------            ------ ------  -------------           
------ ------
  CHO1                     1.0000    800  CHO2                    
1.0000    800
  FAS1                     1.0000    800  FAS2                    
1.0000    800
  ACC1                     1.0000    800  INO1                    
1.0000    800
  OPI3                     1.0000    800
  **********************************************************************
**********
>>
   and the exact command line that was used: 
<<**********************************************************************
**********
  COMMAND LINE SUMMARY
  **********************************************************************
**********
  This information can also be useful in the event you wish to report a
  problem with the MEME software.

  command: meme -mod oops -dna -revcomp -nmotifs 2 -bfile
yeast.nc.6.freq INO_up800.s
  ...
>>
   Next is detailed information on each motif that was found: 
<<**********************************************************************
**********
  MOTIF  1        width =   12   sites =   7   llr = 95   E-value =
2.0e-001
  **********************************************************************
**********
  ----------------------------------------------------------------------
----------
          Motif 1 Description
  ----------------------------------------------------------------------
----------
  Simplified        A  :::9:a::::3:
  pos.-specific     C  ::a:9:11691a
  probability       G  ::::1::94:4:
  matrix            T  aa:1::9::11:
>>
   To parse this file (stored as meme.dna.oops.txt), use
<<>>> with open("meme.INO_up800.classic.oops.xml") as handle:
  ...     record = motifs.parse(handle, "meme")
  ...
>>
   The motifs.parse command reads the complete file directly, so you can
close the file after calling motifs.parse. The header information is
stored in attributes:
<<>>> record.version
  '5.0.1'
  >>> record.datafile
  'common/INO_up800.s'
  >>> record.command
  'meme common/INO_up800.s -oc results/meme10 -mod oops -dna -revcomp
-bfile common/yeast.nc.6.freq -nmotifs 2 -objfun classic -minw 8
-nostatus '
  >>> record.alphabet
  'ACGT'
  >>> record.sequences
  ['sequence_0', 'sequence_1', 'sequence_2', 'sequence_3', 'sequence_4',
'sequence_5', 'sequence_6']
>>
   The record is an object of the Bio.motifs.meme.Record class. The
class inherits from list, and you can think of record as a list of Motif
objects:
<<>>> len(record)
  2
  >>> motif = record[0]
  >>> print(motif.consensus)
  GCGGCATGTGAAA
  >>> print(motif.degenerate_consensus)
  GSKGCATGTGAAA
>>
   In addition to these generic motif attributes, each motif also stores
its specific information as calculated by MEME. For example,
<<>>> motif.num_occurrences
  7
  >>> motif.length
  13
  >>> evalue = motif.evalue
  >>> print("%3.1g" % evalue)
  0.2
  >>> motif.name
  'GSKGCATGTGAAA'
  >>> motif.id
  'motif_1'
>>
   In addition to using an index into the record, as we did above, you
can also find it by its name:
<<>>> motif = record["GSKGCATGTGAAA"]
>>
   Each motif has an attribute .instances with the sequence instances in
which the motif was found, providing some information on each instance:
<<>>> len(motif.instances)
  7
  >>> motif.instances[0]
  Instance('GCGGCATGTGAAA')
  >>> motif.instances[0].motif_name
  'GSKGCATGTGAAA'
  >>> motif.instances[0].sequence_name
  'INO1'
  >>> motif.instances[0].sequence_id
  'sequence_5'
  >>> motif.instances[0].start
  620
  >>> motif.instances[0].strand
  '+'
  >>> motif.instances[0].length
  13
  >>> pvalue = motif.instances[0].pvalue
  >>> print("%5.3g" % pvalue)
  1.21e-08
>>



MAST
----



14.2.3 TRANSFAC
===============

  TRANSFAC is a manually curated database of transcription factors,
together with their genomic binding sites and DNA binding profiles [].
While the file format used in the TRANSFAC database is nowadays also
used by others, we will refer to it as the TRANSFAC file format.
  A minimal file in the TRANSFAC format looks as follows: 
<<ID  motif1
  P0      A      C      G      T
  01      1      2      2      0      S
  02      2      1      2      0      R
  03      3      0      1      1      A
  04      0      5      0      0      C
  05      5      0      0      0      A
  06      0      0      4      1      G
  07      0      1      4      0      G
  08      0      0      0      5      T
  09      0      0      5      0      G
  10      0      1      2      2      K
  11      0      2      0      3      Y
  12      1      0      3      1      G
  //
>>
   This file shows the frequency matrix of motif motif1 of 12
nucleotides. In general, one file in the TRANSFAC format can contain
multiple motifs. For example, this is the contents of the example
TRANSFAC file transfac.dat: 
<<VV  EXAMPLE January 15, 2013
  XX
  //
  ID  motif1
  P0      A      C      G      T
  01      1      2      2      0      S
  02      2      1      2      0      R
  03      3      0      1      1      A
  ...
  11      0      2      0      3      Y
  12      1      0      3      1      G
  //
  ID  motif2
  P0      A      C      G      T
  01      2      1      2      0      R
  02      1      2      2      0      S
  ...
  09      0      0      0      5      T
  10      0      2      0      3      Y
  //
>>
   To parse a TRANSFAC file, use
<<>>> with open("transfac.dat") as handle:
  ...     record = motifs.parse(handle, "TRANSFAC")
  ...
>>
   If any discrepancies between the file contents and the TRANSFAC file
format are detected, a ValueError is raised. Note that you may encounter
files that do not follow the TRANSFAC format strictly. For example, the
number of spaces between columns may be different, or a tab may be used
instead of spaces. Use strict=False to enable parsing such files without
raising a ValueError: 
<<>>> record = motifs.parse(handle, "TRANSFAC", strict=False)
>>
   When parsing a non-compliant file, we recommend to check the record
returned by motif.parse to ensure that it is consistent with the file
contents.
  The overall version number, if available, is stored as record.version:
<<>>> record.version
  'EXAMPLE January 15, 2013'
>>

  Each motif in record is in instance of the Bio.motifs.transfac.Motif
class, which inherits both from the Bio.motifs.Motif class and from a
Python dictionary. The dictionary uses the two-letter keys to store any
additional information about the motif:
<<>>> motif = record[0]
  >>> motif.degenerate_consensus # Using the Bio.motifs.Motif property
  Seq('SRACAGGTGKYG')
  >>> motif["ID"] # Using motif as a dictionary
  'motif1'
>>

  TRANSFAC files are typically much more elaborate than this example,
containing lots of additional information about the motif. Table ??
lists the two-letter field codes that are commonly found in TRANSFAC
files: 
   --------------------------------------------------------------------


     Table 14.1: Fields commonly found in TRANSFAC files

          |AC|Accession number                                ||
          |AS|Accession numbers, secondary                    ||
          |BA|Statistical basis                               ||
          |BF|Binding factors                                 ||
          |BS|Factor binding sites underlying the matrix      ||
          |CC|Comments                                        ||
          |CO|Copyright notice                                ||
          |DE|Short factor description                        ||
          |DR|External databases                              ||
          |DT|Date created/updated                            ||
          |HC|Subfamilies                                     ||
          |HP|Superfamilies                                   ||
          |ID|Identifier                                      ||
          |NA|Name of the binding factor                      ||
          |OC|Taxonomic classification                        ||
          |OS|Species/Taxon                                   ||
          |OV|Older version                                   ||
          |PV|Preferred version                               ||
          |TY|Type                                            ||
          |XX|Empty line; these are not stored in the Record. ||


   --------------------------------------------------------------------

  Each motif also has an attribute .references containing the references
associated with the motif, using these two-letter keys:
   --------------------------------------------------------------------


     Table 14.2: Fields used to store references in TRANSFAC files

                         |RN|Reference number  ||
                         |RA|Reference authors ||
                         |RL|Reference data    ||
                         |RT|Reference title   ||
                         |RX|PubMed ID         ||


   --------------------------------------------------------------------

  Printing the motifs writes them out in their native TRANSFAC format:
<<>>> print(record)
  VV  EXAMPLE January 15, 2013
  XX
  //
  ID  motif1
  XX
  P0      A      C      G      T
  01      1      2      2      0      S
  02      2      1      2      0      R
  03      3      0      1      1      A
  04      0      5      0      0      C
  05      5      0      0      0      A
  06      0      0      4      1      G
  07      0      1      4      0      G
  08      0      0      0      5      T
  09      0      0      5      0      G
  10      0      1      2      2      K
  11      0      2      0      3      Y
  12      1      0      3      1      G
  XX
  //
  ID  motif2
  XX
  P0      A      C      G      T
  01      2      1      2      0      R
  02      1      2      2      0      S
  03      0      5      0      0      C
  04      3      0      1      1      A
  05      0      0      4      1      G
  06      5      0      0      0      A
  07      0      1      4      0      G
  08      0      0      5      0      G
  09      0      0      0      5      T
  10      0      2      0      3      Y
  XX
  //
  <BLANKLINE>
>>
   You can export the motifs in the TRANSFAC format by capturing this
output in a string and saving it in a file: 
<<>>> text = str(record)
  >>> with open("mytransfacfile.dat", "w") as out_handle:
  ...     out_handle.write(text)
  ...
>>



14.3 Writing motifs
*=*=*=*=*=*=*=*=*=*


  Speaking of exporting, let's look at export functions in general. We
can use the format built-in function to write the motif in the simple
JASPAR pfm format: 
<<>>> print(arnt.format("pfm"))
    4.00  19.00   0.00   0.00   0.00   0.00
   16.00   0.00  20.00   0.00   0.00   0.00
    0.00   1.00   0.00  20.00   0.00  20.00
    0.00   0.00   0.00   0.00  20.00   0.00
>>
   Similarly, we can use format to write the motif in the JASPAR jaspar
format: 
<<>>> print(arnt.format("jaspar"))
  >MA0004.1  Arnt
  A [  4.00  19.00   0.00   0.00   0.00   0.00]
  C [ 16.00   0.00  20.00   0.00   0.00   0.00]
  G [  0.00   1.00   0.00  20.00   0.00  20.00]
  T [  0.00   0.00   0.00   0.00  20.00   0.00]
>>

  To write the motif in a TRANSFAC-like matrix format, use
<<>>> print(m.format("transfac"))
  P0      A      C      G      T
  01      3      0      0      4      W
  02      7      0      0      0      A
  03      0      5      0      2      C
  04      2      2      3      0      V
  05      1      6      0      0      C
  XX
  //
  <BLANKLINE>
>>

  To write out multiple motifs, you can use motifs.write. This function
can be used regardless of whether the motifs originated from a TRANSFAC
file. For example,
<<>>> two_motifs = [arnt, srf]
  >>> print(motifs.write(two_motifs, "transfac"))
  P0      A      C      G      T
  01      4     16      0      0      C
  02     19      0      1      0      A
  03      0     20      0      0      C
  04      0      0     20      0      G
  05      0      0      0     20      T
  06      0      0     20      0      G
  XX
  //
  P0      A      C      G      T
  01      2      1     39      4      G
  02      9     33      2      2      C
  03      0     45      1      0      C
  04      1     45      0      0      C
  05     32      1      0     13      A
  06      3      1      0     42      T
  07     46      0      0      0      A
  08      1      0      0     45      T
  09     43      0      0      3      A
  10     15      1      0     30      W
  11      2      0     44      0      G
  12      2      1     43      0      G
  XX
  //
  <BLANKLINE>
>>

  Or, to write multiple motifs in the jaspar format: 
<<>>> two_motifs = [arnt, mef2a]
  >>> print(motifs.write(two_motifs, "jaspar"))
  >MA0004.1  Arnt
  A [  4.00  19.00   0.00   0.00   0.00   0.00]
  C [ 16.00   0.00  20.00   0.00   0.00   0.00]
  G [  0.00   1.00   0.00  20.00   0.00  20.00]
  T [  0.00   0.00   0.00   0.00  20.00   0.00]
  >MA0052.1  MEF2A
  A [  1.00   0.00  57.00   2.00   9.00   6.00  37.00   2.00  56.00  
6.00]
  C [ 50.00   0.00   1.00   1.00   0.00   0.00   0.00   0.00   0.00  
0.00]
  G [  0.00   0.00   0.00   0.00   0.00   0.00   0.00   0.00   2.00 
50.00]
  T [  7.00  58.00   0.00  55.00  49.00  52.00  21.00  56.00   0.00  
2.00]
>>



14.4 Position-Weight Matrices
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*


  The .counts attribute of a Motif object shows how often each
nucleotide appeared at each position along the alignment. We can
normalize this matrix by dividing by the number of instances in the
alignment, resulting in the probability of each nucleotide at each
position along the alignment. We refer to these probabilities as the
position-weight matrix. However, beware that in the literature this term
may also be used to refer to the position-specific scoring matrix, which
we discuss below.
  Usually, pseudocounts are added to each position before normalizing.
This avoids overfitting of the position-weight matrix to the limited
number of motif instances in the alignment, and can also prevent
probabilities from becoming zero. To add a fixed pseudocount to all
nucleotides at all positions, specify a number for the pseudocounts
argument:
<<>>> pwm = m.counts.normalize(pseudocounts=0.5)
  >>> print(pwm)
          0      1      2      3      4
  A:   0.39   0.83   0.06   0.28   0.17
  C:   0.06   0.06   0.61   0.28   0.72
  G:   0.06   0.06   0.06   0.39   0.06
  T:   0.50   0.06   0.28   0.06   0.06
  <BLANKLINE>
>>
   Alternatively, pseudocounts can be a dictionary specifying the
pseudocounts for each nucleotide. For example, as the GC content of the
human genome is about 40%, you may want to choose the pseudocounts
accordingly:
<<>>> pwm = m.counts.normalize(pseudocounts={"A":0.6, "C": 0.4, "G":
0.4, "T": 0.6})
  >>> print(pwm)
          0      1      2      3      4
  A:   0.40   0.84   0.07   0.29   0.18
  C:   0.04   0.04   0.60   0.27   0.71
  G:   0.04   0.04   0.04   0.38   0.04
  T:   0.51   0.07   0.29   0.07   0.07
  <BLANKLINE>
>>
   The position-weight matrix has its own methods to calculate the
consensus, anticonsensus, and degenerate consensus sequences:
<<>>> pwm.consensus
  Seq('TACGC')
  >>> pwm.anticonsensus
  Seq('CCGTG')
  >>> pwm.degenerate_consensus
  Seq('WACNC')
>>
   Note that due to the pseudocounts, the degenerate consensus sequence
calculated from the position-weight matrix is slightly different from
the degenerate consensus sequence calculated from the instances in the
motif:
<<>>> m.degenerate_consensus
  Seq('WACVC')
>>
   The reverse complement of the position-weight matrix can be
calculated directly from the pwm:
<<>>> rpwm = pwm.reverse_complement()
  >>> print(rpwm)
          0      1      2      3      4
  A:   0.07   0.07   0.29   0.07   0.51
  C:   0.04   0.38   0.04   0.04   0.04
  G:   0.71   0.27   0.60   0.04   0.04
  T:   0.18   0.29   0.07   0.84   0.40
  <BLANKLINE>
>>



14.5 Position-Specific Scoring Matrices
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*


  Using the background distribution and PWM with pseudo-counts added,
it's easy to compute the log-odds ratios, telling us what are the log
odds of a particular symbol to be coming from a motif against the
background. We can use the .log_odds() method on the position-weight
matrix:
<<>>> pssm = pwm.log_odds()
  >>> print(pssm)
          0      1      2      3      4
  A:   0.68   1.76  -1.91   0.21  -0.49
  C:  -2.49  -2.49   1.26   0.09   1.51
  G:  -2.49  -2.49  -2.49   0.60  -2.49
  T:   1.03  -1.91   0.21  -1.91  -1.91
  <BLANKLINE>
>>
   Here we can see positive values for symbols more frequent in the
motif than in the background and negative for symbols more frequent in
the background. 0.0 means that it's equally likely to see a symbol in
the background and in the motif.
  This assumes that A, C, G, and T are equally likely in the background.
To calculate the position-specific scoring matrix against a background
with unequal probabilities for A, C, G, T, use the background argument.
For example, against a background with a 40% GC content, use
<<>>> background = {"A":0.3,"C":0.2,"G":0.2,"T":0.3}
  >>> pssm = pwm.log_odds(background)
  >>> print(pssm)
          0      1      2      3      4
  A:   0.42   1.49  -2.17  -0.05  -0.75
  C:  -2.17  -2.17   1.58   0.42   1.83
  G:  -2.17  -2.17  -2.17   0.92  -2.17
  T:   0.77  -2.17  -0.05  -2.17  -2.17
  <BLANKLINE>
>>

  The maximum and minimum score obtainable from the PSSM are stored in
the .max and .min properties:
<<>>> print("%4.2f" % pssm.max)
  6.59
  >>> print("%4.2f" % pssm.min)
  -10.85
>>

  The mean and standard deviation of the PSSM scores with respect to a
specific background are calculated by the .mean and .std methods.
<<>>> mean = pssm.mean(background)
  >>> std = pssm.std(background)
  >>> print("mean = %0.2f, standard deviation = %0.2f" % (mean, std))
  mean = 3.21, standard deviation = 2.59
>>
   A uniform background is used if background is not specified. The mean
is particularly important, as its value is equal to the Kullback-Leibler
divergence or relative entropy, and is a measure for the information
content of the motif compared to the background. As in Biopython the
base-2 logarithm is used in the calculation of the log-odds scores, the
information content has units of bits.
  The .reverse_complement, .consensus, .anticonsensus, and
.degenerate_consensus methods can be applied directly to PSSM objects.


14.6 Searching for instances
*=*=*=*=*=*=*=*=*=*=*=*=*=*=


  The most frequent use for a motif is to find its instances in some
sequence. For the sake of this section, we will use an artificial
sequence like this:
<<>>> test_seq=Seq("TACACTGCATTACAACCCAAGCATTA")
  >>> len(test_seq)
  26
>>



14.6.1 Searching for exact matches
==================================

  The simplest way to find instances, is to look for exact matches of
the true instances of the motif:
<<>>> for pos, seq in m.instances.search(test_seq):
  ...     print("%i %s" % (pos, seq))
  ...
  0 TACAC
  10 TACAA
  13 AACCC
>>
   We can do the same with the reverse complement (to find instances on
the complementary strand):
<<>>> for pos, seq in r.instances.search(test_seq):
  ...     print("%i %s" % (pos, seq))
  ...
  6 GCATT
  20 GCATT
>>



14.6.2 Searching for matches using the PSSM score
=================================================

  It's just as easy to look for positions, giving rise to high log-odds
scores against our motif:
<<>>> for position, score in pssm.search(test_seq, threshold=3.0):
  ...     print("Position %d: score = %5.3f" % (position, score))
  ...
  Position 0: score = 5.622
  Position -20: score = 4.601
  Position 10: score = 3.037
  Position 13: score = 5.738
  Position -6: score = 4.601
>>
   The negative positions refer to instances of the motif found on the
reverse strand of the test sequence, and follow the Python convention on
negative indices. Therefore, the instance of the motif at pos is located
at test_seq[pos:pos+len(m)] both for positive and for negative values of
pos.
  You may notice the threshold parameter, here set arbitrarily to 3.0.
This is in log_2, so we are now looking only for words, which are eight
times more likely to occur under the motif model than in the background.
The default threshold is 0.0, which selects everything that looks more
like the motif than the background.
  You can also calculate the scores at all positions along the sequence:
<<>>> pssm.calculate(test_seq)
  array([  5.62230396,  -5.6796999 ,  -3.43177247,   0.93827754,
          -6.84962511,  -2.04066086, -10.84962463,  -3.65614533,
          -0.03370807,  -3.91102552,   3.03734159,  -2.14918518,
          -0.6016975 ,   5.7381525 ,  -0.50977498,  -3.56422281,
          -8.73414803,  -0.09919716,  -0.6016975 ,  -2.39429784,
         -10.84962463,  -3.65614533], dtype=float32)
>>
   In general, this is the fastest way to calculate PSSM scores. The
scores returned by pssm.calculate are for the forward strand only. To
obtain the scores on the reverse strand, you can take the reverse
complement of the PSSM: 
<<>>> rpssm = pssm.reverse_complement()
  >>> rpssm.calculate(test_seq)
  array([ -9.43458748,  -3.06172252,  -7.18665981,  -7.76216221,
          -2.04066086,  -4.26466274,   4.60124254,  -4.2480607 ,
          -8.73414803,  -2.26503372,  -6.49598789,  -5.64668512,
          -8.73414803, -10.84962463,  -4.82356262,  -4.82356262,
          -5.64668512,  -8.73414803,  -4.15613794,  -5.6796999 ,
           4.60124254,  -4.2480607 ], dtype=float32)
>>



14.6.3 Selecting a score threshold
==================================

  If you want to use a less arbitrary way of selecting thresholds, you
can explore the distribution of PSSM scores. Since the space for a score
distribution grows exponentially with motif length, we are using an
approximation with a given precision to keep computation cost
manageable:
<<>>> distribution = pssm.distribution(background=background,
precision=10**4)
>>
   The distribution object can be used to determine a number of
different thresholds. We can specify the requested false-positive rate
(probability of "finding" a motif instance in background generated
sequence):
<<>>> threshold = distribution.threshold_fpr(0.01)
  >>> print("%5.3f" % threshold)
  4.009
>>
   or the false-negative rate (probability of "not finding" an instance
generated from the motif):
<<>>> threshold = distribution.threshold_fnr(0.1)
  >>> print("%5.3f" % threshold)
  -0.510
>>
   or a threshold (approximately) satisfying some relation between the
false-positive rate and the false-negative rate (fnr/fpr~ t):
<<>>> threshold = distribution.threshold_balanced(1000)
  >>> print("%5.3f" % threshold)
  6.241
>>
   or a threshold satisfying (roughly) the equality between the -log of
the false-positive rate and the information content (as used in patser
software by Hertz and Stormo):
<<>>> threshold = distribution.threshold_patser()
  >>> print("%5.3f" % threshold)
  0.346
>>

  For example, in case of our motif, you can get the threshold giving
you exactly the same results (for this sequence) as searching for
instances with balanced threshold with rate of 1000.
<<>>> threshold = distribution.threshold_fpr(0.01)
  >>> print("%5.3f" % threshold)
  4.009
  >>> for position, score in pssm.search(test_seq, threshold=threshold):
  ...     print("Position %d: score = %5.3f" % (position, score))
  ...
  Position 0: score = 5.622
  Position -20: score = 4.601
  Position 13: score = 5.738
  Position -6: score = 4.601
>>



14.7 Each motif object has an associated Position-Specific Scoring
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=
Matrix
*=*=*=


  To facilitate searching for potential TFBSs using PSSMs, both the
position-weight matrix and the position-specific scoring matrix are
associated with each motif. Using the Arnt motif as an example: 
<<>>> from Bio import motifs
  >>> with open("Arnt.sites") as handle:
  ...     motif = motifs.read(handle, "sites")
  ...
  >>> print(motif.counts)
          0      1      2      3      4      5
  A:   4.00  19.00   0.00   0.00   0.00   0.00
  C:  16.00   0.00  20.00   0.00   0.00   0.00
  G:   0.00   1.00   0.00  20.00   0.00  20.00
  T:   0.00   0.00   0.00   0.00  20.00   0.00
  <BLANKLINE>
  >>> print(motif.pwm)
          0      1      2      3      4      5
  A:   0.20   0.95   0.00   0.00   0.00   0.00
  C:   0.80   0.00   1.00   0.00   0.00   0.00
  G:   0.00   0.05   0.00   1.00   0.00   1.00
  T:   0.00   0.00   0.00   0.00   1.00   0.00
  <BLANKLINE>
  >>> print(motif.pssm)
          0      1      2      3      4      5
  A:  -0.32   1.93   -inf   -inf   -inf   -inf
  C:   1.68   -inf   2.00   -inf   -inf   -inf
  G:   -inf  -2.32   -inf   2.00   -inf   2.00
  T:   -inf   -inf   -inf   -inf   2.00   -inf
  <BLANKLINE>
>>
   The negative infinities appear here because the corresponding entry
in the frequency matrix is 0, and we are using zero pseudocounts by
default:
<<>>> for letter in "ACGT":
  ...     print("%s: %4.2f" % (letter, motif.pseudocounts[letter]))
  ...
  A: 0.00
  C: 0.00
  G: 0.00
  T: 0.00
>>
   If you change the .pseudocounts attribute, the position-frequency
matrix and the position-specific scoring matrix are recalculated
automatically:
<<>>> motif.pseudocounts = 3.0
  >>> for letter in "ACGT":
  ...     print("%s: %4.2f" % (letter, motif.pseudocounts[letter]))
  ...
  A: 3.00
  C: 3.00
  G: 3.00
  T: 3.00
  >>> print(motif.pwm)
          0      1      2      3      4      5
  A:   0.22   0.69   0.09   0.09   0.09   0.09
  C:   0.59   0.09   0.72   0.09   0.09   0.09
  G:   0.09   0.12   0.09   0.72   0.09   0.72
  T:   0.09   0.09   0.09   0.09   0.72   0.09
  <BLANKLINE>
>>

<<>>> print(motif.pssm)
          0      1      2      3      4      5
  A:  -0.19   1.46  -1.42  -1.42  -1.42  -1.42
  C:   1.25  -1.42   1.52  -1.42  -1.42  -1.42
  G:  -1.42  -1.00  -1.42   1.52  -1.42   1.52
  T:  -1.42  -1.42  -1.42  -1.42   1.52  -1.42
  <BLANKLINE>
>>
   You can also set the .pseudocounts to a dictionary over the four
nucleotides if you want to use different pseudocounts for them. Setting
motif.pseudocounts to None resets it to its default value of zero.
  The position-specific scoring matrix depends on the background
distribution, which is uniform by default:
<<>>> for letter in "ACGT":
  ...     print("%s: %4.2f" % (letter, motif.background[letter]))
  ...
  A: 0.25
  C: 0.25
  G: 0.25
  T: 0.25
>>
   Again, if you modify the background distribution, the
position-specific scoring matrix is recalculated:
<<>>> motif.background = {"A": 0.2, "C": 0.3, "G": 0.3, "T": 0.2}
  >>> print(motif.pssm)
          0      1      2      3      4      5
  A:   0.13   1.78  -1.09  -1.09  -1.09  -1.09
  C:   0.98  -1.68   1.26  -1.68  -1.68  -1.68
  G:  -1.68  -1.26  -1.68   1.26  -1.68   1.26
  T:  -1.09  -1.09  -1.09  -1.09   1.85  -1.09
  <BLANKLINE>
>>
   Setting motif.background to None resets it to a uniform distribution:
<<>>> motif.background = None
  >>> for letter in "ACGT":
  ...     print("%s: %4.2f" % (letter, motif.background[letter]))
  ...
  A: 0.25
  C: 0.25
  G: 0.25
  T: 0.25
>>
   If you set motif.background equal to a single value, it will be
interpreted as the GC content:
<<>>> motif.background = 0.8
  >>> for letter in "ACGT":
  ...     print("%s: %4.2f" % (letter, motif.background[letter]))
  ...
  A: 0.10
  C: 0.40
  G: 0.40
  T: 0.10
>>
   Note that you can now calculate the mean of the PSSM scores over the
background against which it was computed:
<<>>> print("%f" % motif.pssm.mean(motif.background))
  4.703928
>>
   as well as its standard deviation:
<<>>> print("%f" % motif.pssm.std(motif.background))
  3.290900
>>
   and its distribution:
<<>>> distribution =
motif.pssm.distribution(background=motif.background)
  >>> threshold = distribution.threshold_fpr(0.01)
  >>> print("%f" % threshold)
  3.854375
>>

  Note that the position-weight matrix and the position-specific scoring
matrix are recalculated each time you call motif.pwm or motif.pssm,
respectively. If speed is an issue and you want to use the PWM or PSSM
repeatedly, you can save them as a variable, as in 
<<>>> pssm = motif.pssm
>>



14.8 Comparing motifs
*=*=*=*=*=*=*=*=*=*=*

    Once we have more than one motif, we might want to compare them.
  Before we start comparing motifs, I should point out that motif
boundaries are usually quite arbitrary. This means we often need to
compare motifs of different lengths, so comparison needs to involve some
kind of alignment. This means we have to take into account two things: 

   - alignment of motifs 
   - some function to compare aligned motifs 
   To align the motifs, we use ungapped alignment of PSSMs and
substitute zeros for any missing columns at the beginning and end of the
matrices. This means that effectively we are using the background
distribution for columns missing from the PSSM. The distance function
then returns the minimal distance between motifs, as well as the
corresponding offset in their alignment.
  To give an example, let us first load another motif, which is similar
to our test motif m: 
<<>>> with open("REB1.pfm") as handle:
  ...    m_reb1 = motifs.read(handle, "pfm")
  ...
  >>> m_reb1.consensus
  Seq('GTTACCCGG')
  >>> print(m_reb1.counts)
          0      1      2      3      4      5      6      7      8
  A:  30.00   0.00   0.00 100.00   0.00   0.00   0.00   0.00  15.00
  C:  10.00   0.00   0.00   0.00 100.00 100.00 100.00   0.00  15.00
  G:  50.00   0.00   0.00   0.00   0.00   0.00   0.00  60.00  55.00
  T:  10.00 100.00 100.00   0.00   0.00   0.00   0.00  40.00  15.00
  <BLANKLINE>
>>

  To make the motifs comparable, we choose the same values for the
pseudocounts and the background distribution as our motif m:
<<>>> m_reb1.pseudocounts = {"A":0.6, "C": 0.4, "G": 0.4, "T": 0.6}
  >>> m_reb1.background = {"A":0.3,"C":0.2,"G":0.2,"T":0.3}
  >>> pssm_reb1 = m_reb1.pssm
  >>> print(pssm_reb1)
          0      1      2      3      4      5      6      7      8
  A:   0.00  -5.67  -5.67   1.72  -5.67  -5.67  -5.67  -5.67  -0.97
  C:  -0.97  -5.67  -5.67  -5.67   2.30   2.30   2.30  -5.67  -0.41
  G:   1.30  -5.67  -5.67  -5.67  -5.67  -5.67  -5.67   1.57   1.44
  T:  -1.53   1.72   1.72  -5.67  -5.67  -5.67  -5.67   0.41  -0.97
  <BLANKLINE>
>>
   We'll compare these motifs using the Pearson correlation. Since we
want it to resemble a distance measure, we actually take 1-r, where r is
the Pearson correlation coefficient (PCC):
<<>>> distance, offset = pssm.dist_pearson(pssm_reb1)
  >>> print("distance = %5.3g" % distance)
  distance = 0.239
  >>> print(offset)
  -2
>>
   This means that the best PCC between motif m and m_reb1 is obtained
with the following alignment: 
<<m:      bbTACGCbb
  m_reb1: GTTACCCGG
>>
   where b stands for background distribution. The PCC itself is roughly
1-0.239=0.761.


14.9 De novo motif finding
*=*=*=*=*=*=*=*=*=*=*=*=*=


  Currently, Biopython has only limited support for de novo motif
finding. Namely, we support running xxmotif and also parsing of MEME.
Since the number of motif finding tools is growing rapidly,
contributions of new parsers are welcome.


14.9.1 MEME
===========

  Let's assume, you have run MEME on sequences of your choice with your
favorite parameters and saved the output in the file meme.out. You can
retrieve the motifs reported by MEME by running the following piece of
code:
<<>>> from Bio import motifs
  >>> with open("meme.psp_test.classic.zoops.xml") as handle:
  ...     motifsM = motifs.parse(handle, "meme")
  ...
>>

<<>>> motifsM
  [<Bio.motifs.meme.Motif object at 0xc356b0>]
>>

  Besides the most wanted list of motifs, the result object contains
more useful information, accessible through properties with
self-explanatory names: 

   - .alphabet 
   - .datafile 
   - .sequences 
   - .version 
   - .command 

  The motifs returned by the MEME Parser can be treated exactly like
regular Motif objects (with instances), they also provide some extra
functionality, by adding additional information about the instances.
<<>>> motifsM[0].consensus
  Seq('GCTTATGTAA')
  >>> motifsM[0].instances[0].sequence_name
  'iYFL005W'
  >>> motifsM[0].instances[0].sequence_id
  'sequence_15'
  >>> motifsM[0].instances[0].start
  480
  >>> motifsM[0].instances[0].strand
  '+'
>>

<<>>> motifsM[0].instances[0].pvalue
  1.97e-06
>>



14.10 Useful links
*=*=*=*=*=*=*=*=*=




   - Sequence motif (6) in wikipedia 
   - PWM (7) in wikipedia 
   - Consensus sequence (8) in wikipedia 
   - Comparison of different motif finding programs (9) 

-----------------------------------


 (1) http://fraenkel-nsf.csbi.mit.edu/TAMO/
 (2) https://weblogo.berkeley.edu
 (3) http://jaspar.genereg.net
 (4) http://jaspar.genereg.net
 (5) http://tfbs.genereg.net/
 (6) https://en.wikipedia.org/wiki/Sequence_motif
 (7) https://en.wikipedia.org/wiki/Position_weight_matrix
 (8) https://en.wikipedia.org/wiki/Consensus_sequence
 (9) http://bio.cs.washington.edu/assessment/


Chapter 15  Cluster analysis
****************************

  Cluster analysis is the grouping of items into clusters based on the
similarity of the items to each other. In bioinformatics, clustering is
widely used in gene expression data analysis to find groups of genes
with similar gene expression profiles. This may identify functionally
related genes, as well as suggest the function of presently unknown
genes.
  The Biopython module Bio.Cluster provides commonly used clustering
algorithms and was designed with the application to gene expression data
in mind. However, this module can also be used for cluster analysis of
other types of data. Bio.Cluster and the underlying C Clustering Library
is described by De Hoon et al. [].
  The following four clustering approaches are implemented in
Bio.Cluster: 

   - Hierarchical clustering (pairwise centroid-, single-, complete-,
   and average-linkage); 
   - k-means, k-medians, and k-medoids clustering; 
   - Self-Organizing Maps; 
   - Principal Component Analysis. 



Data representation
===================

  The data to be clustered are represented by a n X m Numerical Python
array data. Within the context of gene expression data clustering,
typically the rows correspond to different genes whereas the columns
correspond to different experimental conditions. The clustering
algorithms in Bio.Cluster can be applied both to rows (genes) and to
columns (experiments).


Missing values
==============

  The n X m Numerical Python integer array mask indicates if any of the
values in data are missing. If mask[i, j] == 0, then data[i, j] is
missing and is ignored in the analysis.


Random number generator
=======================

  The k-means/medians/medoids clustering algorithms and Self-Organizing
Maps (SOMs) include the use of a random number generator. The uniform
random number generator in Bio.Cluster is based on the algorithm by
L'Ecuyer [], while random numbers following the binomial distribution
are generated using the BTPE algorithm by Kachitvichyanukul and
Schmeiser []. The random number generator is initialized automatically
during its first call. As this random number generator uses a
combination of two multiplicative linear congruential generators, two
(integer) seeds are needed for initialization, for which we use the
system-supplied random number generator rand (in the C standard
library). We initialize this generator by calling srand with the epoch
time in seconds, and use the first two random numbers generated by rand
as seeds for the uniform random number generator in Bio.Cluster.


15.1 Distance functions
*=*=*=*=*=*=*=*=*=*=*=*


  In order to cluster items into groups based on their similarity, we
should first define what exactly we mean by similar. Bio.Cluster
provides eight distance functions, indicated by a single character, to
measure similarity, or conversely, distance: 

   - 'e': Euclidean distance; 
   - 'b': City-block distance. 
   - 'c': Pearson correlation coefficient; 
   - 'a': Absolute value of the Pearson correlation coefficient; 
   - 'u': Uncentered Pearson correlation (equivalent to the cosine of
   the angle between two data vectors); 
   - 'x': Absolute uncentered Pearson correlation; 
   - 's': Spearman's rank correlation; 
   - 'k': Kendall's tau. 
   The first two are true distance functions that satisfy the triangle
inequality: 
       (       )    (       )   (       )                        
      d( u , v )<= d( u , w )+ d( w , v ) for all   u ,  v ,  w ,
       (--- ---)    (--- ---)   (--- ---)          ---  ---  --- 
   and are therefore referred to as metrics. In everyday language, this
means that the shortest distance between two points is a straight line.
  The remaining six distance measures are related to the correlation
coefficient, where the distance d is defined in terms of the correlation
r by d=1-r. Note that these distance functions are semi-metrics that do
not satisfy the triangle inequality. For example, for 
                                 (      ) 
                              u =(1,0,-1);
                             --- (      ) 

                                  (     ) 
                               v =(1,1,0);
                              --- (     ) 

                                  (     ) 
                               w =(0,1,1);
                              --- (     ) 
   we find a Pearson distance d(u,w) = 1.8660, while d(u,v)+d(v,w) =
1.6340.


Euclidean distance
==================

  In Bio.Cluster, we define the Euclidean distance as 
                                  n           
                                 --           
                              1  \   (     )2 
                          d = -- /   (x -y ) .
                              n  --  ( i  i)  
                                 i=1          
   Only those terms are included in the summation for which both x_i and
y_i are present, and the denominator n is chosen accordingly. As the
expression data x_i and y_i are subtracted directly from each other, we
should make sure that the expression data are properly normalized when
using the Euclidean distance.


City-block distance
===================

  The city-block distance, alternatively known as the Manhattan
distance, is related to the Euclidean distance. Whereas the Euclidean
distance corresponds to the length of the shortest path between two
points, the city-block distance is the sum of distances along each
dimension. As gene expression data tend to have missing values, in
Bio.Cluster we define the city-block distance as the sum of distances
divided by the number of dimensions: 
                                  n          
                                 --          
                              1  \   |     | 
                          d = -- /   |x -y |.
                              n  --  | i  i| 
                                 i=1         
   This is equal to the distance you would have to walk between two
points in a city, where you have to walk along city blocks. As for the
Euclidean distance, the expression data are subtracted directly from
each other, and we should therefore make sure that they are properly
normalized.


The Pearson correlation coefficient
===================================

  The Pearson correlation coefficient is defined as 
                               (       )(       ) 
                            n  (x  -x  )(y  -y  ) 
                         1 --  ( i     )( i     ) 
                     r = - \   (------ )(------ ),
                         n /   (       )(       ) 
                           --  (sigma  )(sigma  ) 
                           i=1 (     x )(     y ) 
   in which x, y are the sample mean of x and y respectively, and
sigma_x, sigma_y are the sample standard deviation of x and y. The
Pearson correlation coefficient is a measure for how well a straight
line can be fitted to a scatterplot of x and y. If all the points in the
scatterplot lie on a straight line, the Pearson correlation coefficient
is either +1 or -1, depending on whether the slope of line is positive
or negative. If the Pearson correlation coefficient is equal to zero,
there is no correlation between x and y.
  The Pearson distance is then defined as 

                            d  equiv 1 - r.
                             P             
   As the Pearson correlation coefficient lies between -1 and 1, the
Pearson distance lies between 0 and 2.


Absolute Pearson correlation
============================

  By taking the absolute value of the Pearson correlation, we find a
number between 0 and 1. If the absolute value is 1, all the points in
the scatter plot lie on a straight line with either a positive or a
negative slope. If the absolute value is equal to zero, there is no
correlation between x and y.
  The corresponding distance is defined as 
                                        | | 
                           d  equiv 1 - |r|,
                            A           | | 
   where r is the Pearson correlation coefficient. As the absolute value
of the Pearson correlation coefficient lies between 0 and 1, the
corresponding distance lies between 0 and 1 as well.
  In the context of gene expression experiments, the absolute
correlation is equal to 1 if the gene expression profiles of two genes
are either exactly the same or exactly opposite. The absolute
correlation coefficient should therefore be used with care.


Uncentered correlation (cosine of the angle)
============================================

  In some cases, it may be preferable to use the uncentered correlation
instead of the regular Pearson correlation coefficient. The uncentered
correlation is defined as 
                              (         )(         ) 
                           n  (   x     )(   y     ) 
                        1 --  (    i    )(    i    ) 
                   r  = - \   (-------- )(-------- ),
                    U   n /   (     (0) )(     (0) ) 
                          --  (sigma    )(sigma    ) 
                          i=1 (     x   )(     y   ) 
   where 
                                       (   n   )        
                                       (  --   )        
                               (0)     (1 \   2)1/2     
                          sigma     =  (- /  x )   ;    
                               x       (n --  i)        
                                       (  i=1  )        
                                       (   n   )        
                                       (  --   )        
                               (0)     (1 \   2)1/2     
                          sigma     =  (- /  y )   .    
                               y       (n --  i)        
                                       (  i=1  )        
   This is the same expression as for the regular Pearson correlation
coefficient, except that the sample means x, y are set equal to zero.
The uncentered correlation may be appropriate if there is a zero
reference state. For instance, in the case of gene expression data given
in terms of log-ratios, a log-ratio equal to zero corresponds to the
green and red signal being equal, which means that the experimental
manipulation did not affect the gene expression.
  The distance corresponding to the uncentered correlation coefficient
is defined as 

                            d  equiv 1 - r ,
                             U            U 
   where r_U is the uncentered correlation. As the uncentered
correlation coefficient lies between -1 and 1, the corresponding
distance lies between 0 and 2.
  The uncentered correlation is equal to the cosine of the angle of the
two data vectors in n-dimensional space, and is often referred to as
such.


Absolute uncentered correlation
===============================

  As for the regular Pearson correlation, we can define a distance
measure using the absolute value of the uncentered correlation: 
                                        |  | 
                          d   equiv 1 - |r |,
                           AU           | U| 
   where r_U is the uncentered correlation coefficient. As the absolute
value of the uncentered correlation coefficient lies between 0 and 1,
the corresponding distance lies between 0 and 1 as well.
  Geometrically, the absolute value of the uncentered correlation is
equal to the cosine between the supporting lines of the two data vectors
(i.e., the angle without taking the direction of the vectors into
consideration).


Spearman rank correlation
=========================

  The Spearman rank correlation is an example of a non-parametric
similarity measure, and tends to be more robust against outliers than
the Pearson correlation.
  To calculate the Spearman rank correlation, we replace each data value
by their rank if we would order the data in each vector by their value.
We then calculate the Pearson correlation between the two rank vectors
instead of the data vectors.
  As in the case of the Pearson correlation, we can define a distance
measure corresponding to the Spearman rank correlation as 

                            d  equiv 1 - r ,
                             S            S 
   where r_S is the Spearman rank correlation.


Kendall's tau
=============

  Kendall's tau is another example of a non-parametric similarity
measure. It is similar to the Spearman rank correlation, but instead of
the ranks themselves only the relative ranks are used to calculate tau
(see Snedecor & Cochran []).
  We can define a distance measure corresponding to Kendall's tau as 

                           d  equiv 1 - tau.
                            K               
   As Kendall's tau is always between -1 and 1, the corresponding
distance will be between 0 and 2.


Weighting
=========

  For most of the distance functions available in Bio.Cluster, a weight
vector can be applied. The weight vector contains weights for the items
in the data vector. If the weight for item i is w_i, then that item is
treated as if it occurred w_i times in the data. The weight do not have
to be integers.


Calculating the distance matrix
===============================

  The distance matrix is a square matrix with all pairwise distances
between the items in data, and can be calculated by the function
distancematrix in the Bio.Cluster module: 
<<>>> from Bio.Cluster import distancematrix
  >>> matrix = distancematrix(data)
>>
   where the following arguments are defined: 

   - data (required)
 Array containing the data for the items. 
   - mask (default: None)
 Array of integers showing which data are missing. If mask[i, j] == 0,
   then data[i, j] is missing. If mask is None, then all data are
   present. 
   - weight (default: None)
 The weights to be used when calculating distances. If weight is None,
   then equal weights are assumed. 
   - transpose (default: 0)
 Determines if the distances between the rows of data are to be
   calculated (transpose is False), or between the columns of data
   (transpose is True). 
   - dist (default: 'e', Euclidean distance)
 Defines the distance function to be used (see ??). 

  To save memory, the distance matrix is returned as a list of 1D
arrays. The number of columns in each row is equal to the row number.
Hence, the first row has zero elements. For example,
<<>>> from numpy import array
  >>> from Bio.Cluster import distancematrix
  >>> data = array([[0, 1,  2,  3],
  ...               [4, 5,  6,  7],
  ...               [8, 9, 10, 11],
  ...               [1, 2,  3,  4]])
  >>> distances = distancematrix(data, dist='e')
>>
   yields a distance matrix 
<<>>> distances
  [array([], dtype=float64), array([ 16.]), array([ 64.,  16.]), array([
 1.,   9.,  49.])]
>>
   which can be rewritten as 
<<[array([], dtype=float64), array([16.0]), array([64.0, 16.0]),
array([1.0, 9.0, 49.0])]
>>
   This corresponds to the distance matrix: 
                         (  0  16 64 1     )  
                         (  16 0  16 9     )  
                         (  64 16 0  49    ). 
                         (   1 9  49  0    )  



15.2 Calculating cluster properties
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*




Calculating the cluster centroids
=================================

  The centroid of a cluster can be defined either as the mean or as the
median of each dimension over all cluster items. The function
clustercentroids in Bio.Cluster can be used to calculate either:
<<>>> from Bio.Cluster import clustercentroids
  >>> cdata, cmask = clustercentroids(data)
>>
   where the following arguments are defined: 

   - data (required)
 Array containing the data for the items. 
   - mask (default: None)
 Array of integers showing which data are missing. If mask[i, j] == 0,
   then data[i, j] is missing. If mask is None, then all data are
   present. 
   - clusterid (default: None)
 Vector of integers showing to which cluster each item belongs. If
   clusterid is None, then all items are assumed to belong to the same
   cluster. 
   - method (default: 'a')
 Specifies whether the arithmetic mean (method=='a') or the median
   (method=='m') is used to calculate the cluster center. 
   - transpose (default: 0)
 Determines if the centroids of the rows of data are to be calculated
   (transpose is False), or the centroids of the columns of data
   (transpose is True). 

  This function returns the tuple (cdata, cmask). The centroid data are
stored in the 2D Numerical Python array cdata, with missing data
indicated by the 2D Numerical Python integer array cmask. The dimensions
of these arrays are (number of clusters, number of columns) if transpose
is 0, or (number of rows, number of clusters) if transpose is 1. Each
row (if transpose is 0) or column (if transpose is 1) contains the
averaged data corresponding to the centroid of each cluster.


Calculating the distance between clusters
=========================================

  Given a distance function between items, we can define the distance
between two clusters in several ways. The distance between the
arithmetic means of the two clusters is used in pairwise
centroid-linkage clustering and in k-means clustering. In k-medoids
clustering, the distance between the medians of the two clusters is used
instead. The shortest pairwise distance between items of the two
clusters is used in pairwise single-linkage clustering, while the
longest pairwise distance is used in pairwise maximum-linkage
clustering. In pairwise average-linkage clustering, the distance between
two clusters is defined as the average over the pairwise distances.
  To calculate the distance between two clusters, use 
<<>>> from Bio.Cluster import clusterdistance
  >>> distance = clusterdistance(data)
>>
   where the following arguments are defined: 

   - data (required)
 Array containing the data for the items. 
   - mask (default: None)
 Array of integers showing which data are missing. If mask[i, j] == 0,
   then data[i, j] is missing. If mask is None, then all data are
   present. 
   - weight (default: None)
 The weights to be used when calculating distances. If weight is None,
   then equal weights are assumed. 
   - index1 (default: 0)
 A list containing the indices of the items belonging to the first
   cluster. A cluster containing only one item i can be represented
   either as a list [i], or as an integer i. 
   - index2 (default: 0)
 A list containing the indices of the items belonging to the second
   cluster. A cluster containing only one items i can be represented
   either as a list [i], or as an integer i. 
   - method (default: 'a')
 Specifies how the distance between clusters is defined: 

      - 'a': Distance between the two cluster centroids (arithmetic
      mean); 
      - 'm': Distance between the two cluster centroids (median); 
      - 's': Shortest pairwise distance between items in the two
      clusters; 
      - 'x': Longest pairwise distance between items in the two
      clusters; 
      - 'v': Average over the pairwise distances between items in the
      two clusters. 

   - dist (default: 'e', Euclidean distance)
 Defines the distance function to be used (see ??). 
   - transpose (default: 0)
 If transpose is False, calculate the distance between the rows of data.
   If transpose is True, calculate the distance between the columns of
   data. 



15.3 Partitioning algorithms
*=*=*=*=*=*=*=*=*=*=*=*=*=*=


  Partitioning algorithms divide items into k clusters such that the sum
of distances over the items to their cluster centers is minimal. The
number of clusters k is specified by the user. Three partitioning
algorithms are available in Bio.Cluster: 

   - k-means clustering 
   - k-medians clustering 
   - k-medoids clustering 
   These algorithms differ in how the cluster center is defined. In
k-means clustering, the cluster center is defined as the mean data
vector averaged over all items in the cluster. Instead of the mean, in
k-medians clustering the median is calculated for each dimension in the
data vector. Finally, in k-medoids clustering the cluster center is
defined as the item which has the smallest sum of distances to the other
items in the cluster. This clustering algorithm is suitable for cases in
which the distance matrix is known but the original data matrix is not
available, for example when clustering proteins based on their
structural similarity.
  The expectation-maximization (EM) algorithm is used to find this
partitioning into k groups. In the initialization of the EM algorithm,
we randomly assign items to clusters. To ensure that no empty clusters
are produced, we use the binomial distribution to randomly choose the
number of items in each cluster to be one or more. We then randomly
permute the cluster assignments to items such that each item has an
equal probability to be in any cluster. Each cluster is thus guaranteed
to contain at least one item.
  We then iterate: 

   - Calculate the centroid of each cluster, defined as either the mean,
   the median, or the medoid of the cluster; 
   - Calculate the distances of each item to the cluster centers; 
   - For each item, determine which cluster centroid is closest; 
   - Reassign each item to its closest cluster, or stop the iteration if
   no further item reassignments take place. 

  To avoid clusters becoming empty during the iteration, in k-means and
k-medians clustering the algorithm keeps track of the number of items in
each cluster, and prohibits the last remaining item in a cluster from
being reassigned to a different cluster. For k-medoids clustering, such
a check is not needed, as the item that functions as the cluster
centroid has a zero distance to itself, and will therefore never be
closer to a different cluster.
  As the initial assignment of items to clusters is done randomly,
usually a different clustering solution is found each time the EM
algorithm is executed. To find the optimal clustering solution, the
k-means algorithm is repeated many times, each time starting from a
different initial random clustering. The sum of distances of the items
to their cluster center is saved for each run, and the solution with the
smallest value of this sum will be returned as the overall clustering
solution.
  How often the EM algorithm should be run depends on the number of
items being clustered. As a rule of thumb, we can consider how often the
optimal solution was found; this number is returned by the partitioning
algorithms as implemented in this library. If the optimal solution was
found many times, it is unlikely that better solutions exist than the
one that was found. However, if the optimal solution was found only
once, there may well be other solutions with a smaller within-cluster
sum of distances. If the number of items is large (more than several
hundreds), it may be difficult to find the globally optimal solution.
  The EM algorithm terminates when no further reassignments take place.
We noticed that for some sets of initial cluster assignments, the EM
algorithm fails to converge due to the same clustering solution
reappearing periodically after a small number of iteration steps. We
therefore check for the occurrence of such periodic solutions during the
iteration. After a given number of iteration steps, the current
clustering result is saved as a reference. By comparing the clustering
result after each subsequent iteration step to the reference state, we
can determine if a previously encountered clustering result is found. In
such a case, the iteration is halted. If after a given number of
iterations the reference state has not yet been encountered, the current
clustering solution is saved to be used as the new reference state.
Initially, ten iteration steps are executed before resaving the
reference state. This number of iteration steps is doubled each time, to
ensure that periodic behavior with longer periods can also be detected.


k-means and k-medians
=====================

  The k-means and k-medians algorithms are implemented as the function
kcluster in Bio.Cluster:
<<>>> from Bio.Cluster import kcluster
  >>> clusterid, error, nfound = kcluster(data)
>>
   where the following arguments are defined: 

   - data (required)
 Array containing the data for the items. 
   - nclusters (default: 2)
 The number of clusters k. 
   - mask (default: None)
 Array of integers showing which data are missing. If mask[i, j] == 0,
   then data[i, j] is missing. If mask is None, then all data are
   present. 
   - weight (default: None)
 The weights to be used when calculating distances. If weight is None,
   then equal weights are assumed. 
   - transpose (default: 0)
 Determines if rows (transpose is 0) or columns (transpose is 1) are to
   be clustered. 
   - npass (default: 1)
 The number of times the k-means/-medians clustering algorithm is
   performed, each time with a different (random) initial condition. If
   initialid is given, the value of npass is ignored and the clustering
   algorithm is run only once, as it behaves deterministically in that
   case. 
   - method (default: a)
 describes how the center of a cluster is found: 

      - method=='a': arithmetic mean (k-means clustering); 
      - method=='m': median (k-medians clustering). 
  For other values of method, the arithmetic mean is used. 
   - dist (default: 'e', Euclidean distance)
 Defines the distance function to be used (see ??). Whereas all eight
   distance measures are accepted by kcluster, from a theoretical
   viewpoint it is best to use the Euclidean distance for the k-means
   algorithm, and the city-block distance for k-medians. 
   - initialid (default: None)
 Specifies the initial clustering to be used for the EM algorithm. If
   initialid is None, then a different random initial clustering is used
   for each of the npass runs of the EM algorithm. If initialid is not
   None, then it should be equal to a 1D array containing the cluster
   number (between 0 and nclusters-1) for each item. Each cluster should
   contain at least one item. With the initial clustering specified, the
   EM algorithm is deterministic. 

  This function returns a tuple (clusterid, error, nfound), where
clusterid is an integer array containing the number of the cluster to
which each row or cluster was assigned, error is the within-cluster sum
of distances for the optimal clustering solution, and nfound is the
number of times this optimal solution was found.


k-medoids clustering
====================

  The kmedoids routine performs k-medoids clustering on a given set of
items, using the distance matrix and the number of clusters passed by
the user: 
<<>>> from Bio.Cluster import kmedoids
  >>> clusterid, error, nfound = kmedoids(distance)
>>
   where the following arguments are defined: , nclusters=2, npass=1,
initialid=None)|


   - distance (required)
 The matrix containing the distances between the items; this matrix can
   be specified in three ways: 

      - as a 2D Numerical Python array (in which only the left-lower
      part of the array will be accessed): 
      <<distance = array([[0.0, 1.1, 2.3], [1.1, 0.0, 4.5], [2.3, 4.5,
      0.0]])
      >>

      - as a 1D Numerical Python array containing consecutively the
      distances in the left-lower part of the distance matrix: 
      <<distance = array([1.1, 2.3, 4.5])
      >>

      - as a list containing the rows of the left-lower part of the
      distance matrix: 
      <<distance = [array([]), array([1.1]), array([2.3, 4.5])]
      >>

  These three expressions correspond to the same distance matrix. 
   - nclusters (default: 2)
 The number of clusters k. 
   - npass (default: 1)
 The number of times the k-medoids clustering algorithm is performed,
   each time with a different (random) initial condition. If initialid
   is given, the value of npass is ignored, as the clustering algorithm
   behaves deterministically in that case. 
   - initialid (default: None)
 Specifies the initial clustering to be used for the EM algorithm. If
   initialid is None, then a different random initial clustering is used
   for each of the npass runs of the EM algorithm. If initialid is not
   None, then it should be equal to a 1D array containing the cluster
   number (between 0 and nclusters-1) for each item. Each cluster should
   contain at least one item. With the initial clustering specified, the
   EM algorithm is deterministic. 

  This function returns a tuple (clusterid, error, nfound), where
clusterid is an array containing the number of the cluster to which each
item was assigned, error is the within-cluster sum of distances for the
optimal k-medoids clustering solution, and nfound is the number of times
the optimal solution was found. Note that the cluster number in
clusterid is defined as the item number of the item representing the
cluster centroid.


15.4 Hierarchical clustering
*=*=*=*=*=*=*=*=*=*=*=*=*=*=


  Hierarchical clustering methods are inherently different from the
k-means clustering method. In hierarchical clustering, the similarity in
the expression profile between genes or experimental conditions are
represented in the form of a tree structure. This tree structure can be
shown graphically by programs such as Treeview and Java Treeview, which
has contributed to the popularity of hierarchical clustering in the
analysis of gene expression data.
  The first step in hierarchical clustering is to calculate the distance
matrix, specifying all the distances between the items to be clustered.
Next, we create a node by joining the two closest items. Subsequent
nodes are created by pairwise joining of items or nodes based on the
distance between them, until all items belong to the same node. A tree
structure can then be created by retracing which items and nodes were
merged. Unlike the EM algorithm, which is used in k-means clustering,
the complete process of hierarchical clustering is deterministic.
  Several flavors of hierarchical clustering exist, which differ in how
the distance between subnodes is defined in terms of their members. In
Bio.Cluster, pairwise single, maximum, average, and centroid linkage are
available.


   - In pairwise single-linkage clustering, the distance between two
   nodes is defined as the shortest distance among the pairwise
   distances between the members of the two nodes. 
   - In pairwise maximum-linkage clustering, alternatively known as
   pairwise complete-linkage clustering, the distance between two nodes
   is defined as the longest distance among the pairwise distances
   between the members of the two nodes. 
   - In pairwise average-linkage clustering, the distance between two
   nodes is defined as the average over all pairwise distances between
   the items of the two nodes. 
   - In pairwise centroid-linkage clustering, the distance between two
   nodes is defined as the distance between their centroids. The
   centroids are calculated by taking the mean over all the items in a
   cluster. As the distance from each newly formed node to existing
   nodes and items need to be calculated at each step, the computing
   time of pairwise centroid-linkage clustering may be significantly
   longer than for the other hierarchical clustering methods. Another
   peculiarity is that (for a distance measure based on the Pearson
   correlation), the distances do not necessarily increase when going up
   in the clustering tree, and may even decrease. This is caused by an
   inconsistency between the centroid calculation and the distance
   calculation when using the Pearson correlation: Whereas the Pearson
   correlation effectively normalizes the data for the distance
   calculation, no such normalization occurs for the centroid
   calculation. 

  For pairwise single-, complete-, and average-linkage clustering, the
distance between two nodes can be found directly from the distances
between the individual items. Therefore, the clustering algorithm does
not need access to the original gene expression data, once the distance
matrix is known. For pairwise centroid-linkage clustering, however, the
centroids of newly formed subnodes can only be calculated from the
original data and not from the distance matrix.
  The implementation of pairwise single-linkage hierarchical clustering
is based on the SLINK algorithm (R. Sibson, 1973), which is much faster
and more memory-efficient than a straightforward implementation of
pairwise single-linkage clustering. The clustering result produced by
this algorithm is identical to the clustering solution found by the
conventional single-linkage algorithm. The single-linkage hierarchical
clustering algorithm implemented in this library can be used to cluster
large gene expression data sets, for which conventional hierarchical
clustering algorithms fail due to excessive memory requirements and
running time.


Representing a hierarchical clustering solution
===============================================

  The result of hierarchical clustering consists of a tree of nodes, in
which each node joins two items or subnodes. Usually, we are not only
interested in which items or subnodes are joined at each node, but also
in their similarity (or distance) as they are joined. To store one node
in the hierarchical clustering tree, we make use of the class Node,
which defined in Bio.Cluster. An instance of Node has three attributes: 

   - left 
   - right 
   - distance 
   Here, left and right are integers referring to the two items or
subnodes that are joined at this node, and distance is the distance
between them. The items being clustered are numbered from 0 to (number
of items - 1), while clusters are numbered from -1 to -(number of
items-1). Note that the number of nodes is one less than the number of
items.
  To create a new Node object, we need to specify left and right;
distance is optional.
<<>>> from Bio.Cluster import Node
  >>> Node(2, 3)
  (2, 3): 0
  >>> Node(2, 3, 0.91)
  (2, 3): 0.91
>>

  The attributes left, right, and distance of an existing Node object
can be modified directly:
<<>>> node = Node(4, 5)
  >>> node.left = 6
  >>> node.right = 2
  >>> node.distance = 0.73
  >>> node
  (6, 2): 0.73
>>
   An error is raised if left and right are not integers, or if distance
cannot be converted to a floating-point value.
  The Python class Tree represents a full hierarchical clustering
solution. A Tree object can be created from a list of Node objects:
<<>>> from Bio.Cluster import Node, Tree
  >>> nodes = [Node(1, 2, 0.2), Node(0, 3, 0.5), Node(-2, 4, 0.6),
Node(-1, -3, 0.9)]
  >>> tree = Tree(nodes)
  >>> print(tree)
  (1, 2): 0.2
  (0, 3): 0.5
  (-2, 4): 0.6
  (-1, -3): 0.9
>>

  The Tree initializer checks if the list of nodes is a valid
hierarchical clustering result:
<<>>> nodes = [Node(1, 2, 0.2), Node(0, 2, 0.5)]
  >>> Tree(nodes)
  Traceback (most recent call last):
    File "<stdin>", line 1, in ?
  ValueError: Inconsistent tree
>>

  Individual nodes in a Tree object can be accessed using square
brackets:
<<>>> nodes = [Node(1, 2, 0.2), Node(0, -1, 0.5)]
  >>> tree = Tree(nodes)
  >>> tree[0]
  (1, 2): 0.2
  >>> tree[1]
  (0, -1): 0.5
  >>> tree[-1]
  (0, -1): 0.5
>>

  As a Tree object is immutable, we cannot change individual nodes in a
Tree object. However, we can convert the tree to a list of nodes, modify
this list, and create a new tree from this list:
<<>>> tree = Tree([Node(1, 2, 0.1), Node(0, -1, 0.5), Node(-2, 3, 0.9)])
  >>> print(tree)
  (1, 2): 0.1
  (0, -1): 0.5
  (-2, 3): 0.9
  >>> nodes = tree[:]
  >>> nodes[0] = Node(0, 1, 0.2)
  >>> nodes[1].left = 2
  >>> tree = Tree(nodes)
  >>> print(tree)
  (0, 1): 0.2
  (2, -1): 0.5
  (-2, 3): 0.9
>>

  This guarantees that any Tree object is always well-formed.
  To display a hierarchical clustering solution with visualization
programs such as Java Treeview, it is better to scale all node distances
such that they are between zero and one. This can be accomplished by
calling the scale method on an existing Tree object: 
<<>>> tree.scale()
>>
   This method takes no arguments, and returns None.
  Before drawing the tree, you may also want to reorder the tree nodes.
A hierarchical clustering solution of n items can be drawn as 2^n-1
different but equivalent dendrograms by switching the left and right
subnode at each node. The tree.sort(order) method visits each node in
the hierarchical clustering tree and verifies if the average order value
of the left subnode is less than or equal to the average order value of
the right subnode. If not, the left and right subnodes are exchanged.
Here, the order values of the items are given by the user. In the
resulting dendrogram, items in the left-to-right order will tend to have
increasing order values. The method will return the indices of the
elements in the left-to-right order after sorting: 
<<>>> indices = tree.sort(order)
>>
   such that item indices[i] will occur at position i in the dendrogram.
  After hierarchical clustering, the items can be grouped into k
clusters based on the tree structure stored in the Tree object by
cutting the tree: 
<<>>> clusterid = tree.cut(nclusters=1)
>>
   where nclusters (defaulting to 1) is the desired number of clusters
k. This method ignores the top k-1 linking events in the tree structure,
resulting in k separated clusters of items. The number of clusters k
should be positive, and less than or equal to the number of items. This
method returns an array clusterid containing the number of the cluster
to which each item is assigned. Clusters are numbered 0 to k-1 in their
left-to-right order in the dendrogram.


Performing hierarchical clustering
==================================

  To perform hierarchical clustering, use the treecluster function in
Bio.Cluster. 
<<>>> from Bio.Cluster import treecluster
  >>> tree = treecluster(data)
>>
   where the following arguments are defined:


   - data
 Array containing the data for the items. 
   - mask (default: None)
 Array of integers showing which data are missing. If mask[i, j] == 0,
   then data[i, j] is missing. If mask is None, then all data are
   present. 
   - weight (default: None)
 The weights to be used when calculating distances. If weight is None,
   then equal weights are assumed. 
   - transpose (default: 0)
 Determines if rows (transpose is False) or columns (transpose is True)
   are to be clustered. 
   - method (default: 'm')
 defines the linkage method to be used: 

      - method=='s': pairwise single-linkage clustering 
      - method=='m': pairwise maximum- (or complete-) linkage clustering

      - method=='c': pairwise centroid-linkage clustering 
      - method=='a': pairwise average-linkage clustering 

   - dist (default: 'e', Euclidean distance)
 Defines the distance function to be used (see ??). 

  To apply hierarchical clustering on a precalculated distance matrix,
specify the distancematrix argument when calling treecluster function
instead of the data argument: 
<<>>> from Bio.Cluster import treecluster
  >>> tree = treecluster(distancematrix=distance)
>>
   In this case, the following arguments are defined: 

   - distancematrix
 The distance matrix, which can be specified in three ways: 

      - as a 2D Numerical Python array (in which only the left-lower
      part of the array will be accessed): 
      <<distance = array([[0.0, 1.1, 2.3], [1.1, 0.0, 4.5], [2.3, 4.5,
      0.0]])
      >>

      - as a 1D Numerical Python array containing consecutively the
      distances in the left-lower part of the distance matrix: 
      <<distance = array([1.1, 2.3, 4.5])
      >>

      - as a list containing the rows of the left-lower part of the
      distance matrix: 
      <<distance = [array([]), array([1.1]), array([2.3, 4.5])]
      >>

  These three expressions correspond to the same distance matrix. As
   treecluster may shuffle the values in the distance matrix as part of
   the clustering algorithm, be sure to save this array in a different
   variable before calling treecluster if you need it later. 
   - method
 The linkage method to be used: 

      - method=='s': pairwise single-linkage clustering 
      - method=='m': pairwise maximum- (or complete-) linkage clustering

      - method=='a': pairwise average-linkage clustering 
  While pairwise single-, maximum-, and average-linkage clustering can
   be calculated from the distance matrix alone, pairwise
   centroid-linkage cannot. 

  When calling treecluster, either data or distancematrix should be
None.
  This function returns a Tree object. This object contains (number of
items - 1) nodes, where the number of items is the number of rows if
rows were clustered, or the number of columns if columns were clustered.
Each node describes a pairwise linking event, where the node attributes
left and right each contain the number of one item or subnode, and
distance the distance between them. Items are numbered from 0 to (number
of items - 1), while clusters are numbered -1 to -(number of items-1).


15.5 Self-Organizing Maps
*=*=*=*=*=*=*=*=*=*=*=*=*


  Self-Organizing Maps (SOMs) were invented by Kohonen to describe
neural networks (see for instance Kohonen, 1997 []). Tamayo (1999) first
applied Self-Organizing Maps to gene expression data [].
  SOMs organize items into clusters that are situated in some topology.
Usually a rectangular topology is chosen. The clusters generated by SOMs
are such that neighboring clusters in the topology are more similar to
each other than clusters far from each other in the topology.
  The first step to calculate a SOM is to randomly assign a data vector
to each cluster in the topology. If rows are being clustered, then the
number of elements in each data vector is equal to the number of
columns.
  An SOM is then generated by taking rows one at a time, and finding
which cluster in the topology has the closest data vector. The data
vector of that cluster, as well as those of the neighboring clusters,
are adjusted using the data vector of the row under consideration. The
adjustment is given by 
                                     (                 ) 
               Delta  x      = tau ? ( x     -  x      ).
                     ---cell         (---row   ---cell ) 
   The parameter tau is a parameter that decreases at each iteration
step. We have used a simple linear function of the iteration step: 
                                       (    i ) 
                       tau = tau     ? (1 - --),
                                init   (    n ) 
   tau_init is the initial value of tau as specified by the user, i is
the number of the current iteration step, and n is the total number of
iteration steps to be performed. While changes are made rapidly in the
beginning of the iteration, at the end of iteration only small changes
are made.
  All clusters within a radius R are adjusted to the gene under
consideration. This radius decreases as the calculation progresses as 
                                     (    i ) 
                          R = R    ? (1 - --),
                               max   (    n ) 
   in which the maximum radius is defined as 
                                 ( 2    2)1/2 
                          R    = (N  + N )   ,
                           max   ( x    y)    
   where (N_x, N_y) are the dimensions of the rectangle defining the
topology.
  The function somcluster implements the complete algorithm to calculate
a Self-Organizing Map on a rectangular grid. First it initializes the
random number generator. The node data are then initialized using the
random number generator. The order in which genes or samples are used to
modify the SOM is also randomized. The total number of iterations in the
SOM algorithm is specified by the user.
  To run somcluster, use 
<<>>> from Bio.Cluster import somcluster
  >>> clusterid, celldata = somcluster(data)
>>
   where the following arguments are defined: 

   - data (required)
 Array containing the data for the items. 
   - mask (default: None)
 Array of integers showing which data are missing. If mask[i, j] == 0,
   then data[i, j] is missing. If mask is None, then all data are
   present. 
   - weight (default: None)
 contains the weights to be used when calculating distances. If weight
   is None, then equal weights are assumed. 
   - transpose (default: 0)
 Determines if rows (transpose is 0) or columns (transpose is 1) are to
   be clustered. 
   - nxgrid, nygrid (default: 2, 1)
 The number of cells horizontally and vertically in the rectangular grid
   on which the Self-Organizing Map is calculated. 
   - inittau (default: 0.02)
 The initial value for the parameter tau that is used in the SOM
   algorithm. The default value for inittau is 0.02, which was used in
   Michael Eisen's Cluster/TreeView program. 
   - niter (default: 1)
 The number of iterations to be performed. 
   - dist (default: 'e', Euclidean distance)
 Defines the distance function to be used (see ??). 

  This function returns the tuple (clusterid, celldata): 

   - clusterid:
 An array with two columns, where the number of rows is equal to the
   number of items that were clustered. Each row contains the x and y
   coordinates of the cell in the rectangular SOM grid to which the item
   was assigned. 
   - celldata:
 An array with dimensions (nxgrid, nygrid, number of columns) if rows
   are being clustered, or (nxgrid, nygrid, number of rows) if columns
   are being clustered. Each element [ix][iy] of this array is a 1D
   vector containing the gene expression data for the centroid of the
   cluster in the grid cell with coordinates [ix][iy]. 



15.6 Principal Component Analysis
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*


  Principal Component Analysis (PCA) is a widely used technique for
analyzing multivariate data. A practical example of applying Principal
Component Analysis to gene expression data is presented by Yeung and
Ruzzo (2001) [].
  In essence, PCA is a coordinate transformation in which each row in
the data matrix is written as a linear sum over basis vectors called
principal components, which are ordered and chosen such that each
maximally explains the remaining variance in the data vectors. For
example, an n X 3 data matrix can be represented as an ellipsoidal cloud
of n points in three dimensional space. The first principal component is
the longest axis of the ellipsoid, the second principal component the
second longest axis of the ellipsoid, and the third principal component
is the shortest axis. Each row in the data matrix can be reconstructed
as a suitable linear combination of the principal components. However,
in order to reduce the dimensionality of the data, usually only the most
important principal components are retained. The remaining variance
present in the data is then regarded as unexplained variance.
  The principal components can be found by calculating the eigenvectors
of the covariance matrix of the data. The corresponding eigenvalues
determine how much of the variance present in the data is explained by
each principal component.
  Before applying principal component analysis, typically the mean is
subtracted from each column in the data matrix. In the example above,
this effectively centers the ellipsoidal cloud around its centroid in 3D
space, with the principal components describing the variation of points
in the ellipsoidal cloud with respect to their centroid.
  The function pca below first uses the singular value decomposition to
calculate the eigenvalues and eigenvectors of the data matrix. The
singular value decomposition is implemented as a translation in C of the
Algol procedure svd [], which uses Householder bidiagonalization and a
variant of the QR algorithm. The principal components, the coordinates
of each data vector along the principal components, and the eigenvalues
corresponding to the principal components are then evaluated and
returned in decreasing order of the magnitude of the eigenvalue. If data
centering is desired, the mean should be subtracted from each column in
the data matrix before calling the pca routine.
  To apply Principal Component Analysis to a rectangular matrix data,
use 
<<>>> from Bio.Cluster import pca
  >>> columnmean, coordinates, components, eigenvalues = pca(data)
>>
   This function returns a tuple columnmean, coordinates, components,
eigenvalues: 

   - columnmean
 Array containing the mean over each column in data. 
   - coordinates
 The coordinates of each row in data with respect to the principal
   components. 
   - components
 The principal components. 
   - eigenvalues
 The eigenvalues corresponding to each of the principal components. 
   The original matrix data can be recreated by calculating columnmean +
 dot(coordinates, components).


15.7 Handling Cluster/TreeView-type files
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*


  Cluster/TreeView are GUI-based codes for clustering gene expression
data. They were originally written by Michael Eisen (1) while at
Stanford University. Bio.Cluster contains functions for reading and
writing data files that correspond to the format specified for
Cluster/TreeView. In particular, by saving a clustering result in that
format, TreeView can be used to visualize the clustering results. We
recommend using Alok Saldanha's http://jtreeview.sourceforge.net/Java
TreeView program, which can display hierarchical as well as k-means
clustering results.
  An object of the class Record contains all information stored in a
Cluster/TreeView-type data file. To store the information contained in
the data file in a Record object, we first open the file and then read
it:
<<>>> from Bio import Cluster
  >>> with open("mydatafile.txt") as handle:
  ...     record = Cluster.read(handle)
  ...
>>
   This two-step process gives you some flexibility in the source of the
data. For example, you can use
<<>>> import gzip # Python standard library
  >>> handle = gzip.open("mydatafile.txt.gz", "rt")
>>
   to open a gzipped file, or 
<<>>> from urllib.request import urlopen
  >>> from io import TextIOWrapper
  >>> handle =
TextIOWrapper(urlopen("https://raw.githubusercontent.com/biopython/biopy
thon/master/Tests/Cluster/cyano.txt"))
>>
   to open a file stored on the Internet before calling read.
  The read command reads the tab-delimited text file mydatafile.txt
containing gene expression data in the format specified for Michael
Eisen's Cluster/TreeView program. In this file format, rows represent
genes and columns represent samples or observations. For a simple time
course, a minimal input file would look like this:
   --------------------------------------------------------------------


           -----------------------------------------------------
           |YORF   |0 minutes|30 minutes|1 hour|2 hours|4 hours|
           |YAL001C|        1|       1.3|   2.4|    5.8|   2.4 |
           |YAL002W|      0.9|       0.8|   0.7|    0.5|   0.2 |
           |YAL003W|      0.8|       2.1|   4.2|   10.1|  10.1 |
           |YAL005C|      1.1|       1.3|   0.8|       |   0.4 |
           |YAL010C|      1.2|         1|   1.1|    4.5|   8.3 |
           -----------------------------------------------------


   --------------------------------------------------------------------

  Each row (gene) has an identifier that always goes in the first
column. In this example, we are using yeast open reading frame codes.
Each column (sample) has a label in the first row. In this example, the
labels describe the time at which a sample was taken. The first column
of the first row contains a special field that tells the program what
kind of objects are in each row. In this case, YORF stands for yeast
open reading frame. This field can be any alpha-numeric value. The
remaining cells in the table contain data for the appropriate gene and
sample. The 5.8 in row 2 column 4 means that the observed value for gene
YAL001C at 2 hours was 5.8. Missing values are acceptable and are
designated by empty cells (e.g. YAL004C at 2 hours).
  The input file may contain additional information. A maximal input
file would look like this:
   --------------------------------------------------------------------


   ---------------------------------------------------------------------
                                   -----
   |YORF   |NAME                      |GWEIGHT|GORDER|  0| 30|  1|   2| 
                                    4 |
   |EWEIGHT|                          |       |      |  1|  1|  1|   1| 
                                    0 |
   |EORDER |                          |       |      |  5|  3|  2|   1| 
                                    1 |
   |YAL001C|TFIIIC 138 KD SUBUNIT     |      1|     1|  1|1.3|2.4| 5.8|
                                   2.4 |
   |YAL002W|UNKNOWN                   |    0.4|     3|0.9|0.8|0.7| 0.5|
                                   0.2 |
             |YAL003W|ELONGATION FACTOR EF1-BETA|    0.4|    
                         2|0.8|2.1|4.2|10.1|10.1 |
   |YAL005C|CYTOSOLIC HSP70           |    0.4|     5|1.1|1.3|0.8|    |
                                   0.4 |
   ---------------------------------------------------------------------
                                   -----


   --------------------------------------------------------------------

  The added columns NAME, GWEIGHT, and GORDER and rows EWEIGHT and
EORDER are optional. The NAME column allows you to specify a label for
each gene that is distinct from the ID in column 1.
  A Record object has the following attributes:


   - data
 The data array containing the gene expression data. Genes are stored
   row-wise, while samples are stored column-wise.

   - mask
 This array shows which elements in the data array, if any, are missing.
   If mask[i, j] == 0, then data[i, j] is missing. If no data were found
   to be missing, mask is set to None.

   - geneid
 This is a list containing a unique description for each gene (i.e., ORF
   numbers).

   - genename
 This is a list containing a description for each gene (i.e., gene
   name). If not present in the data file, genename is set to None.

   - gweight
 The weights that are to be used to calculate the distance in expression
   profile between genes. If not present in the data file, gweight is
   set to None.

   - gorder
 The preferred order in which genes should be stored in an output file.
   If not present in the data file, gorder is set to None.

   - expid
 This is a list containing a description of each sample, e.g.
   experimental condition.

   - eweight
 The weights that are to be used to calculate the distance in expression
   profile between samples. If not present in the data file, eweight is
   set to None.

   - eorder
 The preferred order in which samples should be stored in an output
   file. If not present in the data file, eorder is set to None.

   - uniqid
 The string that was used instead of UNIQID in the data file. 

  After loading a Record object, each of these attributes can be
accessed and modified directly. For example, the data can be
log-transformed by taking the logarithm of record.data.


Calculating the distance matrix
===============================

  To calculate the distance matrix between the items stored in the
record, use 
<<>>> matrix = record.distancematrix()
>>
   where the following arguments are defined: 

   - transpose (default: 0)
 Determines if the distances between the rows of data are to be
   calculated (transpose is False), or between the columns of data
   (transpose is True). 
   - dist (default: 'e', Euclidean distance)
 Defines the distance function to be used (see ??). 

  This function returns the distance matrix as a list of rows, where the
number of columns of each row is equal to the row number (see section
??).


Calculating the cluster centroids
=================================

  To calculate the centroids of clusters of items stored in the record,
use 
<<>>> cdata, cmask = record.clustercentroids()
>>



   - clusterid (default: None)
 Vector of integers showing to which cluster each item belongs. If
   clusterid is not given, then all items are assumed to belong to the
   same cluster. 
   - method (default: 'a')
 Specifies whether the arithmetic mean (method=='a') or the median
   (method=='m') is used to calculate the cluster center. 
   - transpose (default: 0)
 Determines if the centroids of the rows of data are to be calculated
   (transpose is False), or the centroids of the columns of data
   (transpose is True). 

  This function returns the tuple cdata, cmask; see section ?? for a
description.


Calculating the distance between clusters
=========================================
   To calculate the distance between clusters of items stored in the
record, use 
<<>>> distance = record.clusterdistance()
>>
   where the following arguments are defined: 

   - index1 (default: 0)
 A list containing the indices of the items belonging to the first
   cluster. A cluster containing only one item i can be represented
   either as a list [i], or as an integer i. 
   - index2 (default: 0)
 A list containing the indices of the items belonging to the second
   cluster. A cluster containing only one item i can be represented
   either as a list [i], or as an integer i. 
   - method (default: 'a')
 Specifies how the distance between clusters is defined: 

      - 'a': Distance between the two cluster centroids (arithmetic
      mean); 
      - 'm': Distance between the two cluster centroids (median); 
      - 's': Shortest pairwise distance between items in the two
      clusters; 
      - 'x': Longest pairwise distance between items in the two
      clusters; 
      - 'v': Average over the pairwise distances between items in the
      two clusters. 

   - dist (default: 'e', Euclidean distance)
 Defines the distance function to be used (see ??). 
   - transpose (default: 0)
 If transpose is False, calculate the distance between the rows of data.
   If transpose is True, calculate the distance between the columns of
   data. 



Performing hierarchical clustering
==================================

  To perform hierarchical clustering on the items stored in the record,
use 
<<>>> tree = record.treecluster()
>>
   where the following arguments are defined: 

   - transpose (default: 0)
 Determines if rows (transpose is False) or columns (transpose is True)
   are to be clustered. 
   - method (default: 'm')
 defines the linkage method to be used: 

      - method=='s': pairwise single-linkage clustering 
      - method=='m': pairwise maximum- (or complete-) linkage clustering

      - method=='c': pairwise centroid-linkage clustering 
      - method=='a': pairwise average-linkage clustering 

   - dist (default: 'e', Euclidean distance)
 Defines the distance function to be used (see ??). 
   - transpose
 Determines if genes or samples are being clustered. If transpose is
   False, genes (rows) are being clustered. If transpose is True,
   samples (columns) are clustered. 

  This function returns a Tree object. This object contains (number of
items - 1) nodes, where the number of items is the number of rows if
rows were clustered, or the number of columns if columns were clustered.
Each node describes a pairwise linking event, where the node attributes
left and right each contain the number of one item or subnode, and
distance the distance between them. Items are numbered from 0 to (number
of items - 1), while clusters are numbered -1 to -(number of items-1).


Performing k-means or k-medians clustering
==========================================

  To perform k-means or k-medians clustering on the items stored in the
record, use 
<<>>> clusterid, error, nfound = record.kcluster()
>>
   where the following arguments are defined: 

   - nclusters (default: 2)
 The number of clusters k. 
   - transpose (default: 0)
 Determines if rows (transpose is 0) or columns (transpose is 1) are to
   be clustered. 
   - npass (default: 1)
 The number of times the k-means/-medians clustering algorithm is
   performed, each time with a different (random) initial condition. If
   initialid is given, the value of npass is ignored and the clustering
   algorithm is run only once, as it behaves deterministically in that
   case. 
   - method (default: a)
 describes how the center of a cluster is found: 

      - method=='a': arithmetic mean (k-means clustering); 
      - method=='m': median (k-medians clustering). 
  For other values of method, the arithmetic mean is used. 
   - dist (default: 'e', Euclidean distance)
 Defines the distance function to be used (see ??). 

  This function returns a tuple (clusterid, error, nfound), where
clusterid is an integer array containing the number of the cluster to
which each row or cluster was assigned, error is the within-cluster sum
of distances for the optimal clustering solution, and nfound is the
number of times this optimal solution was found.


Calculating a Self-Organizing Map
=================================

  To calculate a Self-Organizing Map of the items stored in the record,
use 
<<>>> clusterid, celldata = record.somcluster()
>>
   where the following arguments are defined: 

   - transpose (default: 0)
 Determines if rows (transpose is 0) or columns (transpose is 1) are to
   be clustered. 
   - nxgrid, nygrid (default: 2, 1)
 The number of cells horizontally and vertically in the rectangular grid
   on which the Self-Organizing Map is calculated. 
   - inittau (default: 0.02)
 The initial value for the parameter tau that is used in the SOM
   algorithm. The default value for inittau is 0.02, which was used in
   Michael Eisen's Cluster/TreeView program. 
   - niter (default: 1)
 The number of iterations to be performed. 
   - dist (default: 'e', Euclidean distance)
 Defines the distance function to be used (see ??). 

  This function returns the tuple (clusterid, celldata): 

   - clusterid:
 An array with two columns, where the number of rows is equal to the
   number of items that were clustered. Each row contains the x and y
   coordinates of the cell in the rectangular SOM grid to which the item
   was assigned. 
   - celldata:
 An array with dimensions (nxgrid, nygrid, number of columns) if rows
   are being clustered, or (nxgrid, nygrid, number of rows) if columns
   are being clustered. Each element [ix][iy] of this array is a 1D
   vector containing the gene expression data for the centroid of the
   cluster in the grid cell with coordinates [ix][iy]. 



Saving the clustering result
============================

  To save the clustering result, use 
<<>>> record.save(jobname, geneclusters, expclusters)
>>
   where the following arguments are defined: 

   - jobname
 The string jobname is used as the base name for names of the files that
   are to be saved. 
   - geneclusters
 This argument describes the gene (row-wise) clustering result. In case
   of k-means clustering, this is a 1D array containing the number of
   the cluster each gene belongs to. It can be calculated using
   kcluster. In case of hierarchical clustering, geneclusters is a Tree
   object. 
   - expclusters
 This argument describes the (column-wise) clustering result for the
   experimental conditions. In case of k-means clustering, this is a 1D
   array containing the number of the cluster each experimental
   condition belongs to. It can be calculated using kcluster. In case of
   hierarchical clustering, expclusters is a Tree object. 

  This method writes the text file jobname.cdt, jobname.gtr,
jobname.atr, jobname*.kgg, and/or jobname*.kag for subsequent reading by
the Java TreeView program. If geneclusters and expclusters are both
None, this method only writes the text file jobname.cdt; this file can
subsequently be read into a new Record object.

15.8 Example calculation
*=*=*=*=*=*=*=*=*=*=*=*=


  This is an example of a hierarchical clustering calculation, using
single linkage clustering for genes and maximum linkage clustering for
experimental conditions. As the Euclidean distance is being used for
gene clustering, it is necessary to scale the node distances genetree
such that they are all between zero and one. This is needed for the Java
TreeView code to display the tree diagram correctly. To cluster the
experimental conditions, the uncentered correlation is being used. No
scaling is needed in this case, as the distances in exptree are already
between zero and two.
  The example data cyano.txt can be found in Biopython's Tests/Cluster
subdirectory and is from the paper [, Hihara et al., 2001].
<<>>> from Bio import Cluster
  >>> with open("cyano.txt") as handle:
  ...     record = Cluster.read(handle)
  ...
  >>> genetree = record.treecluster(method="s")
  >>> genetree.scale()
  >>> exptree = record.treecluster(dist="u", transpose=1)
  >>> record.save("cyano_result", genetree, exptree)
>>

  This will create the files cyano_result.cdt, cyano_result.gtr, and
cyano_result.atr.
  Similarly, we can save a k-means clustering solution:
<<>>> from Bio import Cluster
  >>> with open("cyano.txt") as handle:
  ...     record = Cluster.read(handle)
  ...
  >>> (geneclusters, error, ifound) = record.kcluster(nclusters=5,
npass=1000)
  >>> (expclusters, error, ifound) = record.kcluster(nclusters=2,
npass=100, transpose=1)
  >>> record.save("cyano_result", geneclusters, expclusters)
>>

  This will create the files cyano_result_K_G2_A2.cdt,
cyano_result_K_G2.kgg, and cyano_result_K_A2.kag.   
-----------------------------------


 (1) http://rana.lbl.gov


Chapter 16  Supervised learning methods
***************************************

  Note the supervised learning methods described in this chapter all
require Numerical Python (numpy) to be installed.


16.1 The Logistic Regression Model
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=




16.1.1 Background and Purpose
=============================

  Logistic regression is a supervised learning approach that attempts to
distinguish K classes from each other using a weighted sum of some
predictor variables x_i. The logistic regression model is used to
calculate the weights beta_i of the predictor variables. In Biopython,
the logistic regression model is currently implemented for two classes
only (K = 2); the number of predictor variables has no predefined limit.
  As an example, let's try to predict the operon structure in bacteria.
An operon is a set of adjacent genes on the same strand of DNA that are
transcribed into a single mRNA molecule. Translation of the single mRNA
molecule then yields the individual proteins. For Bacillus subtilis,
whose data we will be using, the average number of genes in an operon is
about 2.4.
  As a first step in understanding gene regulation in bacteria, we need
to know the operon structure. For about 10% of the genes in Bacillus
subtilis, the operon structure is known from experiments. A supervised
learning method can be used to predict the operon structure for the
remaining 90% of the genes.
  For such a supervised learning approach, we need to choose some
predictor variables x_i that can be measured easily and are somehow
related to the operon structure. One predictor variable might be the
distance in base pairs between genes. Adjacent genes belonging to the
same operon tend to be separated by a relatively short distance, whereas
adjacent genes in different operons tend to have a larger space between
them to allow for promoter and terminator sequences. Another predictor
variable is based on gene expression measurements. By definition, genes
belonging to the same operon have equal gene expression profiles, while
genes in different operons are expected to have different expression
profiles. In practice, the measured expression profiles of genes in the
same operon are not quite identical due to the presence of measurement
errors. To assess the similarity in the gene expression profiles, we
assume that the measurement errors follow a normal distribution and
calculate the corresponding log-likelihood score.
  We now have two predictor variables that we can use to predict if two
adjacent genes on the same strand of DNA belong to the same operon: 

   - x_1: the number of base pairs between them; 
   - x_2: their similarity in expression profile. 

  In a logistic regression model, we use a weighted sum of these two
predictors to calculate a joint score S: 

                 S = beta  + beta  x  + beta  x . (16.1)
                         0       1  1       2  2        
   The logistic regression model gives us appropriate values for the
parameters beta_0, beta_1, beta_2 using two sets of example genes: 

   - OP: Adjacent genes, on the same strand of DNA, known to belong to
   the same operon; 
   - NOP: Adjacent genes, on the same strand of DNA, known to belong to
   different operons. 

  In the logistic regression model, the probability of belonging to a
class depends on the score via the logistic function. For the two
classes OP and NOP, we can write this as 


                               exp(beta  + beta  x  + beta  x )         

                                       0       1  1       2  2          

              Pr(OP|x , x )  =  ----------------------------------   
                                (16.2) 
                   1   2                                                

                              1+exp(beta  + beta  x  + beta  x )        

                                        0       1  1       2  2         

                                              1                         

                              ----------------------------------        

             Pr(NOP|x , x )  =                                       
                                (16.3) 
                   1   2      1+exp(beta  + beta  x  + beta  x )        

                                        0       1  1       2  2         

   Using a set of gene pairs for which it is known whether they belong
to the same operon (class OP) or to different operons (class NOP), we
can calculate the weights beta_0, beta_1, beta_2 by maximizing the
log-likelihood corresponding to the probability functions (??) and (??).


16.1.2 Training the logistic regression model
=============================================

   --------------------------------------------------------------------


     Table 16.1: Adjacent gene pairs known to belong to the same operon
   (class OP) or to different operons (class NOP). Intergene distances
   are negative if the two genes overlap.

   ---------------------------------------------------------------------
                                  -------
      |   Gene pair   |Intergene distance (x_1)|Gene expression score
                               (x_2)|Class|
   ---------------------------------------------------------------------
                                  -------
   |cotJA --- cotJB|          -53           |          -200.78         
                                  | OP  |
   | yesK --- yesL |          117           |          -267.14         
                                  | OP  |
   | lplA --- lplB |           57           |          -163.47         
                                  | OP  |
   | lplB --- lplC |           16           |          -190.30         
                                  | OP  |
   | lplC --- lplD |           11           |          -220.94         
                                  | OP  |
   | lplD --- yetF |           85           |          -193.94         
                                  | OP  |
   | yfmT --- yfmS |           16           |          -182.71         
                                  | OP  |
   | yfmF --- yfmE |           15           |          -180.41         
                                  | OP  |
   | citS --- citT |          -26           |          -181.73         
                                  | OP  |
   | citM --- yflN |           58           |          -259.87         
                                  | OP  |
   | yfiI --- yfiJ |          126           |          -414.53         
                                  | NOP |
   | lipB --- yfiQ |          191           |          -249.57         
                                  | NOP |
   | yfiU --- yfiV |          113           |          -265.28         
                                  | NOP |
   | yfhH --- yfhI |          145           |          -312.99         
                                  | NOP |
   | cotY --- cotX |          154           |          -213.83         
                                  | NOP |
   | yjoB --- rapA |          147           |          -380.85         
                                  | NOP |
   | ptsI --- splA |           93           |          -291.13         
                                  |NOP  |
   ---------------------------------------------------------------------
                                  -------


   --------------------------------------------------------------------

  Table ?? lists some of the Bacillus subtilis gene pairs for which the
operon structure is known. Let's calculate the logistic regression model
from these data:
<<>>> from Bio import LogisticRegression
  >>> xs = [[-53, -200.78],
  ...       [117, -267.14],
  ...       [57, -163.47],
  ...       [16, -190.30],
  ...       [11, -220.94],
  ...       [85, -193.94],
  ...       [16, -182.71],
  ...       [15, -180.41],
  ...       [-26, -181.73],
  ...       [58, -259.87],
  ...       [126, -414.53],
  ...       [191, -249.57],
  ...       [113, -265.28],
  ...       [145, -312.99],
  ...       [154, -213.83],
  ...       [147, -380.85],
  ...       [93, -291.13]]
  ...
  >>> ys = [1,
  ...       1,
  ...       1,
  ...       1,
  ...       1,
  ...       1,
  ...       1,
  ...       1,
  ...       1,
  ...       1,
  ...       0,
  ...       0,
  ...       0,
  ...       0,
  ...       0,
  ...       0,
  ...       0]
  ...
  >>> model = LogisticRegression.train(xs, ys)
>>

  Here, xs and ys are the training data: xs contains the predictor
variables for each gene pair, and ys specifies if the gene pair belongs
to the same operon (1, class OP) or different operons (0, class NOP).
The resulting logistic regression model is stored in model, which
contains the weights beta_0, beta_1, and beta_2:
<<>>> model.beta
  [8.9830290157144681, -0.035968960444850887, 0.02181395662983519]
>>

  Note that beta_1 is negative, as gene pairs with a shorter intergene
distance have a higher probability of belonging to the same operon
(class OP). On the other hand, beta_2 is positive, as gene pairs
belonging to the same operon typically have a higher similarity score of
their gene expression profiles. The parameter beta_0 is positive due to
the higher prevalence of operon gene pairs than non-operon gene pairs in
the training data.
  The function train has two optional arguments: update_fn and typecode.
The update_fn can be used to specify a callback function, taking as
arguments the iteration number and the log-likelihood. With the callback
function, we can for example track the progress of the model calculation
(which uses a Newton-Raphson iteration to maximize the log-likelihood
function of the logistic regression model):
<<>>> def show_progress(iteration, loglikelihood):
          print("Iteration:", iteration, "Log-likelihood function:",
loglikelihood)
  >>>
  >>> model = LogisticRegression.train(xs, ys, update_fn=show_progress)
  Iteration: 0 Log-likelihood function: -11.7835020695
  Iteration: 1 Log-likelihood function: -7.15886767672
  Iteration: 2 Log-likelihood function: -5.76877209868
  Iteration: 3 Log-likelihood function: -5.11362294338
  Iteration: 4 Log-likelihood function: -4.74870642433
  Iteration: 5 Log-likelihood function: -4.50026077146
  Iteration: 6 Log-likelihood function: -4.31127773737
  Iteration: 7 Log-likelihood function: -4.16015043396
  Iteration: 8 Log-likelihood function: -4.03561719785
  Iteration: 9 Log-likelihood function: -3.93073282192
  Iteration: 10 Log-likelihood function: -3.84087660929
  Iteration: 11 Log-likelihood function: -3.76282560605
  Iteration: 12 Log-likelihood function: -3.69425027154
  Iteration: 13 Log-likelihood function: -3.6334178602
  Iteration: 14 Log-likelihood function: -3.57900855837
  Iteration: 15 Log-likelihood function: -3.52999671386
  Iteration: 16 Log-likelihood function: -3.48557145163
  Iteration: 17 Log-likelihood function: -3.44508206139
  Iteration: 18 Log-likelihood function: -3.40799948447
  Iteration: 19 Log-likelihood function: -3.3738885624
  Iteration: 20 Log-likelihood function: -3.3423876581
  Iteration: 21 Log-likelihood function: -3.31319343769
  Iteration: 22 Log-likelihood function: -3.2860493346
  Iteration: 23 Log-likelihood function: -3.2607366863
  Iteration: 24 Log-likelihood function: -3.23706784091
  Iteration: 25 Log-likelihood function: -3.21488073614
  Iteration: 26 Log-likelihood function: -3.19403459259
  Iteration: 27 Log-likelihood function: -3.17440646052
  Iteration: 28 Log-likelihood function: -3.15588842703
  Iteration: 29 Log-likelihood function: -3.13838533947
  Iteration: 30 Log-likelihood function: -3.12181293595
  Iteration: 31 Log-likelihood function: -3.10609629966
  Iteration: 32 Log-likelihood function: -3.09116857282
  Iteration: 33 Log-likelihood function: -3.07696988017
  Iteration: 34 Log-likelihood function: -3.06344642288
  Iteration: 35 Log-likelihood function: -3.05054971191
  Iteration: 36 Log-likelihood function: -3.03823591619
  Iteration: 37 Log-likelihood function: -3.02646530573
  Iteration: 38 Log-likelihood function: -3.01520177394
  Iteration: 39 Log-likelihood function: -3.00441242601
  Iteration: 40 Log-likelihood function: -2.99406722296
  Iteration: 41 Log-likelihood function: -2.98413867259
>>

  The iteration stops once the increase in the log-likelihood function
is less than 0.01. If no convergence is reached after 500 iterations,
the train function returns with an AssertionError.
  The optional keyword typecode can almost always be ignored. This
keyword allows the user to choose the type of Numeric matrix to use. In
particular, to avoid memory problems for very large problems, it may be
necessary to use single-precision floats (Float8, Float16, etc.) rather
than double, which is used by default.


16.1.3 Using the logistic regression model for classification
=============================================================

  Classification is performed by calling the classify function. Given a
logistic regression model and the values for x_1 and x_2 (e.g. for a
gene pair of unknown operon structure), the classify function returns 1
or 0, corresponding to class OP and class NOP, respectively. For
example, let's consider the gene pairs yxcE, yxcD and yxiB, yxiA:
   --------------------------------------------------------------------


     Table 16.2: Adjacent gene pairs of unknown operon status.

     -----------------------------------------------------------------
     |  Gene pair  |Intergene distance x_1|Gene expression score x_2 |
     -----------------------------------------------------------------
     |yxcE --- yxcD|          6           |     -173.143442352       |
     |yxiB --- yxiA|         309          |     -271.005880394       |
     -----------------------------------------------------------------


   --------------------------------------------------------------------

  The logistic regression model classifies yxcE, yxcD as belonging to
the same operon (class OP), while yxiB, yxiA are predicted to belong to
different operons: 
<<>>> print("yxcE, yxcD:", LogisticRegression.classify(model, [6,
-173.143442352]))
  yxcE, yxcD: 1
  >>> print("yxiB, yxiA:", LogisticRegression.classify(model, [309,
-271.005880394]))
  yxiB, yxiA: 0
>>
   (which, by the way, agrees with the biological literature).
  To find out how confident we can be in these predictions, we can call
the calculate function to obtain the probabilities (equations (??) and
??) for class OP and NOP. For yxcE, yxcD we find 
<<>>> q, p = LogisticRegression.calculate(model, [6, -173.143442352])
  >>> print("class OP: probability =", p, "class NOP: probability =", q)
  class OP: probability = 0.993242163503 class NOP: probability =
0.00675783649744
>>
   and for yxiB, yxiA 
<<>>> q, p = LogisticRegression.calculate(model, [309, -271.005880394])
  >>> print("class OP: probability =", p, "class NOP: probability =", q)
  class OP: probability = 0.000321211251817 class NOP: probability =
0.999678788748
>>

  To get some idea of the prediction accuracy of the logistic regression
model, we can apply it to the training data: 
<<>>> for i in range(len(ys)):
          print("True:", ys[i], "Predicted:",
LogisticRegression.classify(model, xs[i]))
  True: 1 Predicted: 1
  True: 1 Predicted: 0
  True: 1 Predicted: 1
  True: 1 Predicted: 1
  True: 1 Predicted: 1
  True: 1 Predicted: 1
  True: 1 Predicted: 1
  True: 1 Predicted: 1
  True: 1 Predicted: 1
  True: 1 Predicted: 1
  True: 0 Predicted: 0
  True: 0 Predicted: 0
  True: 0 Predicted: 0
  True: 0 Predicted: 0
  True: 0 Predicted: 0
  True: 0 Predicted: 0
  True: 0 Predicted: 0
>>
   showing that the prediction is correct for all but one of the gene
pairs. A more reliable estimate of the prediction accuracy can be found
from a leave-one-out analysis, in which the model is recalculated from
the training data after removing the gene to be predicted: 
<<>>> for i in range(len(ys)):
          model = LogisticRegression.train(xs[:i]+xs[i+1:],
ys[:i]+ys[i+1:])
          print("True:", ys[i], "Predicted:",
LogisticRegression.classify(model, xs[i]))
  True: 1 Predicted: 1
  True: 1 Predicted: 0
  True: 1 Predicted: 1
  True: 1 Predicted: 1
  True: 1 Predicted: 1
  True: 1 Predicted: 1
  True: 1 Predicted: 1
  True: 1 Predicted: 1
  True: 1 Predicted: 1
  True: 1 Predicted: 1
  True: 0 Predicted: 0
  True: 0 Predicted: 0
  True: 0 Predicted: 0
  True: 0 Predicted: 0
  True: 0 Predicted: 1
  True: 0 Predicted: 0
  True: 0 Predicted: 0
>>
   The leave-one-out analysis shows that the prediction of the logistic
regression model is incorrect for only two of the gene pairs, which
corresponds to a prediction accuracy of 88%.


16.1.4 Logistic Regression, Linear Discriminant Analysis, and Support
=====================================================================
Vector Machines
===============

  The logistic regression model is similar to linear discriminant
analysis. In linear discriminant analysis, the class probabilities also
follow equations (??) and (??). However, instead of estimating the
coefficients beta directly, we first fit a normal distribution to the
predictor variables x. The coefficients beta are then calculated from
the means and covariances of the normal distribution. If the
distribution of x is indeed normal, then we expect linear discriminant
analysis to perform better than the logistic regression model. The
logistic regression model, on the other hand, is more robust to
deviations from normality.
  Another similar approach is a support vector machine with a linear
kernel. Such an SVM also uses a linear combination of the predictors,
but estimates the coefficients beta from the predictor variables x near
the boundary region between the classes. If the logistic regression
model (equations (??) and (??)) is a good description for x away from
the boundary region, we expect the logistic regression model to perform
better than an SVM with a linear kernel, as it relies on more data. If
not, an SVM with a linear kernel may perform better.
  Trevor Hastie, Robert Tibshirani, and Jerome Friedman: The Elements of
Statistical Learning. Data Mining, Inference, and Prediction. Springer
Series in Statistics, 2001. Chapter 4.4.


16.2 k-Nearest Neighbors
*=*=*=*=*=*=*=*=*=*=*=*=




16.2.1 Background and purpose
=============================

  The k-nearest neighbors method is a supervised learning approach that
does not need to fit a model to the data. Instead, data points are
classified based on the categories of the k nearest neighbors in the
training data set.
  In Biopython, the k-nearest neighbors method is available in Bio.kNN.
To illustrate the use of the k-nearest neighbor method in Biopython, we
will use the same operon data set as in section ??.


16.2.2 Initializing a k-nearest neighbors model
===============================================

  Using the data in Table ??, we create and initialize a k-nearest
neighbors model as follows:
<<>>> from Bio import kNN
  >>> k = 3
  >>> model = kNN.train(xs, ys, k)
>>

  where xs and ys are the same as in Section ??. Here, k is the number
of neighbors k that will be considered for the classification. For
classification into two classes, choosing an odd number for k lets you
avoid tied votes. The function name train is a bit of a misnomer, since
no model training is done: this function simply stores xs, ys, and k in
model.


16.2.3 Using a k-nearest neighbors model for classification
===========================================================

  To classify new data using the k-nearest neighbors model, we use the
classify function. This function takes a data point (x_1,x_2) and finds
the k-nearest neighbors in the training data set xs. The data point
(x_1, x_2) is then classified based on which category (ys) occurs most
among the k neighbors.
  For the example of the gene pairs yxcE, yxcD and yxiB, yxiA, we find: 
<<>>> x = [6, -173.143442352]
  >>> print("yxcE, yxcD:", kNN.classify(model, x))
  yxcE, yxcD: 1
  >>> x = [309, -271.005880394]
  >>> print("yxiB, yxiA:", kNN.classify(model, x))
  yxiB, yxiA: 0
>>
   In agreement with the logistic regression model, yxcE, yxcD are
classified as belonging to the same operon (class OP), while yxiB, yxiA
are predicted to belong to different operons.
  The classify function lets us specify both a distance function and a
weight function as optional arguments. The distance function affects
which k neighbors are chosen as the nearest neighbors, as these are
defined as the neighbors with the smallest distance to the query point
(x, y). By default, the Euclidean distance is used. Instead, we could
for example use the city-block (Manhattan) distance:
<<>>> def cityblock(x1, x2):
  ...    assert len(x1)==2
  ...    assert len(x2)==2
  ...    distance = abs(x1[0]-x2[0]) + abs(x1[1]-x2[1])
  ...    return distance
  ...
  >>> x = [6, -173.143442352]
  >>> print("yxcE, yxcD:", kNN.classify(model, x, distance_fn =
cityblock))
  yxcE, yxcD: 1
>>

  The weight function can be used for weighted voting. For example, we
may want to give closer neighbors a higher weight than neighbors that
are further away:
<<>>> def weight(x1, x2):
  ...    assert len(x1)==2
  ...    assert len(x2)==2
  ...    return exp(-abs(x1[0]-x2[0]) - abs(x1[1]-x2[1]))
  ...
  >>> x = [6, -173.143442352]
  >>> print("yxcE, yxcD:", kNN.classify(model, x, weight_fn = weight))
  yxcE, yxcD: 1
>>
   By default, all neighbors are given an equal weight.
  To find out how confident we can be in these predictions, we can call
the calculate function, which will calculate the total weight assigned
to the classes OP and NOP. For the default weighting scheme, this
reduces to the number of neighbors in each category. For yxcE, yxcD, we
find 
<<>>> x = [6, -173.143442352]
  >>> weight = kNN.calculate(model, x)
  >>> print("class OP: weight =", weight[0], "class NOP: weight =",
weight[1])
  class OP: weight = 0.0 class NOP: weight = 3.0
>>
   which means that all three neighbors of x1, x2 are in the NOP class.
As another example, for yesK, yesL we find
<<>>> x = [117, -267.14]
  >>> weight = kNN.calculate(model, x)
  >>> print("class OP: weight =", weight[0], "class NOP: weight =",
weight[1])
  class OP: weight = 2.0 class NOP: weight = 1.0
>>
   which means that two neighbors are operon pairs and one neighbor is a
non-operon pair.
  To get some idea of the prediction accuracy of the k-nearest neighbors
approach, we can apply it to the training data: 
<<>>> for i in range(len(ys)):
          print("True:", ys[i], "Predicted:", kNN.classify(model,
xs[i]))
  True: 1 Predicted: 1
  True: 1 Predicted: 0
  True: 1 Predicted: 1
  True: 1 Predicted: 1
  True: 1 Predicted: 1
  True: 1 Predicted: 1
  True: 1 Predicted: 1
  True: 1 Predicted: 1
  True: 1 Predicted: 1
  True: 1 Predicted: 0
  True: 0 Predicted: 0
  True: 0 Predicted: 0
  True: 0 Predicted: 0
  True: 0 Predicted: 0
  True: 0 Predicted: 0
  True: 0 Predicted: 0
  True: 0 Predicted: 0
>>
   showing that the prediction is correct for all but two of the gene
pairs. A more reliable estimate of the prediction accuracy can be found
from a leave-one-out analysis, in which the model is recalculated from
the training data after removing the gene to be predicted: 
<<>>> k = 3
  >>> for i in range(len(ys)):
          model = kNN.train(xs[:i]+xs[i+1:], ys[:i]+ys[i+1:], k)
          print("True:", ys[i], "Predicted:", kNN.classify(model,
xs[i]))
  True: 1 Predicted: 1
  True: 1 Predicted: 0
  True: 1 Predicted: 1
  True: 1 Predicted: 1
  True: 1 Predicted: 1
  True: 1 Predicted: 1
  True: 1 Predicted: 1
  True: 1 Predicted: 1
  True: 1 Predicted: 1
  True: 1 Predicted: 0
  True: 0 Predicted: 0
  True: 0 Predicted: 0
  True: 0 Predicted: 1
  True: 0 Predicted: 0
  True: 0 Predicted: 0
  True: 0 Predicted: 0
  True: 0 Predicted: 1
>>
   The leave-one-out analysis shows that k-nearest neighbors model is
correct for 13 out of 17 gene pairs, which corresponds to a prediction
accuracy of 76%.


16.3 Naive Bayes
*=*=*=*=*=*=*=*=


  This section will describe the Bio.NaiveBayes module.


16.4 Maximum Entropy
*=*=*=*=*=*=*=*=*=*=


  This section will describe the Bio.MaxEntropy module.


16.5 Markov Models
*=*=*=*=*=*=*=*=*=


  This section will describe the Bio.MarkovModel and/or
Bio.HMM.MarkovModel modules.

Chapter 17  Graphics including GenomeDiagram
********************************************

  The Bio.Graphics module depends on the third party Python library
ReportLab (1). Although focused on producing PDF files, ReportLab can
also create encapsulated postscript (EPS) and (SVG) files. In addition
to these vector based images, provided certain further dependencies such
as the Python Imaging Library (PIL) (2) are installed, ReportLab can
also output bitmap images (including JPEG, PNG, GIF, BMP and PICT
formats).


17.1 GenomeDiagram
*=*=*=*=*=*=*=*=*=



17.1.1 Introduction
===================

  The Bio.Graphics.GenomeDiagram module was added to Biopython 1.50,
having previously been available as a separate Python module dependent
on Biopython. GenomeDiagram is described in the Bioinformatics journal
publication by Pritchard et al. (2006) [], which includes some examples
images. There is a PDF copy of the old manual here,
http://biopython.org/DIST/docs/GenomeDiagram/userguide.pdf which has
some more examples. 
  As the name might suggest, GenomeDiagram was designed for drawing
whole genomes, in particular prokaryotic genomes, either as linear
diagrams (optionally broken up into fragments to fit better) or as
circular wheel diagrams. Have a look at Figure 2 in Toth et al. (2006)
[] for a good example. It proved also well suited to drawing quite
detailed figures for smaller genomes such as phage, plasmids or
mitochrondia, for example see Figures 1 and 2 in Van der Auwera et al.
(2009) [] (shown with additional manual editing).
  This module is easiest to use if you have your genome loaded as a
SeqRecord object containing lots of SeqFeature objects - for example as
loaded from a GenBank file (see Chapters ?? and ??).


17.1.2 Diagrams, tracks, feature-sets and features
==================================================

  GenomeDiagram uses a nested set of objects. At the top level, you have
a diagram object representing a sequence (or sequence region) along the
horizontal axis (or circle). A diagram can contain one or more tracks,
shown stacked vertically (or radially on circular diagrams). These will
typically all have the same length and represent the same sequence
region. You might use one track to show the gene locations, another to
show regulatory regions, and a third track to show the GC percentage. 
  The most commonly used type of track will contain features, bundled
together in feature-sets. You might choose to use one feature-set for
all your CDS features, and another for tRNA features. This isn't
required - they can all go in the same feature-set, but it makes it
easier to update the properties of just selected features (e.g. make all
the tRNA features red).
  There are two main ways to build up a complete diagram. Firstly, the
top down approach where you create a diagram object, and then using its
methods add track(s), and use the track methods to add feature-set(s),
and use their methods to add the features. Secondly, you can create the
individual objects separately (in whatever order suits your code), and
then combine them.


17.1.3 A top down example
=========================

  We're going to draw a whole genome from a SeqRecord object read in
from a GenBank file (see Chapter ??). This example uses the pPCP1
plasmid from Yersinia pestis biovar Microtus, the file is included with
the Biopython unit tests under the GenBank folder, or online
NC_005816.gb (3) from our website.
<<from reportlab.lib import colors
  from reportlab.lib.units import cm
  from Bio.Graphics import GenomeDiagram
  from Bio import SeqIO

  record = SeqIO.read("NC_005816.gb", "genbank")
>>

  We're using a top down approach, so after loading in our sequence we
next create an empty diagram, then add an (empty) track, and to that add
an (empty) feature set:
<<gd_diagram = GenomeDiagram.Diagram("Yersinia pestis biovar Microtus
plasmid pPCP1")
  gd_track_for_features = gd_diagram.new_track(1, name="Annotated
Features")
  gd_feature_set = gd_track_for_features.new_set()
>>

  Now the fun part - we take each gene SeqFeature object in our
SeqRecord, and use it to generate a feature on the diagram. We're going
to color them blue, alternating between a dark blue and a light blue. 
<<for feature in record.features:
      if feature.type != "gene":
          # Exclude this feature
          continue
      if len(gd_feature_set) % 2 == 0:
          color = colors.blue
      else:
          color = colors.lightblue
      gd_feature_set.add_feature(feature, color=color, label=True)
>>

  Now we come to actually making the output file. This happens in two
steps, first we call the draw method, which creates all the shapes using
ReportLab objects. Then we call the write method which renders these to
the requested file format. Note you can output in multiple file formats:
<<gd_diagram.draw(
      format="linear",
      orientation="landscape",
      pagesize="A4",
      fragments=4,
      start=0,
      end=len(record),
  )
  gd_diagram.write("plasmid_linear.pdf", "PDF")
  gd_diagram.write("plasmid_linear.eps", "EPS")
  gd_diagram.write("plasmid_linear.svg", "SVG")
>>

  Also, provided you have the dependencies installed, you can also do
bitmaps, for example:
<<gd_diagram.write("plasmid_linear.png", "PNG")
>>

   *images/plasmid_linear.png*
    Notice that the fragments argument which we set to four controls how
many pieces the genome gets broken up into.
  If you want to do a circular figure, then try this:
<<gd_diagram.draw(
      format="circular",
      circular=True,
      pagesize=(20 * cm, 20 * cm),
      start=0,
      end=len(record),
      circle_core=0.7,
  )
  gd_diagram.write("plasmid_circular.pdf", "PDF")
>>

   *images/plasmid_circular.png*
    These figures are not very exciting, but we've only just got
started.


17.1.4 A bottom up example
==========================
   Now let's produce exactly the same figures, but using the bottom up
approach. This means we create the different objects directly (and this
can be done in almost any order) and then combine them.
<<from reportlab.lib import colors
  from reportlab.lib.units import cm
  from Bio.Graphics import GenomeDiagram
  from Bio import SeqIO

  record = SeqIO.read("NC_005816.gb", "genbank")

  # Create the feature set and its feature objects,
  gd_feature_set = GenomeDiagram.FeatureSet()
  for feature in record.features:
      if feature.type != "gene":
          # Exclude this feature
          continue
      if len(gd_feature_set) % 2 == 0:
          color = colors.blue
      else:
          color = colors.lightblue
      gd_feature_set.add_feature(feature, color=color, label=True)
  # (this for loop is the same as in the previous example)

  # Create a track, and a diagram
  gd_track_for_features = GenomeDiagram.Track(name="Annotated Features")
  gd_diagram = GenomeDiagram.Diagram("Yersinia pestis biovar Microtus
plasmid pPCP1")

  # Now have to glue the bits together...
  gd_track_for_features.add_set(gd_feature_set)
  gd_diagram.add_track(gd_track_for_features, 1)
>>

  You can now call the draw and write methods as before to produce a
linear or circular diagram, using the code at the end of the top-down
example above. The figures should be identical.


17.1.5 Features without a SeqFeature
====================================

  In the above example we used a SeqRecord's SeqFeature objects to build
our diagram (see also Section ??). Sometimes you won't have SeqFeature
objects, but just the coordinates for a feature you want to draw. You
have to create minimal SeqFeature object, but this is easy:
<<from Bio.SeqFeature import SeqFeature, FeatureLocation

  my_seq_feature = SeqFeature(FeatureLocation(50, 100), strand=+1)
>>

  For strand, use +1 for the forward strand, -1 for the reverse strand,
and None for both. Here is a short self contained example:
<<from Bio.SeqFeature import SeqFeature, FeatureLocation
  from Bio.Graphics import GenomeDiagram
  from reportlab.lib.units import cm

  gdd = GenomeDiagram.Diagram("Test Diagram")
  gdt_features = gdd.new_track(1, greytrack=False)
  gds_features = gdt_features.new_set()

  # Add three features to show the strand options,
  feature = SeqFeature(FeatureLocation(25, 125), strand=+1)
  gds_features.add_feature(feature, name="Forward", label=True)
  feature = SeqFeature(FeatureLocation(150, 250), strand=None)
  gds_features.add_feature(feature, name="Strandless", label=True)
  feature = SeqFeature(FeatureLocation(275, 375), strand=-1)
  gds_features.add_feature(feature, name="Reverse", label=True)

  gdd.draw(format="linear", pagesize=(15 * cm, 4 * cm), fragments=1,
start=0, end=400)
  gdd.write("GD_labels_default.pdf", "pdf")
>>

   The top part of the image in the next subsection shows the output  
(in the default feature color, pale green).
  Notice that we have used the name argument here to specify the caption
text for these features. This is discussed in more detail next.


17.1.6 Feature captions
=======================

  Recall we used the following (where feature was a SeqFeature object)
to add a feature to the diagram:
<<gd_feature_set.add_feature(feature, color=color, label=True)
>>

  In the example above the SeqFeature annotation was used to pick a
sensible caption for the features. By default the following possible
entries under the SeqFeature object's qualifiers dictionary are used:
gene, label, name, locus_tag, and product. More simply, you can specify
a name directly:
<<gd_feature_set.add_feature(feature, color=color, label=True, name="My
Gene")
>>

  In addition to the caption text for each feature's label, you can also
choose the font, position (this defaults to the start of the sigil, you
can also choose the middle or at the end) and orientation (for linear
diagrams only, where this defaults to rotated by 45 degrees):
<<# Large font, parallel with the track
  gd_feature_set.add_feature(
      feature, label=True, color="green", label_size=25, label_angle=0
  )

  # Very small font, perpendicular to the track (towards it)
  gd_feature_set.add_feature(
      feature,
      label=True,
      color="purple",
      label_position="end",
      label_size=4,
      label_angle=90,
  )

  # Small font, perpendicular to the track (away from it)
  gd_feature_set.add_feature(
      feature,
      label=True,
      color="blue",
      label_position="middle",
      label_size=6,
      label_angle=-90,
  )
>>

  Combining each of these three fragments with the complete example in
the previous section should give something like  this:
  *images/GD_sigil_labels.png* 

  We've not shown it here, but you can also set label_color to control
the label's color (used in Section ??).
  You'll notice the default font is quite small - this makes sense
because you will usually be drawing many (small) features on a page, not
just a few large ones as shown here.


17.1.7 Feature sigils
=====================

  The examples above have all just used the default sigil for the
feature, a plain box, which was all that was available in the last
publicly released standalone version of GenomeDiagram. Arrow sigils were
included when GenomeDiagram was added to Biopython 1.50:
<<# Default uses a BOX sigil
  gd_feature_set.add_feature(feature)

  # You can make this explicit:
  gd_feature_set.add_feature(feature, sigil="BOX")

  # Or opt for an arrow:
  gd_feature_set.add_feature(feature, sigil="ARROW")
>>

  Biopython 1.61 added three more sigils,
<<# Box with corners cut off (making it an octagon)
  gd_feature_set.add_feature(feature, sigil="OCTO")

  # Box with jagged edges (useful for showing breaks in contains)
  gd_feature_set.add_feature(feature, sigil="JAGGY")

  # Arrow which spans the axis with strand used only for direction
  gd_feature_set.add_feature(feature, sigil="BIGARROW")
>>

  These are shown  below.   Most sigils fit into a bounding box (as
given by the default BOX sigil), either above or below the axis for the
forward or reverse strand, or straddling it (double the height) for
strand-less features. The BIGARROW sigil is different, always straddling
the axis with the direction taken from the feature's stand.
   *images/GD_sigils.png*  


17.1.8 Arrow sigils
===================

  We introduced the arrow sigils in the previous section. There are two
additional options to adjust the shapes of the arrows, firstly the
thickness of the arrow shaft, given as a proportion of the height of the
bounding box:
<<# Full height shafts, giving pointed boxes:
  gd_feature_set.add_feature(feature, sigil="ARROW", color="brown",
arrowshaft_height=1.0)
  # Or, thin shafts:
  gd_feature_set.add_feature(feature, sigil="ARROW", color="teal",
arrowshaft_height=0.2)
  # Or, very thin shafts:
  gd_feature_set.add_feature(
      feature, sigil="ARROW", color="darkgreen", arrowshaft_height=0.1
  )
>>

   The results are shown below:
  *images/GD_sigil_arrow_shafts.png*

  Secondly, the length of the arrow head - given as a proportion of the
height of the bounding box (defaulting to 0.5, or 50%):
<<# Short arrow heads:
  gd_feature_set.add_feature(feature, sigil="ARROW", color="blue",
arrowhead_length=0.25)
  # Or, longer arrow heads:
  gd_feature_set.add_feature(feature, sigil="ARROW", color="orange",
arrowhead_length=1)
  # Or, very very long arrow heads (i.e. all head, no shaft, so
triangles):
  gd_feature_set.add_feature(feature, sigil="ARROW", color="red",
arrowhead_length=10000)
>>

   The results are shown below:
  *images/GD_sigil_arrow_heads.png*

  Biopython 1.61 adds a new BIGARROW sigil which always stradles the
axis, pointing left for the reverse strand or right otherwise:
<<# A large arrow straddling the axis:
  gd_feature_set.add_feature(feature, sigil="BIGARROW")
>>

  All the shaft and arrow head options shown above for the ARROW sigil
can be used for the BIGARROW sigil too.


17.1.9 A nice example
=====================

  Now let's return to the pPCP1 plasmid from Yersinia pestis biovar
Microtus, and the top down approach used in Section ??, but take
advantage of the sigil options we've now discussed. This time we'll use
arrows for the genes, and overlay them with strand-less features (as
plain boxes) showing the position of some restriction digest sites.
<<from reportlab.lib import colors
  from reportlab.lib.units import cm
  from Bio.Graphics import GenomeDiagram
  from Bio import SeqIO
  from Bio.SeqFeature import SeqFeature, FeatureLocation

  record = SeqIO.read("NC_005816.gb", "genbank")

  gd_diagram = GenomeDiagram.Diagram(record.id)
  gd_track_for_features = gd_diagram.new_track(1, name="Annotated
Features")
  gd_feature_set = gd_track_for_features.new_set()

  for feature in record.features:
      if feature.type != "gene":
          # Exclude this feature
          continue
      if len(gd_feature_set) % 2 == 0:
          color = colors.blue
      else:
          color = colors.lightblue
      gd_feature_set.add_feature(
          feature, sigil="ARROW", color=color, label=True,
label_size=14, label_angle=0
      )

  # I want to include some strandless features, so for an example
  # will use EcoRI recognition sites etc.
  for site, name, color in [
      ("GAATTC", "EcoRI", colors.green),
      ("CCCGGG", "SmaI", colors.orange),
      ("AAGCTT", "HindIII", colors.red),
      ("GGATCC", "BamHI", colors.purple),
  ]:
      index = 0
      while True:
          index = record.seq.find(site, start=index)
          if index == -1:
              break
          feature = SeqFeature(FeatureLocation(index, index +
len(site)))
          gd_feature_set.add_feature(
              feature,
              color=color,
              name=name,
              label=True,
              label_size=10,
              label_color=color,
          )
          index += len(site)

  gd_diagram.draw(format="linear", pagesize="A4", fragments=4, start=0,
end=len(record))
  gd_diagram.write("plasmid_linear_nice.pdf", "PDF")
  gd_diagram.write("plasmid_linear_nice.eps", "EPS")
  gd_diagram.write("plasmid_linear_nice.svg", "SVG")

  gd_diagram.draw(
      format="circular",
      circular=True,
      pagesize=(20 * cm, 20 * cm),
      start=0,
      end=len(record),
      circle_core=0.5,
  )
  gd_diagram.write("plasmid_circular_nice.pdf", "PDF")
  gd_diagram.write("plasmid_circular_nice.eps", "EPS")
  gd_diagram.write("plasmid_circular_nice.svg", "SVG")
>>

   And the output:
  *images/plasmid_linear_nice.png*
  *images/plasmid_circular_nice.png*



17.1.10 Multiple tracks
=======================

  All the examples so far have used a single track, but you can have
more than one track -- for example show the genes on one, and repeat
regions on another. In this example we're going to show three phage
genomes side by side to scale, inspired by Figure 6 in Proux et al.
(2002) []. We'll need the GenBank files for the following three phage: 

   - NC_002703 -- Lactococcus phage Tuc2009, complete genome (38347 bp) 
   - AF323668 -- Bacteriophage bIL285, complete genome (35538 bp) 
   - NC_003212 -- Listeria innocua Clip11262, complete genome, of which
   we are focussing only on integrated prophage 5 (similar length). 

  You can download these using Entrez if you like, see Section ?? for
more details. For the third record we've worked out where the phage is
integrated into the genome, and slice the record to extract it (with the
features preserved, see Section ??), and must also reverse complement to
match the orientation of the first two phage (again preserving the
features, see Section ??):
<<from Bio import SeqIO

  A_rec = SeqIO.read("NC_002703.gbk", "gb")
  B_rec = SeqIO.read("AF323668.gbk", "gb")
  C_rec = SeqIO.read("NC_003212.gbk",
"gb")[2587879:2625807].reverse_complement(name=True)
>>

  The figure we are imitating used different colors for different gene
functions. One way to do this is to edit the GenBank file to record
color preferences for each feature - something Sanger's Artemis
editor (4) does, and which GenomeDiagram should understand. Here
however, we'll just hard code three lists of colors.
  Note that the annotation in the GenBank files doesn't exactly match
that shown in Proux et al., they have drawn some unannotated genes.
<<from reportlab.lib.colors import (
      red,
      grey,
      orange,
      green,
      brown,
      blue,
      lightblue,
      purple,
  )

  A_colors = (
      [red] * 5
      + [grey] * 7
      + [orange] * 2
      + [grey] * 2
      + [orange]
      + [grey] * 11
      + [green] * 4
      + [grey]
      + [green] * 2
      + [grey, green]
      + [brown] * 5
      + [blue] * 4
      + [lightblue] * 5
      + [grey, lightblue]
      + [purple] * 2
      + [grey]
  )
  B_colors = (
      [red] * 6
      + [grey] * 8
      + [orange] * 2
      + [grey]
      + [orange]
      + [grey] * 21
      + [green] * 5
      + [grey]
      + [brown] * 4
      + [blue] * 3
      + [lightblue] * 3
      + [grey] * 5
      + [purple] * 2
  )
  C_colors = (
      [grey] * 30
      + [green] * 5
      + [brown] * 4
      + [blue] * 2
      + [grey, blue]
      + [lightblue] * 2
      + [grey] * 5
  )
>>

  Now to draw them -- this time we add three tracks to the diagram, and
also notice they are given different start/end values to reflect their
different lengths (this requires Biopython 1.59 or later).
<<from Bio.Graphics import GenomeDiagram

  name = "Proux Fig 6"
  gd_diagram = GenomeDiagram.Diagram(name)
  max_len = 0
  for record, gene_colors in zip([A_rec, B_rec, C_rec], [A_colors,
B_colors, C_colors]):
      max_len = max(max_len, len(record))
      gd_track_for_features = gd_diagram.new_track(
          1, name=record.name, greytrack=True, start=0, end=len(record)
      )
      gd_feature_set = gd_track_for_features.new_set()

      i = 0
      for feature in record.features:
          if feature.type != "gene":
              # Exclude this feature
              continue
          gd_feature_set.add_feature(
              feature,
              sigil="ARROW",
              color=gene_colors[i],
              label=True,
              name=str(i + 1),
              label_position="start",
              label_size=6,
              label_angle=0,
          )
          i += 1

  gd_diagram.draw(format="linear", pagesize="A4", fragments=1, start=0,
end=max_len)
  gd_diagram.write(name + ".pdf", "PDF")
  gd_diagram.write(name + ".eps", "EPS")
  gd_diagram.write(name + ".svg", "SVG")
>>

   The result:
  *images/three_track_simple.png*
    I did wonder why in the original manuscript there were no red or
orange genes marked in the bottom phage. Another important point is here
the phage are shown with different lengths - this is because they are
all drawn to the same scale (they are different lengths).
  The key difference from the published figure is they have color-coded
links between similar proteins -- which is what we will do in the next
section.


17.1.11 Cross-Links between tracks
==================================

  Biopython 1.59 added the ability to draw cross links between tracks -
both simple linear diagrams as we will show here, but also linear
diagrams split into fragments and circular diagrams.
  Continuing the example from the previous section inspired by Figure 6
from Proux et al. 2002 [], we would need a list of cross links between
pairs of genes, along with a score or color to use. Realistically you
might extract this from a BLAST file computationally, but here I have
manually typed them in.
  My naming convention continues to refer to the three phage as A, B and
C. Here are the links we want to show between A and B, given as a list
of tuples (percentage similarity score, gene in A, gene in B).
<<# Tuc2009 (NC_002703) vs bIL285 (AF323668)
  A_vs_B = [
      (99, "Tuc2009_01", "int"),
      (33, "Tuc2009_03", "orf4"),
      (94, "Tuc2009_05", "orf6"),
      (100, "Tuc2009_06", "orf7"),
      (97, "Tuc2009_07", "orf8"),
      (98, "Tuc2009_08", "orf9"),
      (98, "Tuc2009_09", "orf10"),
      (100, "Tuc2009_10", "orf12"),
      (100, "Tuc2009_11", "orf13"),
      (94, "Tuc2009_12", "orf14"),
      (87, "Tuc2009_13", "orf15"),
      (94, "Tuc2009_14", "orf16"),
      (94, "Tuc2009_15", "orf17"),
      (88, "Tuc2009_17", "rusA"),
      (91, "Tuc2009_18", "orf20"),
      (93, "Tuc2009_19", "orf22"),
      (71, "Tuc2009_20", "orf23"),
      (51, "Tuc2009_22", "orf27"),
      (97, "Tuc2009_23", "orf28"),
      (88, "Tuc2009_24", "orf29"),
      (26, "Tuc2009_26", "orf38"),
      (19, "Tuc2009_46", "orf52"),
      (77, "Tuc2009_48", "orf54"),
      (91, "Tuc2009_49", "orf55"),
      (95, "Tuc2009_52", "orf60"),
  ]
>>

  Likewise for B and C:
<<# bIL285 (AF323668) vs Listeria innocua prophage 5 (in NC_003212)
  B_vs_C = [
      (42, "orf39", "lin2581"),
      (31, "orf40", "lin2580"),
      (49, "orf41", "lin2579"),  # terL
      (54, "orf42", "lin2578"),  # portal
      (55, "orf43", "lin2577"),  # protease
      (33, "orf44", "lin2576"),  # mhp
      (51, "orf46", "lin2575"),
      (33, "orf47", "lin2574"),
      (40, "orf48", "lin2573"),
      (25, "orf49", "lin2572"),
      (50, "orf50", "lin2571"),
      (48, "orf51", "lin2570"),
      (24, "orf52", "lin2568"),
      (30, "orf53", "lin2567"),
      (28, "orf54", "lin2566"),
  ]
>>

  For the first and last phage these identifiers are locus tags, for the
middle phage there are no locus tags so I've used gene names instead.
The following little helper function lets us lookup a feature using
either a locus tag or gene name:
<<def get_feature(features, id, tags=["locus_tag", "gene"]):
      """Search list of SeqFeature objects for an identifier under the
given tags."""
      for f in features:
          for key in tags:
              # tag may not be present in this feature
              for x in f.qualifiers.get(key, []):
                  if x == id:
                      return f
      raise KeyError(id)
>>

  We can now turn those list of identifier pairs into SeqFeature pairs,
and thus find their location co-ordinates. We can now add all that code
and the following snippet to the previous example (just before the
gd_diagram.draw(...) line -- see the finished example script
Proux_et_al_2002_Figure_6.py (5) included in the Doc/examples folder of
the Biopython source code) to add cross links to the figure:
<<from Bio.Graphics.GenomeDiagram import CrossLink
  from reportlab.lib import colors

  # Note it might have been clearer to assign the track numbers
explicitly...
  for rec_X, tn_X, rec_Y, tn_Y, X_vs_Y in [
      (A_rec, 3, B_rec, 2, A_vs_B),
      (B_rec, 2, C_rec, 1, B_vs_C),
  ]:
      track_X = gd_diagram.tracks[tn_X]
      track_Y = gd_diagram.tracks[tn_Y]
      for score, id_X, id_Y in X_vs_Y:
          feature_X = get_feature(rec_X.features, id_X)
          feature_Y = get_feature(rec_Y.features, id_Y)
          color = colors.linearlyInterpolatedColor(
              colors.white, colors.firebrick, 0, 100, score
          )
          link_xy = CrossLink(
              (track_X, feature_X.location.start,
feature_X.location.end),
              (track_Y, feature_Y.location.start,
feature_Y.location.end),
              color,
              colors.lightgrey,
          )
          gd_diagram.cross_track_links.append(link_xy)
>>

  There are several important pieces to this code. First the
GenomeDiagram object has a cross_track_links attribute which is just a
list of CrossLink objects. Each CrossLink object takes two sets of
track-specific co-ordinates (here given as tuples, you can alternatively
use a GenomeDiagram.Feature object instead). You can optionally supply a
colour, border color, and say if this link should be drawn flipped
(useful for showing inversions).
  You can also see how we turn the BLAST percentage identity score into
a colour, interpolating between white (0%) and a dark red (100%). In
this example we don't have any problems with overlapping cross-links.
One way to tackle that is to use transparency in ReportLab, by using
colors with their alpha channel set. However, this kind of shaded color
scheme combined with overlap transparency would be difficult to
interpret.  The result:
  *images/three_track_cl.png*

  There is still a lot more that can be done within Biopython to help
improve this figure. First of all, the cross links in this case are
between proteins which are drawn in a strand specific manor. It can help
to add a background region (a feature using the `BOX' sigil) on the
feature track to extend the cross link. Also, we could reduce the
vertical height of the feature tracks to allocate more to the links
instead -- one way to do that is to allocate space for empty tracks.
Furthermore, in cases like this where there are no large gene overlaps,
we can use the axis-straddling BIGARROW sigil, which allows us to
further reduce the vertical space needed for the track. These
improvements are demonstrated in the example script
Proux_et_al_2002_Figure_6.py (6) included in the Doc/examples folder of
the Biopython source code.  The result:
  *images/three_track_cl2a.png*

  Beyond that, finishing touches you might want to do manually in a
vector image editor include fine tuning the placement of gene labels,
and adding other custom annotation such as highlighting particular
regions.
  Although not really necessary in this example since none of the
cross-links overlap, using a transparent color in ReportLab is a very
useful technique for superimposing multiple links. However, in this case
a shaded color scheme should be avoided.


17.1.12 Further options
=======================

  You can control the tick marks to show the scale -- after all every
graph should show its units, and the number of the grey-track labels.
  Also, we have only used the FeatureSet so far. GenomeDiagram also has
a GraphSet which can be used for show line graphs, bar charts and heat
plots (e.g. to show plots of GC% on a track parallel to the features).
  These options are not covered here yet, so for now we refer you to the
User Guide (PDF) (7) included with the standalone version of
GenomeDiagram (but please read the next section first), and the
docstrings.


17.1.13 Converting old code
===========================

  If you have old code written using the standalone version of
GenomeDiagram, and you want to switch it over to using the new version
included with Biopython then you will have to make a few changes - most
importantly to your import statements.
  Also, the older version of GenomeDiagram used only the UK spellings of
color and center (colour and centre). You will need to change to the
American spellings, although for several years the Biopython version of
GenomeDiagram supported both.
  For example, if you used to have: 
<<from GenomeDiagram import GDFeatureSet, GDDiagram

  gdd = GDDiagram("An example")
  ...
>>
   you could just switch the import statements like this: 
<<from Bio.Graphics.GenomeDiagram import FeatureSet as GDFeatureSet,
Diagram as GDDiagram

  gdd = GDDiagram("An example")
  ...
>>
   and hopefully that should be enough. In the long term you might want
to switch to the new names, but you would have to change more of your
code: 
<<from Bio.Graphics.GenomeDiagram import FeatureSet, Diagram

  gdd = Diagram("An example")
  ...
>>
   or: 
<<from Bio.Graphics import GenomeDiagram

  gdd = GenomeDiagram.Diagram("An example")
  ...
>>

  If you run into difficulties, please ask on the Biopython mailing list
for advice. One catch is that we have not included the old module
GenomeDiagram.GDUtilities yet. This included a number of GC% related
functions, which will probably be merged under Bio.SeqUtils later on. 


17.2 Chromosomes
*=*=*=*=*=*=*=*=


  The Bio.Graphics.BasicChromosome module allows drawing of chromosomes.
There is an example in Jupe et al. (2012) [] (open access) using colors
to highlight different gene families.


17.2.1 Simple Chromosomes
=========================
   Here is a very simple example - for which we'll use Arabidopsis
thaliana.

  You can skip this bit, but first I downloaded the five sequenced
chromosomes as five individual FASTA files from the NCBI's FTP site
ftp://ftp.ncbi.nlm.nih.gov/genomes/archive/old_refseq/Arabidopsis_thalia
na/ and then parsed them with Bio.SeqIO to find out their lengths. You
could use the GenBank files for this (and the next example uses those
for plotting features), but if all you want is the length it is faster
to use the FASTA files for the whole chromosomes:
<<from Bio import SeqIO

  entries = [
      ("Chr I", "CHR_I/NC_003070.fna"),
      ("Chr II", "CHR_II/NC_003071.fna"),
      ("Chr III", "CHR_III/NC_003074.fna"),
      ("Chr IV", "CHR_IV/NC_003075.fna"),
      ("Chr V", "CHR_V/NC_003076.fna"),
  ]
  for (name, filename) in entries:
      record = SeqIO.read(filename, "fasta")
      print(name, len(record))
>>

  This gave the lengths of the five chromosomes, which we'll now use in
the following short demonstration of the BasicChromosome module:
<<from reportlab.lib.units import cm
  from Bio.Graphics import BasicChromosome

  entries = [
      ("Chr I", 30432563),
      ("Chr II", 19705359),
      ("Chr III", 23470805),
      ("Chr IV", 18585042),
      ("Chr V", 26992728),
  ]

  max_len = 30432563  # Could compute this from the entries dict
  telomere_length = 1000000  # For illustration

  chr_diagram = BasicChromosome.Organism()
  chr_diagram.page_size = (29.7 * cm, 21 * cm)  # A4 landscape

  for name, length in entries:
      cur_chromosome = BasicChromosome.Chromosome(name)
      # Set the scale to the MAXIMUM length plus the two telomeres in
bp,
      # want the same scale used on all five chromosomes so they can be
      # compared to each other
      cur_chromosome.scale_num = max_len + 2 * telomere_length

      # Add an opening telomere
      start = BasicChromosome.TelomereSegment()
      start.scale = telomere_length
      cur_chromosome.add(start)

      # Add a body - using bp as the scale length here.
      body = BasicChromosome.ChromosomeSegment()
      body.scale = length
      cur_chromosome.add(body)

      # Add a closing telomere
      end = BasicChromosome.TelomereSegment(inverted=True)
      end.scale = telomere_length
      cur_chromosome.add(end)

      # This chromosome is done
      chr_diagram.add(cur_chromosome)

  chr_diagram.draw("simple_chrom.pdf", "Arabidopsis thaliana")
>>

  This should create a very simple PDF file, shown  here:
  *images/simple_chrom.png*
    This example is deliberately short and sweet. The next example shows
the location of features of interest.


17.2.2 Annotated Chromosomes
============================

  Continuing from the previous example, let's also show the tRNA genes.
We'll get their locations by parsing the GenBank files for the five
Arabidopsis thaliana chromosomes. You'll need to download these files
from the NCBI FTP site
ftp://ftp.ncbi.nlm.nih.gov/genomes/archive/old_refseq/Arabidopsis_thalia
na/, and preserve the subdirectory names or edit the paths below:
<<from reportlab.lib.units import cm
  from Bio import SeqIO
  from Bio.Graphics import BasicChromosome

  entries = [
      ("Chr I", "CHR_I/NC_003070.gbk"),
      ("Chr II", "CHR_II/NC_003071.gbk"),
      ("Chr III", "CHR_III/NC_003074.gbk"),
      ("Chr IV", "CHR_IV/NC_003075.gbk"),
      ("Chr V", "CHR_V/NC_003076.gbk"),
  ]

  max_len = 30432563  # Could compute this from the entries dict
  telomere_length = 1000000  # For illustration

  chr_diagram = BasicChromosome.Organism()
  chr_diagram.page_size = (29.7 * cm, 21 * cm)  # A4 landscape

  for index, (name, filename) in enumerate(entries):
      record = SeqIO.read(filename, "genbank")
      length = len(record)
      features = [f for f in record.features if f.type == "tRNA"]
      # Record an Artemis style integer color in the feature's
qualifiers,
      # 1 = Black, 2 = Red, 3 = Green, 4 = blue, 5 =cyan, 6 = purple
      for f in features:
          f.qualifiers["color"] = [index + 2]

      cur_chromosome = BasicChromosome.Chromosome(name)
      # Set the scale to the MAXIMUM length plus the two telomeres in
bp,
      # want the same scale used on all five chromosomes so they can be
      # compared to each other
      cur_chromosome.scale_num = max_len + 2 * telomere_length

      # Add an opening telomere
      start = BasicChromosome.TelomereSegment()
      start.scale = telomere_length
      cur_chromosome.add(start)

      # Add a body - again using bp as the scale length here.
      body = BasicChromosome.AnnotatedChromosomeSegment(length,
features)
      body.scale = length
      cur_chromosome.add(body)

      # Add a closing telomere
      end = BasicChromosome.TelomereSegment(inverted=True)
      end.scale = telomere_length
      cur_chromosome.add(end)

      # This chromosome is done
      chr_diagram.add(cur_chromosome)

  chr_diagram.draw("tRNA_chrom.pdf", "Arabidopsis thaliana")
>>

  It might warn you about the labels being too close together - have a
look at the forward strand (right hand side) of Chr I, but it should
create a colorful PDF file, shown  here:
  *images/tRNA_chrom.png*

-----------------------------------


 (1) https://www.reportlab.com/
 (2) http://www.pythonware.com/products/pil/
 (3) https://raw.githubusercontent.com/biopython/biopython/master/Tests/
   GenBank/NC_005816.gb
 (4) https://www.sanger.ac.uk/science/tools/artemis
 (5) https://github.com/biopython/biopython/blob/master/Doc/examples/Pro
   ux_et_al_2002_Figure_6.py
 (6) https://github.com/biopython/biopython/blob/master/Doc/examples/Pro
   ux_et_al_2002_Figure_6.py
 (7) http://biopython.org/DIST/docs/GenomeDiagram/userguide.pdf


Chapter 18  KEGG
****************

  KEGG (https://www.kegg.jp/) is a database resource for understanding
high-level functions and utilities of the biological system, such as the
cell, the organism and the ecosystem, from molecular-level information,
especially large-scale molecular datasets generated by genome sequencing
and other high-throughput experimental technologies.
  Please note that the KEGG parser implementation in Biopython is
incomplete. While the KEGG website indicates many flat file formats,
only parsers and writers for compound, enzyme, and map are currently
implemented. However, a generic parser is implemented to handle the
other formats.


18.1 Parsing KEGG records
*=*=*=*=*=*=*=*=*=*=*=*=*

   Parsing a KEGG record is as simple as using any other file format
parser in Biopython.  (Before running the following codes, please open
http://rest.kegg.jp/get/ec:5.4.2.2 with your web browser and save it as
ec_5.4.2.2.txt.)
<<>>> from Bio.KEGG import Enzyme
  >>> records = Enzyme.parse(open("ec_5.4.2.2.txt"))
  >>> record = list(records)[0]
  >>> record.classname
  ['Isomerases;', 'Intramolecular transferases;', 'Phosphotransferases
(phosphomutases)']
  >>> record.entry
  '5.4.2.2'
>>

  Alternatively, if the input KEGG file has exactly one entry, you can
use read:
<<>>> from Bio.KEGG import Enzyme
  >>> record = Enzyme.read(open("ec_5.4.2.2.txt"))
  >>> record.classname
  ['Isomerases;', 'Intramolecular transferases;', 'Phosphotransferases
(phosphomutases)']
  >>> record.entry
  '5.4.2.2'
>>

  The following section will shows how to download the above enzyme
using the KEGG api as well as how to use the generic parser with data
that does not have a custom parser implemented.


18.2 Querying the KEGG API
*=*=*=*=*=*=*=*=*=*=*=*=*=


  Biopython has full support for the querying of the KEGG api. Querying
all KEGG endpoints are supported; all methods documented by KEGG
(https://www.kegg.jp/kegg/rest/keggapi.html) are supported. The
interface has some validation of queries which follow rules defined on
the KEGG site. However, invalid queries which return a 400 or 404 must
be handled by the user.
  First, here is how to extend the above example by downloading the
relevant enzyme and passing it through the Enzyme parser.
<<>>> from Bio.KEGG import REST
  >>> from Bio.KEGG import Enzyme
  >>> request = REST.kegg_get("ec:5.4.2.2")
  >>> open("ec_5.4.2.2.txt", "w").write(request.read())
  >>> records = Enzyme.parse(open("ec_5.4.2.2.txt"))
  >>> record = list(records)[0]
  >>> record.classname
  ['Isomerases;', 'Intramolecular transferases;', 'Phosphotransferases
(phosphomutases)']
  >>> record.entry
  '5.4.2.2'
>>

  Now, here's a more realistic example which shows a combination of
querying the KEGG API. This will demonstrate how to extract a unique set
of all human pathway gene symbols which relate to DNA repair. The steps
that need to be taken to do so are as follows. First, we need to get a
list of all human pathways. Secondly, we need to filter those for ones
which relate to "repair". Lastly, we need to get a list of all the gene
symbols in all repair pathways.
<<from Bio.KEGG import REST

  human_pathways = REST.kegg_list("pathway", "hsa").read()

  # Filter all human pathways for repair pathways
  repair_pathways = []
  for line in human_pathways.rstrip().split("\n"):
      entry, description = line.split("\t")
      if "repair" in description:
          repair_pathways.append(entry)

  # Get the genes for pathways and add them to a list
  repair_genes = []
  for pathway in repair_pathways:
      pathway_file = REST.kegg_get(pathway).read()  # query and read
each pathway

      # iterate through each KEGG pathway file, keeping track of which
section
      # of the file we're in, only read the gene in each pathway
      current_section = None
      for line in pathway_file.rstrip().split("\n"):
          section = line[:12].strip()  # section names are within 12
columns
          if not section == "":
              current_section = section

          if current_section == "GENE":
              gene_identifiers, gene_description = line[12:].split("; ")
              gene_id, gene_symbol = gene_identifiers.split()

              if not gene_symbol in repair_genes:
                  repair_genes.append(gene_symbol)

  print(
      "There are %d repair pathways and %d repair genes. The genes are:"
      % (len(repair_pathways), len(repair_genes))
  )
  print(", ".join(repair_genes))
>>

  The KEGG API wrapper is compatible with all endpoints. Usage is
essentially replacing all slashes in the url with commas and using that
list as arguments to the corresponding method in the KEGG module. Here
are a few examples from the api documentation
(https://www.kegg.jp/kegg/docs/keggapi.html).
<</list/hsa:10458+ece:Z5100          -> REST.kegg_list(["hsa:10458",
"ece:Z5100"])
  /find/compound/300-310/mol_weight  -> REST.kegg_find("compound",
"300-310", "mol_weight")
  /get/hsa:10458+ece:Z5100/aaseq     -> REST.kegg_get(["hsa:10458",
"ece:Z5100"], "aaseq")
>>


Chapter 19  Bio.phenotype: analyse phenotypic data
**************************************************

  This chapter gives an overview of the functionalities of the
Bio.phenotype package included in Biopython. The scope of this package
is the analysis of phenotypic data, which means parsing and analysing
growth measurements of cell cultures. In its current state the package
is focused on the analysis of high-throughput phenotypic experiments
produced by the Phenotype Microarray technology (1), but future
developments may include other platforms and formats.


19.1 Phenotype Microarrays
*=*=*=*=*=*=*=*=*=*=*=*=*=


  The Phenotype Microarray (2) is a technology that measures the
metabolism of bacterial and eukaryotic cells on roughly 2000 chemicals,
divided in twenty 96-well plates. The technology measures the reduction
of a tetrazolium dye by NADH, whose production by the cell is used as a
proxy for cell metabolism; color development due to the reduction of
this dye is typically measured once every 15 minutes. When cells are
grown in a media that sustains cell metabolism, the recorded phenotypic
data resembles a sigmoid growth curve, from which a series of growth
parameters can be retrieved.


19.1.1 Parsing Phenotype Microarray data
========================================

  The Bio.phenotype package can parse two different formats of Phenotype
Microarray data: the CSV (3) (comma separated values) files produced by
the machine's proprietary software and JSON (4) files produced by
analysis software, like opm (5) or DuctApe (6). The parser will return
one or a generator of PlateRecord objects, depending on whether the read
or parse method is being used. You can test the parse function by using
the Plates.csv (7) file provided with the Biopython source code.
<<>>> from Bio import phenotype
  >>> for record in phenotype.parse("Plates.csv", "pm-csv"):
  ...     print("%s %i" % (record.id, len(record)))
  ...
  PM01 96
  PM01 96
  PM09 96
  PM09 96
>>

  The parser returns a series of PlateRecord objects, each one
containing a series of WellRecord objects (holding each well's
experimental data) arranged in 8 rows and 12 columns; each row is
indicated by a uppercase character from A to H, while columns are
indicated by a two digit number, from 01 to 12. There are several ways
to access WellRecord objects from a PlateRecord objects:


 Well identifier  If you know the well identifier (row + column
   identifiers) you can access the desired well directly. 
   <<    >>> record["A02"]
         >>


 Well plate coordinates  The same well can be retrieved by using the row
   and columns numbers (0-based index).
   <<>>> from Bio import phenotype
     >>> record = list(phenotype.parse("Plates.csv", "pm-csv"))[-1]
     >>> print(record[0, 1].id)
     A02
   >>


 Row or column coordinates  A series of WellRecord objects contiguous to
   each other in the plate can be retrieved in bulk by using the python
   list slicing syntax on PlateRecord objects; rows and columns are
   numbered with a 0-based index.
   <<>>> print(record[0])
     Plate ID: PM09
     Well: 12
     Rows: 1
     Columns: 12
     PlateRecord('WellRecord['A01'], WellRecord['A02'],
   WellRecord['A03'], ..., WellRecord['A12']')
     >>> print(record[:, 0])
     Plate ID: PM09
     Well: 8
     Rows: 8
     Columns: 1
     PlateRecord('WellRecord['A01'], WellRecord['B01'],
   WellRecord['C01'], ..., WellRecord['H01']')
     >>> print(record[:3, :3])
     Plate ID: PM09
     Well: 9
     Rows: 3
     Columns: 3
     PlateRecord('WellRecord['A01'], WellRecord['A02'],
   WellRecord['A03'], ..., WellRecord['C03']')
   >>




19.1.2 Manipulating Phenotype Microarray data
=============================================



19.1.2.1 Accessing raw data
---------------------------
   The raw data extracted from the PM files is comprised of a series of
tuples for each well, containing the time (in hours) and the
colorimetric measure (in arbitrary units). Usually the instrument
collects data every fifteen minutes, but that can vary between
experiments. The raw data can be accessed by iterating on a WellRecord
object; in the example below only the first ten time points are shown.
<<>>> from Bio import phenotype
  >>> record = list(phenotype.parse("Plates.csv", "pm-csv"))[-1]
  >>> well = record["A02"]
>>

<<>>> for time, signal in well:
  ...    print(time, signal)
  ...
  (0.0, 12.0)
  (0.25, 18.0)
  (0.5, 27.0)
  (0.75, 35.0)
  (1.0, 37.0)
  (1.25, 41.0)
  (1.5, 44.0)
  (1.75, 44.0)
  (2.0, 44.0)
  (2.25, 44.0)
  [...]
>>

  This method, while providing a way to access the raw data, doesn't
allow a direct comparison between different WellRecord objects, which
may have measurements at different time points.


19.1.2.2 Accessing interpolated data
------------------------------------
   To make it easier to compare different experiments and in general to
allow a more intuitive handling of the phenotypic data, the module
allows to define a custom slicing of the time points that are present in
the WellRecord object. Colorimetric data for time points that have not
been directly measured are derived through a linear interpolation of the
available data, otherwise a NaN is returned. This method only works in
the time interval where actual data is available. Time intervals can be
defined with the same syntax as list indexing; the default time interval
is therefore one hour.
<<>>> well[:10]  
  [12.0, 37.0, 44.0, 44.0, 44.0, 44.0, 44.0, 44.0, 44.0, 44.0]
>>

  Different time intervals can be used, for instance five minutes: 
<<>>> well[63:64:0.083]
  [12.0, 37.0, 44.0, 44.0, 44.0, 44.0, 44.0, 44.0, 44.0, 44.0]
  >>> well[9.55]
  44.0
  >>> well[63.33:73.33]
  [113.31999999999999,
   117.0,
   120.31999999999999,
   128.0,
   129.63999999999999,
   132.95999999999998,
   136.95999999999998,
   140.0,
   142.0,
   nan]
>>



19.1.2.3 Control well subtraction
---------------------------------
   Many Phenotype Microarray plates contain a control well (usually
A01), that is a well where the media shouldn't support any growth; the
low signal produced by this well can be subtracted from the other wells.
The PlateRecord objects have a dedicated function for that, which
returns another PlateRecord object with the corrected data.
<<>>> corrected = record.subtract_control(control="A01")
  >>> record["A01"][63]
  336.0
  >>> corrected["A01"][63]
  0.0
>>



19.1.2.4 Parameters extraction
------------------------------
   Those wells where metabolic activity is observed show a sigmoid
behavior for the colorimetric data. To allow an easier way to compare
different experiments a sigmoid curve can be fitted onto the data, so
that a series of summary parameters can be extracted and used for
comparisons. The parameters that can be extracted from the curve are:


   - Minimum (min) and maximum (max) signal;

   - Average height (average_height);

   - Area under the curve (area);

   - Curve plateau point (plateau);

   - Curve slope during exponential metabolic activity (slope);

   - Curve lag time (lag). 

  All the parameters (except min, max and average_height) require the
scipy library (8) to be installed.
  The fit function uses three sigmoid functions:


 Gompertz  Ae^-e^(mu_me/A(lambda - t) + 1) + y0

 Logistic  A/1+e^(4mu_m/A(lambda - t) + 2) + y_0

 Richards  A(1 + ve^1 + v + e^mu_m/A(1 + v)(1 + 1/v)(lambda - t))^-1/v +
   y0

  Where: 

   - A corresponds to the plateau

   - mu_m corresponds to the slope

   - lambda corresponds to the lag

  These functions have been derived from this publication (9). The fit
method by default tries first to fit the gompertz function: if it fails
it will then try to fit the logistic and then the richards function. The
user can also specify one of the three functions to be applied.
<<>>> from Bio import phenotype
  >>> record = list(phenotype.parse("Plates.csv", "pm-csv"))[-1]
  >>> well = record["A02"]
  >>> well.fit()
  >>> print("Function fitted: %s" % well.model)
  Function fitted: gompertz
  >>> for param in ["area", "average_height", "lag", "max", "min",
  ...               "plateau", "slope"]:
  ...     print("%s\t%.2f" % (param, getattr(well, param)))
  ...
  area    4414.38
  average_height  61.58
  lag     48.60
  max     143.00
  min     12.00
  plateau 120.02
  slope   4.99
>>



19.1.3 Writing Phenotype Microarray data
========================================
   PlateRecord objects can be written to file in the form of JSON (10)
files, a format compatible with other software packages such as opm (11)
or DuctApe (12). 
<<>>> phenotype.write(record, "out.json", "pm-json")
  1
>>

-----------------------------------


 (1) https://en.wikipedia.org/wiki/Phenotype_microarray
 (2) https://en.wikipedia.org/wiki/Phenotype_microarray
 (3) https://en.wikipedia.org/wiki/Comma-separated_values
 (4) https://en.wikipedia.org/wiki/JSON
 (5) https://www.dsmz.de/research/microorganisms/projects/analysis-of-om
   nilog-phenotype-microarray-data.html
 (6) https://combogenomics.github.io/DuctApe/
 (7) https://github.com/biopython/biopython/blob/master/Doc/examples/Pla
   tes.csv
 (8) https://www.scipy.org/
 (9) https://www.ncbi.nlm.nih.gov/pubmed/16348228
 (10) https://en.wikipedia.org/wiki/JSON
 (11)
   https://www.dsmz.de/research/microorganisms/projects/analysis-of-omni
   log-phenotype-microarray-data.html
 (12) https://combogenomics.github.io/DuctApe/


Chapter 20  Cookbook -- Cool things to do with it
*************************************************

  Biopython now has two collections of "cookbook" examples -- this
chapter (which has been included in this tutorial for many years and has
gradually grown), and http://biopython.org/wiki/Category:Cookbook which
is a user contributed collection on our wiki.
  We're trying to encourage Biopython users to contribute their own
examples to the wiki. In addition to helping the community, one direct
benefit of sharing an example like this is that you could also get some
feedback on the code from other Biopython users and developers - which
could help you improve all your Python code.
  In the long term, we may end up moving all of the examples in this
chapter to the wiki, or elsewhere within the tutorial.


20.1 Working with sequence files
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=


  This section shows some more examples of sequence input/output, using
the Bio.SeqIO module described in Chapter ??.


20.1.1 Filtering a sequence file
================================

  Often you'll have a large file with many sequences in it (e.g. FASTA
file or genes, or a FASTQ or SFF file of reads), a separate shorter list
of the IDs for a subset of sequences of interest, and want to make a new
sequence file for this subset.
  Let's say the list of IDs is in a simple text file, as the first word
on each line. This could be a tabular file where the first column is the
ID. Try something like this:
<<from Bio import SeqIO

  input_file = "big_file.sff"
  id_file = "short_list.txt"
  output_file = "short_list.sff"

  with open(id_file) as id_handle:
      wanted = set(line.rstrip("\n").split(None, 1)[0] for line in
id_handle)
  print("Found %i unique identifiers in %s" % (len(wanted), id_file))

  records = (r for r in SeqIO.parse(input_file, "sff") if r.id in
wanted)
  count = SeqIO.write(records, output_file, "sff")
  print("Saved %i records from %s to %s" % (count, input_file,
output_file))
  if count < len(wanted):
      print("Warning %i IDs not found in %s" % (len(wanted) - count,
input_file))
>>

  Note that we use a Python set rather than a list, this makes testing
membership faster.
  As discussed in Section ??, for a large FASTA or FASTQ file for speed
you would be better off not using the high-level SeqIO interface, but
working directly with strings. This next example shows how to do this
with FASTQ files -- it is more complicated:
<<from Bio.SeqIO.QualityIO import FastqGeneralIterator

  input_file = "big_file.fastq"
  id_file = "short_list.txt"
  output_file = "short_list.fastq"

  with open(id_file) as id_handle:
      # Taking first word on each line as an identifer
      wanted = set(line.rstrip("\n").split(None, 1)[0] for line in
id_handle)
  print("Found %i unique identifiers in %s" % (len(wanted), id_file))

  with open(input_file) as in_handle:
      with open(output_file, "w") as out_handle:
          for title, seq, qual in FastqGeneralIterator(in_handle):
              # The ID is the first word in the title line (after the @
sign):
              if title.split(None, 1)[0] in wanted:
                  # This produces a standard 4-line FASTQ entry:
                  out_handle.write("@%s\n%s\n+\n%s\n" % (title, seq,
qual))
                  count += 1
  print("Saved %i records from %s to %s" % (count, input_file,
output_file))
  if count < len(wanted):
      print("Warning %i IDs not found in %s" % (len(wanted) - count,
input_file))
>>



20.1.2 Producing randomised genomes
===================================

  Let's suppose you are looking at genome sequence, hunting for some
sequence feature -- maybe extreme local GC% bias, or possible
restriction digest sites. Once you've got your Python code working on
the real genome it may be sensible to try running the same search on
randomised versions of the same genome for statistical analysis (after
all, any "features" you've found could just be there just by chance).
  For this discussion, we'll use the GenBank file for the pPCP1 plasmid
from Yersinia pestis biovar Microtus. The file is included with the
Biopython unit tests under the GenBank folder, or you can get it from
our website, NC_005816.gb (1). This file contains one and only one
record, so we can read it in as a SeqRecord using the Bio.SeqIO.read()
function:
<<>>> from Bio import SeqIO
  >>> original_rec = SeqIO.read("NC_005816.gb", "genbank")
>>

  So, how can we generate a shuffled versions of the original sequence?
I would use the built in Python random module for this, in particular
the function random.shuffle -- but this works on a Python list. Our
sequence is a Seq object, so in order to shuffle it we need to turn it
into a list:
<<>>> import random
  >>> nuc_list = list(original_rec.seq)
  >>> random.shuffle(nuc_list)  # acts in situ!
>>

  Now, in order to use Bio.SeqIO to output the shuffled sequence, we
need to construct a new SeqRecord with a new Seq object using this
shuffled list. In order to do this, we need to turn the list of
nucleotides (single letter strings) into a long string -- the standard
Python way to do this is with the string object's join method.
<<>>> from Bio.Seq import Seq
  >>> from Bio.SeqRecord import SeqRecord
  >>> shuffled_rec = SeqRecord(Seq("".join(nuc_list)),
  ...                          id="Shuffled", description="Based on %s"
% original_rec.id)
  ...
>>

  Let's put all these pieces together to make a complete Python script
which generates a single FASTA file containing 30 randomly shuffled
versions of the original sequence.
  This first version just uses a big for loop and writes out the records
one by one (using the SeqRecord's format method described in
Section ??):
<<import random
  from Bio.Seq import Seq
  from Bio.SeqRecord import SeqRecord
  from Bio import SeqIO

  original_rec = SeqIO.read("NC_005816.gb", "genbank")

  with open("shuffled.fasta", "w") as output_handle:
      for i in range(30):
          nuc_list = list(original_rec.seq)
          random.shuffle(nuc_list)
          shuffled_rec = SeqRecord(
              Seq("".join(nuc_list)),
              id="Shuffled%i" % (i + 1),
              description="Based on %s" % original_rec.id,
          )
          output_handle.write(shuffled_rec.format("fasta"))
>>

  Personally I prefer the following version using a function to shuffle
the record and a generator expression instead of the for loop:
<<import random
  from Bio.Seq import Seq
  from Bio.SeqRecord import SeqRecord
  from Bio import SeqIO


  def make_shuffle_record(record, new_id):
      nuc_list = list(record.seq)
      random.shuffle(nuc_list)
      return SeqRecord(
          Seq("".join(nuc_list)), id=new_id, description="Based on %s" %
original_rec.id,
      )


  original_rec = SeqIO.read("NC_005816.gb", "genbank")
  shuffled_recs = (
      make_shuffle_record(original_rec, "Shuffled%i" % (i + 1)) for i in
range(30)
  )

  SeqIO.write(shuffled_recs, "shuffled.fasta", "fasta")
>>



20.1.3 Translating a FASTA file of CDS entries
==============================================
    Suppose you've got an input file of CDS entries for some organism,
and you want to generate a new FASTA file containing their protein
sequences. i.e. Take each nucleotide sequence from the original file,
and translate it. Back in Section ?? we saw how to use the Seq object's
translate method, and the optional cds argument which enables correct
translation of alternative start codons.
  We can combine this with Bio.SeqIO as shown in the reverse complement
example in Section ??. The key point is that for each nucleotide
SeqRecord, we need to create a protein SeqRecord - and take care of
naming it.
  You can write you own function to do this, choosing suitable protein
identifiers for your sequences, and the appropriate genetic code. In
this example we just use the default table and add a prefix to the
identifier:
<<from Bio.SeqRecord import SeqRecord


  def make_protein_record(nuc_record):
      """Returns a new SeqRecord with the translated sequence (default
table)."""
      return SeqRecord(
          seq=nuc_record.seq.translate(cds=True),
          id="trans_" + nuc_record.id,
          description="translation of CDS, using default table",
      )
>>

  We can then use this function to turn the input nucleotide records
into protein records ready for output. An elegant way and memory
efficient way to do this is with a generator expression:
<<from Bio import SeqIO

  proteins = (
      make_protein_record(nuc_rec)
      for nuc_rec in SeqIO.parse("coding_sequences.fasta", "fasta")
  )
  SeqIO.write(proteins, "translations.fasta", "fasta")
>>

  This should work on any FASTA file of complete coding sequences. If
you are working on partial coding sequences, you may prefer to use
nuc_record.seq.translate(to_stop=True) in the example above, as this
wouldn't check for a valid start codon etc.


20.1.4 Making the sequences in a FASTA file upper case
======================================================

  Often you'll get data from collaborators as FASTA files, and sometimes
the sequences can be in a mixture of upper and lower case. In some cases
this is deliberate (e.g. lower case for poor quality regions), but
usually it is not important. You may want to edit the file to make
everything consistent (e.g. all upper case), and you can do this easily
using the upper() method of the SeqRecord object (added in Biopython
1.55):
<<from Bio import SeqIO

  records = (rec.upper() for rec in SeqIO.parse("mixed.fas", "fasta"))
  count = SeqIO.write(records, "upper.fas", "fasta")
  print("Converted %i records to upper case" % count)
>>

  How does this work? The first line is just importing the Bio.SeqIO
module. The second line is the interesting bit -- this is a Python
generator expression which gives an upper case version of each record
parsed from the input file (mixed.fas). In the third line we give this
generator expression to the Bio.SeqIO.write() function and it saves the
new upper cases records to our output file (upper.fas).
  The reason we use a generator expression (rather than a list or list
comprehension) is this means only one record is kept in memory at a
time. This can be really important if you are dealing with large files
with millions of entries.


20.1.5 Sorting a sequence file
==============================

  Suppose you wanted to sort a sequence file by length (e.g. a set of
contigs from an assembly), and you are working with a file format like
FASTA or FASTQ which Bio.SeqIO can read, write (and index).
  If the file is small enough, you can load it all into memory at once
as a list of SeqRecord objects, sort the list, and save it:
<<from Bio import SeqIO

  records = list(SeqIO.parse("ls_orchid.fasta", "fasta"))
  records.sort(key=lambda r: len(r))
  SeqIO.write(records, "sorted_orchids.fasta", "fasta")
>>

  The only clever bit is specifying a comparison method for how to sort
the records (here we sort them by length). If you wanted the longest
records first, you could flip the comparison or use the reverse
argument:
<<from Bio import SeqIO

  records = list(SeqIO.parse("ls_orchid.fasta", "fasta"))
  records.sort(key=lambda r: -len(r))
  SeqIO.write(records, "sorted_orchids.fasta", "fasta")
>>

  Now that's pretty straight forward - but what happens if you have a
very large file and you can't load it all into memory like this? For
example, you might have some next-generation sequencing reads to sort by
length. This can be solved using the Bio.SeqIO.index() function.
<<from Bio import SeqIO

  # Get the lengths and ids, and sort on length
  len_and_ids = sorted(
      (len(rec), rec.id) for rec in SeqIO.parse("ls_orchid.fasta",
"fasta")
  )
  ids = reversed([id for (length, id) in len_and_ids])
  del len_and_ids  # free this memory
  record_index = SeqIO.index("ls_orchid.fasta", "fasta")
  records = (record_index[id] for id in ids)
  SeqIO.write(records, "sorted.fasta", "fasta")
>>

  First we scan through the file once using Bio.SeqIO.parse(), recording
the record identifiers and their lengths in a list of tuples. We then
sort this list to get them in length order, and discard the lengths.
Using this sorted list of identifiers Bio.SeqIO.index() allows us to
retrieve the records one by one, and we pass them to Bio.SeqIO.write()
for output.
  These examples all use Bio.SeqIO to parse the records into SeqRecord
objects which are output using Bio.SeqIO.write(). What if you want to
sort a file format which Bio.SeqIO.write() doesn't support, like the
plain text SwissProt format? Here is an alternative solution using the
get_raw() method added to Bio.SeqIO.index() in Biopython 1.54 (see
Section ??).
<<from Bio import SeqIO

  # Get the lengths and ids, and sort on length
  len_and_ids = sorted(
      (len(rec), rec.id) for rec in SeqIO.parse("ls_orchid.fasta",
"fasta")
  )
  ids = reversed([id for (length, id) in len_and_ids])
  del len_and_ids  # free this memory

  record_index = SeqIO.index("ls_orchid.fasta", "fasta")
  with open("sorted.fasta", "wb") as out_handle:
      for id in ids:
          out_handle.write(record_index.get_raw(id))
>>

  Note with Python 3 onwards, we have to open the file for writing in
binary mode because the get_raw() method returns bytes objects.
  As a bonus, because it doesn't parse the data into SeqRecord objects a
second time it should be faster. If you only want to use this with FASTA
format, we can speed this up one step further by using the low-level
FASTA parser to get the record identifiers and lengths:
<<from Bio.SeqIO.FastaIO import SimpleFastaParser
  from Bio import SeqIO

  # Get the lengths and ids, and sort on length
  with open("ls_orchid.fasta") as in_handle:
      len_and_ids = sorted(
          (len(seq), title.split(None, 1)[0])
          for title, seq in SimpleFastaParser(in_handle)
      )
  ids = reversed([id for (length, id) in len_and_ids])
  del len_and_ids  # free this memory

  record_index = SeqIO.index("ls_orchid.fasta", "fasta")
  with open("sorted.fasta", "wb") as out_handle:
      for id in ids:
          out_handle.write(record_index.get_raw(id))
>>



20.1.6 Simple quality filtering for FASTQ files
===============================================

  The FASTQ file format was introduced at Sanger and is now widely used
for holding nucleotide sequencing reads together with their quality
scores. FASTQ files (and the related QUAL files) are an excellent
example of per-letter-annotation, because for each nucleotide in the
sequence there is an associated quality score. Any per-letter-annotation
is held in a SeqRecord in the letter_annotations dictionary as a list,
tuple or string (with the same number of elements as the sequence
length).
  One common task is taking a large set of sequencing reads and
filtering them (or cropping them) based on their quality scores. The
following example is very simplistic, but should illustrate the basics
of working with quality data in a SeqRecord object. All we are going to
do here is read in a file of FASTQ data, and filter it to pick out only
those records whose PHRED quality scores are all above some threshold
(here 20).
  For this example we'll use some real data downloaded from the ENA
sequence read archive,
ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR020/SRR020192/SRR020192.fastq.gz
(2MB) which unzips to a 19MB file SRR020192.fastq. This is some Roche
454 GS FLX single end data from virus infected California sea lions (see
https://www.ebi.ac.uk/ena/data/view/SRS004476 for details).
  First, let's count the reads:
<<from Bio import SeqIO

  count = 0
  for rec in SeqIO.parse("SRR020192.fastq", "fastq"):
      count += 1
  print("%i reads" % count)
>>

  Now let's do a simple filtering for a minimum PHRED quality of 20:
<<from Bio import SeqIO

  good_reads = (
      rec
      for rec in SeqIO.parse("SRR020192.fastq", "fastq")
      if min(rec.letter_annotations["phred_quality"]) >= 20
  )
  count = SeqIO.write(good_reads, "good_quality.fastq", "fastq")
  print("Saved %i reads" % count)
>>

  This pulled out only 14580 reads out of the 41892 present. A more
sensible thing to do would be to quality trim the reads, but this is
intended as an example only.
  FASTQ files can contain millions of entries, so it is best to avoid
loading them all into memory at once. This example uses a generator
expression, which means only one SeqRecord is created at a time -
avoiding any memory limitations.
  Note that it would be faster to use the low-level FastqGeneralIterator
parser here (see Section ??), but that does not turn the quality string
into integer scores.


20.1.7 Trimming off primer sequences
====================================

  For this example we're going to pretend that GATGACGGTGT is a 5'
primer sequence we want to look for in some FASTQ formatted read data.
As in the example above, we'll use the SRR020192.fastq file downloaded
from the ENA
(ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR020/SRR020192/SRR020192.fastq.gz)
.
  By using the main Bio.SeqIO interface, the same approach would work
with any other supported file format (e.g. FASTA files). However, for
large FASTQ files it would be faster the low-level FastqGeneralIterator
parser here (see the earlier example, and Section ??).
  This code uses Bio.SeqIO with a generator expression (to avoid loading
all the sequences into memory at once), and the Seq object's startswith
method to see if the read starts with the primer sequence:
<<from Bio import SeqIO

  primer_reads = (
      rec
      for rec in SeqIO.parse("SRR020192.fastq", "fastq")
      if rec.seq.startswith("GATGACGGTGT")
  )
  count = SeqIO.write(primer_reads, "with_primer.fastq", "fastq")
  print("Saved %i reads" % count)
>>

  That should find 13819 reads from SRR014849.fastq and save them to a
new FASTQ file, with_primer.fastq.
  Now suppose that instead you wanted to make a FASTQ file containing
these reads but with the primer sequence removed? That's just a small
change as we can slice the SeqRecord (see Section ??) to remove the
first eleven letters (the length of our primer):
<<from Bio import SeqIO

  trimmed_primer_reads = (
      rec[11:]
      for rec in SeqIO.parse("SRR020192.fastq", "fastq")
      if rec.seq.startswith("GATGACGGTGT")
  )
  count = SeqIO.write(trimmed_primer_reads, "with_primer_trimmed.fastq",
"fastq")
  print("Saved %i reads" % count)
>>

  Again, that should pull out the 13819 reads from SRR020192.fastq, but
this time strip off the first ten characters, and save them to another
new FASTQ file, with_primer_trimmed.fastq.
  Now, suppose you want to create a new FASTQ file where these reads
have their primer removed, but all the other reads are kept as they
were? If we want to still use a generator expression, it is probably
clearest to define our own trim function:
<<from Bio import SeqIO


  def trim_primer(record, primer):
      if record.seq.startswith(primer):
          return record[len(primer) :]
      else:
          return record


  trimmed_reads = (
      trim_primer(record, "GATGACGGTGT")
      for record in SeqIO.parse("SRR020192.fastq", "fastq")
  )
  count = SeqIO.write(trimmed_reads, "trimmed.fastq", "fastq")
  print("Saved %i reads" % count)
>>

  This takes longer, as this time the output file contains all 41892
reads. Again, we're used a generator expression to avoid any memory
problems. You could alternatively use a generator function rather than a
generator expression.
<<from Bio import SeqIO


  def trim_primers(records, primer):
      """Removes perfect primer sequences at start of reads.

      This is a generator function, the records argument should
      be a list or iterator returning SeqRecord objects.
      """
      len_primer = len(primer)  # cache this for later
      for record in records:
          if record.seq.startswith(primer):
              yield record[len_primer:]
          else:
              yield record


  original_reads = SeqIO.parse("SRR020192.fastq", "fastq")
  trimmed_reads = trim_primers(original_reads, "GATGACGGTGT")
  count = SeqIO.write(trimmed_reads, "trimmed.fastq", "fastq")
  print("Saved %i reads" % count)
>>

  This form is more flexible if you want to do something more
complicated where only some of the records are retained -- as shown in
the next example.


20.1.8 Trimming off adaptor sequences
=====================================

  This is essentially a simple extension to the previous example. We are
going to going to pretend GATGACGGTGT is an adaptor sequence in some
FASTQ formatted read data, again the SRR020192.fastq file from the NCBI
(ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR020/SRR020192/SRR020192.fastq.gz)
.
  This time however, we will look for the sequence anywhere in the
reads, not just at the very beginning:
<<from Bio import SeqIO


  def trim_adaptors(records, adaptor):
      """Trims perfect adaptor sequences.

      This is a generator function, the records argument should
      be a list or iterator returning SeqRecord objects.
      """
      len_adaptor = len(adaptor)  # cache this for later
      for record in records:
          index = record.seq.find(adaptor)
          if index == -1:
              # adaptor not found, so won't trim
              yield record
          else:
              # trim off the adaptor
              yield record[index + len_adaptor :]


  original_reads = SeqIO.parse("SRR020192.fastq", "fastq")
  trimmed_reads = trim_adaptors(original_reads, "GATGACGGTGT")
  count = SeqIO.write(trimmed_reads, "trimmed.fastq", "fastq")
  print("Saved %i reads" % count)
>>

  Because we are using a FASTQ input file in this example, the SeqRecord
objects have per-letter-annotation for the quality scores. By slicing
the SeqRecord object the appropriate scores are used on the trimmed
records, so we can output them as a FASTQ file too.
  Compared to the output of the previous example where we only looked
for a primer/adaptor at the start of each read, you may find some of the
trimmed reads are quite short after trimming (e.g. if the adaptor was
found in the middle rather than near the start). So, let's add a minimum
length requirement as well:
<<from Bio import SeqIO


  def trim_adaptors(records, adaptor, min_len):
      """Trims perfect adaptor sequences, checks read length.

      This is a generator function, the records argument should
      be a list or iterator returning SeqRecord objects.
      """
      len_adaptor = len(adaptor)  # cache this for later
      for record in records:
          len_record = len(record)  # cache this for later
          if len(record) < min_len:
              # Too short to keep
              continue
          index = record.seq.find(adaptor)
          if index == -1:
              # adaptor not found, so won't trim
              yield record
          elif len_record - index - len_adaptor >= min_len:
              # after trimming this will still be long enough
              yield record[index + len_adaptor :]


  original_reads = SeqIO.parse("SRR020192.fastq", "fastq")
  trimmed_reads = trim_adaptors(original_reads, "GATGACGGTGT", 100)
  count = SeqIO.write(trimmed_reads, "trimmed.fastq", "fastq")
  print("Saved %i reads" % count)
>>

  By changing the format names, you could apply this to FASTA files
instead. This code also could be extended to do a fuzzy match instead of
an exact match (maybe using a pairwise alignment, or taking into account
the read quality scores), but that will be much slower.


20.1.9 Converting FASTQ files
=============================

  Back in Section ?? we showed how to use Bio.SeqIO to convert between
two file formats. Here we'll go into a little more detail regarding
FASTQ files which are used in second generation DNA sequencing. Please
refer to Cock et al. (2009) [] for a longer description. FASTQ files
store both the DNA sequence (as a string) and the associated read
qualities.
  PHRED scores (used in most FASTQ files, and also in QUAL files, ACE
files and SFF files) have become a de facto standard for representing
the probability of a sequencing error (here denoted by P_e) at a given
base using a simple base ten log transformation:

                   Q      = - 10 X log   ( P  ) (20.1)
                    PHRED             10    e         

  This means a wrong read (P_e = 1) gets a PHRED quality of 0, while a
very good read like P_e = 0.00001 gets a PHRED quality of 50. While for
raw sequencing data qualities higher than this are rare, with post
processing such as read mapping or assembly, qualities of up to about 90
are possible (indeed, the MAQ tool allows for PHRED scores in the range
0 to 93 inclusive).
  The FASTQ format has the potential to become a de facto standard for
storing the letters and quality scores for a sequencing read in a single
plain text file. The only fly in the ointment is that there are at least
three versions of the FASTQ format which are incompatible and difficult
to distinguish...


   1. The original Sanger FASTQ format uses PHRED qualities encoded with
   an ASCII offset of 33. The NCBI are using this format in their Short
   Read Archive. We call this the fastq (or fastq-sanger) format in
   Bio.SeqIO. 
   2. Solexa (later bought by Illumina) introduced their own version
   using Solexa qualities encoded with an ASCII offset of 64. We call
   this the fastq-solexa format. 
   3. Illumina pipeline 1.3 onwards produces FASTQ files with PHRED
   qualities (which is more consistent), but encoded with an ASCII
   offset of 64. We call this the fastq-illumina format. 

  The Solexa quality scores are defined using a different log
transformation:
                                         (     )       
                                         ( P   )       
                                         (  e  )       
                  Q       = - 10 X log   (---- ) (20.2)
                   Solexa             10 (     )       
                                         (1-P  )       
                                         (   e )       

  Given Solexa/Illumina have now moved to using PHRED scores in version
1.3 of their pipeline, the Solexa quality scores will gradually fall out
of use. If you equate the error estimates (P_e) these two equations
allow conversion between the two scoring systems - and Biopython
includes functions to do this in the Bio.SeqIO.QualityIO module, which
are called if you use Bio.SeqIO to convert an old Solexa/Illumina file
into a standard Sanger FASTQ file:
<<from Bio import SeqIO

  SeqIO.convert("solexa.fastq", "fastq-solexa", "standard.fastq",
"fastq")
>>

  If you want to convert a new Illumina 1.3+ FASTQ file, all that gets
changed is the ASCII offset because although encoded differently the
scores are all PHRED qualities:
<<from Bio import SeqIO

  SeqIO.convert("illumina.fastq", "fastq-illumina", "standard.fastq",
"fastq")
>>

  Note that using Bio.SeqIO.convert() like this is much faster than
combining Bio.SeqIO.parse() and Bio.SeqIO.write() because optimised code
is used for converting between FASTQ variants (and also for FASTQ to
FASTA conversion).
  For good quality reads, PHRED and Solexa scores are approximately
equal, which means since both the fasta-solexa and fastq-illumina
formats use an ASCII offset of 64 the files are almost the same. This
was a deliberate design choice by Illumina, meaning applications
expecting the old fasta-solexa style files will probably be OK using the
newer fastq-illumina files (on good data). Of course, both variants are
very different from the original FASTQ standard as used by Sanger, the
NCBI, and elsewhere (format name fastq or fastq-sanger).
  For more details, see the built in help (also online (2)):
<<>>> from Bio.SeqIO import QualityIO
  >>> help(QualityIO)
  ...
>>



20.1.10 Converting FASTA and QUAL files into FASTQ files
========================================================

  FASTQ files hold both sequences and their quality strings. FASTA files
hold just sequences, while QUAL files hold just the qualities. Therefore
a single FASTQ file can be converted to or from paired FASTA and QUAL
files.
  Going from FASTQ to FASTA is easy:
<<from Bio import SeqIO

  SeqIO.convert("example.fastq", "fastq", "example.fasta", "fasta")
>>

  Going from FASTQ to QUAL is also easy:
<<from Bio import SeqIO

  SeqIO.convert("example.fastq", "fastq", "example.qual", "qual")
>>

  However, the reverse is a little more tricky. You can use
Bio.SeqIO.parse() to iterate over the records in a single file, but in
this case we have two input files. There are several strategies
possible, but assuming that the two files are really paired the most
memory efficient way is to loop over both together. The code is a little
fiddly, so we provide a function called PairedFastaQualIterator in the
Bio.SeqIO.QualityIO module to do this. This takes two handles (the FASTA
file and the QUAL file) and returns a SeqRecord iterator:
<<from Bio.SeqIO.QualityIO import PairedFastaQualIterator

  for record in PairedFastaQualIterator(open("example.fasta"),
open("example.qual")):
      print(record)
>>

  This function will check that the FASTA and QUAL files are consistent
(e.g. the records are in the same order, and have the same sequence
length). You can combine this with the Bio.SeqIO.write() function to
convert a pair of FASTA and QUAL files into a single FASTQ files:
<<from Bio import SeqIO
  from Bio.SeqIO.QualityIO import PairedFastaQualIterator

  with open("example.fasta") as f_handle, open("example.qual") as
q_handle:
      records = PairedFastaQualIterator(f_handle, q_handle)
      count = SeqIO.write(records, "temp.fastq", "fastq")
  print("Converted %i records" % count)
>>



20.1.11 Indexing a FASTQ file
=============================

  FASTQ files are usually very large, with millions of reads in them.
Due to the sheer amount of data, you can't load all the records into
memory at once. This is why the examples above (filtering and trimming)
iterate over the file looking at just one SeqRecord at a time.
  However, sometimes you can't use a big loop or an iterator - you may
need random access to the reads. Here the Bio.SeqIO.index() function may
prove very helpful, as it allows you to access any read in the FASTQ
file by its name (see Section ??).
  Again we'll use the SRR020192.fastq file from the ENA
(ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR020/SRR020192/SRR020192.fastq.gz)
, although this is actually quite a small FASTQ file with less than
50,000 reads:
<<>>> from Bio import SeqIO
  >>> fq_dict = SeqIO.index("SRR020192.fastq", "fastq")
  >>> len(fq_dict)
  41892
  >>> list(fq_dict.keys())[:4]
  ['SRR020192.38240', 'SRR020192.23181', 'SRR020192.40568',
'SRR020192.23186']
  >>> fq_dict["SRR020192.23186"].seq
  Seq('GTCCCAGTATTCGGATTTGTCTGCCAAAACAATGAAATTGACACAGTTTACAAC...CCG')
>>

  When testing this on a FASTQ file with seven million reads, indexing
took about a minute, but record access was almost instant.
  The sister function Bio.SeqIO.index_db() lets you save the index to an
SQLite3 database file for near instantaneous reuse - see Section ?? for
more details.
  The example in Section ?? show how you can use the Bio.SeqIO.index()
function to sort a large FASTA file -- this could also be used on FASTQ
files.


20.1.12 Converting SFF files
============================

  If you work with 454 (Roche) sequence data, you will probably have
access to the raw data as a Standard Flowgram Format (SFF) file. This
contains the sequence reads (called bases) with quality scores and the
original flow information.
  A common task is to convert from SFF to a pair of FASTA and QUAL
files, or to a single FASTQ file. These operations are trivial using the
Bio.SeqIO.convert() function (see Section ??):
<<>>> from Bio import SeqIO
  >>> SeqIO.convert("E3MFGYR02_random_10_reads.sff", "sff",
"reads.fasta", "fasta")
  10
  >>> SeqIO.convert("E3MFGYR02_random_10_reads.sff", "sff",
"reads.qual", "qual")
  10
  >>> SeqIO.convert("E3MFGYR02_random_10_reads.sff", "sff",
"reads.fastq", "fastq")
  10
>>

  Remember the convert function returns the number of records, in this
example just ten. This will give you the untrimmed reads, where the
leading and trailing poor quality sequence or adaptor will be in lower
case. If you want the trimmed reads (using the clipping information
recorded within the SFF file) use this:
<<>>> from Bio import SeqIO
  >>> SeqIO.convert("E3MFGYR02_random_10_reads.sff", "sff-trim",
"trimmed.fasta", "fasta")
  10
  >>> SeqIO.convert("E3MFGYR02_random_10_reads.sff", "sff-trim",
"trimmed.qual", "qual")
  10
  >>> SeqIO.convert("E3MFGYR02_random_10_reads.sff", "sff-trim",
"trimmed.fastq", "fastq")
  10
>>

  If you run Linux, you could ask Roche for a copy of their "off
instrument" tools (often referred to as the Newbler tools). This offers
an alternative way to do SFF to FASTA or QUAL conversion at the command
line (but currently FASTQ output is not supported), e.g.
<<$ sffinfo -seq -notrim E3MFGYR02_random_10_reads.sff > reads.fasta
  $ sffinfo -qual -notrim E3MFGYR02_random_10_reads.sff > reads.qual
  $ sffinfo -seq -trim E3MFGYR02_random_10_reads.sff > trimmed.fasta
  $ sffinfo -qual -trim E3MFGYR02_random_10_reads.sff > trimmed.qual
>>

  The way Biopython uses mixed case sequence strings to represent the
trimming points deliberately mimics what the Roche tools do.
  For more information on the Biopython SFF support, consult the built
in help:
<<>>> from Bio.SeqIO import SffIO
  >>> help(SffIO)
  ...
>>



20.1.13 Identifying open reading frames
=======================================

  A very simplistic first step at identifying possible genes is to look
for open reading frames (ORFs). By this we mean look in all six frames
for long regions without stop codons -- an ORF is just a region of
nucleotides with no in frame stop codons.
  Of course, to find a gene you would also need to worry about locating
a start codon, possible promoters -- and in Eukaryotes there are introns
to worry about too. However, this approach is still useful in viruses
and Prokaryotes.
  To show how you might approach this with Biopython, we'll need a
sequence to search, and as an example we'll again use the bacterial
plasmid -- although this time we'll start with a plain FASTA file with
no pre-marked genes: NC_005816.fna (3). This is a bacterial sequence, so
we'll want to use NCBI codon table 11 (see Section ?? about
translation).
<<>>> from Bio import SeqIO
  >>> record = SeqIO.read("NC_005816.fna", "fasta")
  >>> table = 11
  >>> min_pro_len = 100
>>

  Here is a neat trick using the Seq object's split method to get a list
of all the possible ORF translations in the six reading frames:
<<>>> for strand, nuc in [(+1, record.seq), (-1,
record.seq.reverse_complement())]:
  ...     for frame in range(3):
  ...         length = 3 * ((len(record)-frame) // 3) #Multiple of three
  ...         for pro in
nuc[frame:frame+length].translate(table).split("*"):
  ...             if len(pro) >= min_pro_len:
  ...                 print("%s...%s - length %i, strand %i, frame %i" \
  ...                       % (pro[:30], pro[-3:], len(pro), strand,
frame))
  GCLMKKSSIVATIITILSGSANAASSQLIP...YRF - length 315, strand 1, frame 0
  KSGELRQTPPASSTLHLRLILQRSGVMMEL...NPE - length 285, strand 1, frame 1
  GLNCSFFSICNWKFIDYINRLFQIIYLCKN...YYH - length 176, strand 1, frame 1
  VKKILYIKALFLCTVIKLRRFIFSVNNMKF...DLP - length 165, strand 1, frame 1
  NQIQGVICSPDSGEFMVTFETVMEIKILHK...GVA - length 355, strand 1, frame 2
  RRKEHVSKKRRPQKRPRRRRFFHRLRPPDE...PTR - length 128, strand 1, frame 2
  TGKQNSCQMSAIWQLRQNTATKTRQNRARI...AIK - length 100, strand 1, frame 2
  QGSGYAFPHASILSGIAMSHFYFLVLHAVK...CSD - length 114, strand -1, frame 0
  IYSTSEHTGEQVMRTLDEVIASRSPESQTR...FHV - length 111, strand -1, frame 0
  WGKLQVIGLSMWMVLFSQRFDDWLNEQEDA...ESK - length 125, strand -1, frame 1
  RGIFMSDTMVVNGSGGVPAFLFSGSTLSSY...LLK - length 361, strand -1, frame 1
  WDVKTVTGVLHHPFHLTFSLCPEGATQSGR...VKR - length 111, strand -1, frame 1
  LSHTVTDFTDQMAQVGLCQCVNVFLDEVTG...KAA - length 107, strand -1, frame 2
  RALTGLSAPGIRSQTSCDRLRELRYVPVSL...PLQ - length 119, strand -1, frame 2
>>

  Note that here we are counting the frames from the 5' end (start) of
each strand. It is sometimes easier to always count from the 5' end
(start) of the forward strand.
  You could easily edit the above loop based code to build up a list of
the candidate proteins, or convert this to a list comprehension. Now,
one thing this code doesn't do is keep track of where the proteins are.
  You could tackle this in several ways. For example, the following code
tracks the locations in terms of the protein counting, and converts back
to the parent sequence by multiplying by three, then adjusting for the
frame and strand:
<<from Bio import SeqIO

  record = SeqIO.read("NC_005816.gb", "genbank")
  table = 11
  min_pro_len = 100


  def find_orfs_with_trans(seq, trans_table, min_protein_length):
      answer = []
      seq_len = len(seq)
      for strand, nuc in [(+1, seq), (-1, seq.reverse_complement())]:
          for frame in range(3):
              trans = nuc[frame:].translate(trans_table)
              trans_len = len(trans)
              aa_start = 0
              aa_end = 0
              while aa_start < trans_len:
                  aa_end = trans.find("*", aa_start)
                  if aa_end == -1:
                      aa_end = trans_len
                  if aa_end - aa_start >= min_protein_length:
                      if strand == 1:
                          start = frame + aa_start * 3
                          end = min(seq_len, frame + aa_end * 3 + 3)
                      else:
                          start = seq_len - frame - aa_end * 3 - 3
                          end = seq_len - frame - aa_start * 3
                      answer.append((start, end, strand,
trans[aa_start:aa_end]))
                  aa_start = aa_end + 1
      answer.sort()
      return answer


  orf_list = find_orfs_with_trans(record.seq, table, min_pro_len)
  for start, end, strand, pro in orf_list:
      print(
          "%s...%s - length %i, strand %i, %i:%i"
          % (pro[:30], pro[-3:], len(pro), strand, start, end)
      )
>>

  And the output:
<<NQIQGVICSPDSGEFMVTFETVMEIKILHK...GVA - length 355, strand 1, 41:1109
  WDVKTVTGVLHHPFHLTFSLCPEGATQSGR...VKR - length 111, strand -1, 491:827
  KSGELRQTPPASSTLHLRLILQRSGVMMEL...NPE - length 285, strand 1, 1030:1888
  RALTGLSAPGIRSQTSCDRLRELRYVPVSL...PLQ - length 119, strand -1,
2830:3190
  RRKEHVSKKRRPQKRPRRRRFFHRLRPPDE...PTR - length 128, strand 1, 3470:3857
  GLNCSFFSICNWKFIDYINRLFQIIYLCKN...YYH - length 176, strand 1, 4249:4780
  RGIFMSDTMVVNGSGGVPAFLFSGSTLSSY...LLK - length 361, strand -1,
4814:5900
  VKKILYIKALFLCTVIKLRRFIFSVNNMKF...DLP - length 165, strand 1, 5923:6421
  LSHTVTDFTDQMAQVGLCQCVNVFLDEVTG...KAA - length 107, strand -1,
5974:6298
  GCLMKKSSIVATIITILSGSANAASSQLIP...YRF - length 315, strand 1, 6654:7602
  IYSTSEHTGEQVMRTLDEVIASRSPESQTR...FHV - length 111, strand -1,
7788:8124
  WGKLQVIGLSMWMVLFSQRFDDWLNEQEDA...ESK - length 125, strand -1,
8087:8465
  TGKQNSCQMSAIWQLRQNTATKTRQNRARI...AIK - length 100, strand 1, 8741:9044
  QGSGYAFPHASILSGIAMSHFYFLVLHAVK...CSD - length 114, strand -1,
9264:9609
>>

  If you comment out the sort statement, then the protein sequences will
be shown in the same order as before, so you can check this is doing the
same thing. Here we have sorted them by location to make it easier to
compare to the actual annotation in the GenBank file (as visualised in
Section ??).
  If however all you want to find are the locations of the open reading
frames, then it is a waste of time to translate every possible codon,
including doing the reverse complement to search the reverse strand too.
All you need to do is search for the possible stop codons (and their
reverse complements). Using regular expressions is an obvious approach
here (see the Python module re). These are an extremely powerful (but
rather complex) way of describing search strings, which are supported in
lots of programming languages and also command line tools like grep as
well). You can find whole books about this topic!


20.2 Sequence parsing plus simple plots
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*


  This section shows some more examples of sequence parsing, using the
Bio.SeqIO module described in Chapter ??, plus the Python library
matplotlib's pylab plotting interface (see the matplotlib website for a
tutorial (4)). Note that to follow these examples you will need
matplotlib installed - but without it you can still try the data parsing
bits.


20.2.1 Histogram of sequence lengths
====================================

  There are lots of times when you might want to visualise the
distribution of sequence lengths in a dataset -- for example the range
of contig sizes in a genome assembly project. In this example we'll
reuse our orchid FASTA file ls_orchid.fasta (5) which has only 94
sequences.
  First of all, we will use Bio.SeqIO to parse the FASTA file and
compile a list of all the sequence lengths. You could do this with a for
loop, but I find a list comprehension more pleasing:
<<>>> from Bio import SeqIO
  >>> sizes = [len(rec) for rec in SeqIO.parse("ls_orchid.fasta",
"fasta")]
  >>> len(sizes), min(sizes), max(sizes)
  (94, 572, 789)
  >>> sizes
  [740, 753, 748, 744, 733, 718, 730, 704, 740, 709, 700, 726, ..., 592]
>>

  Now that we have the lengths of all the genes (as a list of integers),
we can use the matplotlib histogram function to display it.
<<from Bio import SeqIO

  sizes = [len(rec) for rec in SeqIO.parse("ls_orchid.fasta", "fasta")]

  import pylab

  pylab.hist(sizes, bins=20)
  pylab.title(
      "%i orchid sequences\nLengths %i to %i" % (len(sizes), min(sizes),
max(sizes))
  )
  pylab.xlabel("Sequence length (bp)")
  pylab.ylabel("Count")
  pylab.show()
>>

   That should pop up a new window containing the following graph:
  *images/hist_plot.png*
    Notice that most of these orchid sequences are about 740 bp long,
and there could be two distinct classes of sequence here with a subset
of shorter sequences.
  Tip: Rather than using pylab.show() to show the plot in a window, you
can also use pylab.savefig(...) to save the figure to a file (e.g. as a
PNG or PDF).


20.2.2 Plot of sequence GC%
===========================

  Another easily calculated quantity of a nucleotide sequence is the
GC%. You might want to look at the GC% of all the genes in a bacterial
genome for example, and investigate any outliers which could have been
recently acquired by horizontal gene transfer. Again, for this example
we'll reuse our orchid FASTA file ls_orchid.fasta (6).
  First of all, we will use Bio.SeqIO to parse the FASTA file and
compile a list of all the GC percentages. Again, you could do this with
a for loop, but I prefer this:
<<from Bio import SeqIO
  from Bio.SeqUtils import GC

  gc_values = sorted(GC(rec.seq) for rec in
SeqIO.parse("ls_orchid.fasta", "fasta"))
>>

  Having read in each sequence and calculated the GC%, we then sorted
them into ascending order. Now we'll take this list of floating point
values and plot them with matplotlib:
<<import pylab

  pylab.plot(gc_values)
  pylab.title(
      "%i orchid sequences\nGC%% %0.1f to %0.1f"
      % (len(gc_values), min(gc_values), max(gc_values))
  )
  pylab.xlabel("Genes")
  pylab.ylabel("GC%")
  pylab.show()
>>

   As in the previous example, that should pop up a new window
containing a graph:
  *images/gc_plot.png*
    If you tried this on the full set of genes from one organism, you'd
probably get a much smoother plot than this.


20.2.3 Nucleotide dot plots
===========================
   A dot plot is a way of visually comparing two nucleotide sequences
for similarity to each other. A sliding window is used to compare short
sub-sequences to each other, often with a mis-match threshold. Here for
simplicity we'll only look for perfect matches (shown in black   in the
plot below). 

  To start off, we'll need two sequences. For the sake of argument,
we'll just take the first two from our orchid FASTA file
ls_orchid.fasta (7):
<<from Bio import SeqIO

  with open("ls_orchid.fasta") as in_handle:
      record_iterator = SeqIO.parse(in_handle, "fasta")
      rec_one = next(record_iterator)
      rec_two = next(record_iterator)
>>

  We're going to show two approaches. Firstly, a simple naive
implementation which compares all the window sized sub-sequences to each
other to compiles a similarity matrix. You could construct a matrix or
array object, but here we just use a list of lists of booleans created
with a nested list comprehension:
<<window = 7
  seq_one = rec_one.seq.upper()
  seq_two = rec_two.seq.upper()
  data = [
      [
          (seq_one[i : i + window] != seq_two[j : j + window])
          for j in range(len(seq_one) - window)
      ]
      for i in range(len(seq_two) - window)
  ]
>>

  Note that we have not checked for reverse complement matches here. Now
we'll use the matplotlib's pylab.imshow() function to display this data,
first requesting the gray color scheme so this is done in black and
white:
<<import pylab

  pylab.gray()
  pylab.imshow(data)
  pylab.xlabel("%s (length %i bp)" % (rec_one.id, len(rec_one)))
  pylab.ylabel("%s (length %i bp)" % (rec_two.id, len(rec_two)))
  pylab.title("Dot plot using window size %i\n(allowing no mis-matches)"
% window)
  pylab.show()
>>

   That should pop up a new window containing a graph like this:
  *images/dot_plot.png*
    As you might have expected, these two sequences are very similar
with a partial line of window sized matches along the diagonal. There
are no off diagonal matches which would be indicative of inversions or
other interesting events.
  The above code works fine on small examples, but there are two
problems applying this to larger sequences, which we will address below.
First off all, this brute force approach to the all against all
comparisons is very slow. Instead, we'll compile dictionaries mapping
the window sized sub-sequences to their locations, and then take the set
intersection to find those sub-sequences found in both sequences. This
uses more memory, but is much faster. Secondly, the pylab.imshow()
function is limited in the size of matrix it can display. As an
alternative, we'll use the pylab.scatter() function.
  We start by creating dictionaries mapping the window-sized
sub-sequences to locations: 
<<window = 7
  dict_one = {}
  dict_two = {}
  for (seq, section_dict) in [
      (rec_one.seq.upper(), dict_one),
      (rec_two.seq.upper(), dict_two),
  ]:
      for i in range(len(seq) - window):
          section = seq[i : i + window]
          try:
              section_dict[section].append(i)
          except KeyError:
              section_dict[section] = [i]
  # Now find any sub-sequences found in both sequences
  matches = set(dict_one).intersection(dict_two)
  print("%i unique matches" % len(matches))
>>
   In order to use the pylab.scatter() we need separate lists for the x
and y co-ordinates: 
<<# Create lists of x and y co-ordinates for scatter plot
  x = []
  y = []
  for section in matches:
      for i in dict_one[section]:
          for j in dict_two[section]:
              x.append(i)
              y.append(j)
>>
   We are now ready to draw the revised dot plot as a scatter plot: 
<<import pylab

  pylab.cla()  # clear any prior graph
  pylab.gray()
  pylab.scatter(x, y)
  pylab.xlim(0, len(rec_one) - window)
  pylab.ylim(0, len(rec_two) - window)
  pylab.xlabel("%s (length %i bp)" % (rec_one.id, len(rec_one)))
  pylab.ylabel("%s (length %i bp)" % (rec_two.id, len(rec_two)))
  pylab.title("Dot plot using window size %i\n(allowing no mis-matches)"
% window)
  pylab.show()
>>
    That should pop up a new window containing a graph like this:
  *images/dot_plot_scatter.png*
    Personally I find this second plot much easier to read! Again note
that we have not checked for reverse complement matches here -- you
could extend this example to do this, and perhaps plot the forward
matches in one color and the reverse matches in another.


20.2.4 Plotting the quality scores of sequencing read data
==========================================================

  If you are working with second generation sequencing data, you may
want to try plotting the quality data. Here is an example using two
FASTQ files containing paired end reads, SRR001666_1.fastq for the
forward reads, and SRR001666_2.fastq for the reverse reads. These were
downloaded from the ENA sequence read archive FTP site
(ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR001/SRR001666/SRR001666_1.fastq.g
z and
ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR001/SRR001666/SRR001666_2.fastq.gz
), and are from E. coli -- see
https://www.ebi.ac.uk/ena/data/view/SRR001666 for details. 
  In the following code the pylab.subplot(...) function is used in order
to show the forward and reverse qualities on two subplots, side by side.
There is also a little bit of code to only plot the first fifty reads.
<<import pylab
  from Bio import SeqIO

  for subfigure in [1, 2]:
      filename = "SRR001666_%i.fastq" % subfigure
      pylab.subplot(1, 2, subfigure)
      for i, record in enumerate(SeqIO.parse(filename, "fastq")):
          if i >= 50:
              break  # trick!
          pylab.plot(record.letter_annotations["phred_quality"])
      pylab.ylim(0, 45)
      pylab.ylabel("PHRED quality score")
      pylab.xlabel("Position")
  pylab.savefig("SRR001666.png")
  print("Done")
>>

  You should note that we are using the Bio.SeqIO format name fastq here
because the NCBI has saved these reads using the standard Sanger FASTQ
format with PHRED scores. However, as you might guess from the read
lengths, this data was from an Illumina Genome Analyzer and was probably
originally in one of the two Solexa/Illumina FASTQ variant file formats
instead.
  This example uses the pylab.savefig(...) function instead of
pylab.show(...), but as mentioned before both are useful.   Here is the
result:
  *images/SRR001666.png*



20.3 Dealing with alignments
*=*=*=*=*=*=*=*=*=*=*=*=*=*=


  This section can been seen as a follow on to Chapter ??.


20.3.1 Calculating summary information
======================================

  Once you have an alignment, you are very likely going to want to find
out information about it. Instead of trying to have all of the functions
that can generate information about an alignment in the alignment object
itself, we've tried to separate out the functionality into separate
classes, which act on the alignment.
  Getting ready to calculate summary information about an object is
quick to do. Let's say we've got an alignment object called alignment,
for example read in using Bio.AlignIO.read(...) as described in
Chapter ??. All we need to do to get an object that will calculate
summary information is:
<<from Bio.Align import AlignInfo

  summary_align = AlignInfo.SummaryInfo(alignment)
>>

  The summary_align object is very useful, and will do the following
neat things for you:


   1. Calculate a quick consensus sequence -- see section ?? 
   2. Get a position specific score matrix for the alignment -- see
   section ?? 
   3. Calculate the information content for the alignment -- see
   section ?? 
   4. Generate information on substitutions in the alignment --
   section ?? details using this to generate a substitution matrix. 



20.3.2 Calculating a quick consensus sequence
=============================================

  The SummaryInfo object, described in section ??, provides
functionality to calculate a quick consensus of an alignment. Assuming
we've got a SummaryInfo object called summary_align we can calculate a
consensus by doing:
<<consensus = summary_align.dumb_consensus()
>>

  As the name suggests, this is a really simple consensus calculator,
and will just add up all of the residues at each point in the consensus,
and if the most common value is higher than some threshold value will
add the common residue to the consensus. If it doesn't reach the
threshold, it adds an ambiguity character to the consensus. The returned
consensus object is a Seq object.
  You can adjust how dumb_consensus works by passing optional
parameters:


 the threshold  This is the threshold specifying how common a particular
   residue has to be at a position before it is added. The default is
   0.7 (meaning 70%).

 the ambiguous character  This is the ambiguity character to use. The
   default is 'N'.



20.3.3 Position Specific Score Matrices
=======================================

  Position specific score matrices (PSSMs) summarize the alignment
information in a different way than a consensus, and may be useful for
different tasks. Basically, a PSSM is a count matrix. For each column in
the alignment, the number of each alphabet letters is counted and
totaled. The totals are displayed relative to some representative
sequence along the left axis. This sequence may be the consesus
sequence, but can also be any sequence in the alignment. For instance
for the alignment,
<<GTATC
  AT--C
  CTGTC
>>

  the PSSM is:
<<      G A T C
      G 1 1 0 1
      T 0 0 3 0
      A 1 1 0 0
      T 0 0 2 0
      C 0 0 0 3
>>

  Let's assume we've got an alignment object called c_align. To get a
PSSM with the consensus sequence along the side we first get a summary
object and calculate the consensus sequence:
<<summary_align = AlignInfo.SummaryInfo(c_align)
  consensus = summary_align.dumb_consensus()
>>

  Now, we want to make the PSSM, but ignore any N ambiguity residues
when calculating this:
<<my_pssm = summary_align.pos_specific_score_matrix(consensus,
chars_to_ignore=["N"])
>>

  Two notes should be made about this:


   1. To maintain strictness with the alphabets, you can only include
   characters along the top of the PSSM that are in the alphabet of the
   alignment object. Gaps are not included along the top axis of the
   PSSM.

   2. The sequence passed to be displayed along the left side of the
   axis does not need to be the consensus. For instance, if you wanted
   to display the second sequence in the alignment along this axis, you
   would need to do:
   <<second_seq = alignment.get_seq_by_num(1)
     my_pssm = summary_align.pos_specific_score_matrix(second_seq,
   chars_to_ignore=["N"])
   >>


  The command above returns a PSSM object. To print out the PSSM as
shown above, we simply need to do a print(my_pssm), which gives:
<<    A   C   G   T
  T  0.0 0.0 0.0 7.0
  A  7.0 0.0 0.0 0.0
  T  0.0 0.0 0.0 7.0
  A  7.0 0.0 0.0 0.0
  C  0.0 7.0 0.0 0.0
  A  7.0 0.0 0.0 0.0
  T  0.0 0.0 0.0 7.0
  T  1.0 0.0 0.0 6.0
  ...
>>

  You can access any element of the PSSM by subscripting like
your_pssm[sequence_number][residue_count_name]. For instance, to get the
counts for the 'A' residue in the second element of the above PSSM you
would do:
<<>>> print(my_pssm[1]["A"])
  7.0
>>

  The structure of the PSSM class hopefully makes it easy both to access
elements and to pretty print the matrix.


20.3.4 Information Content
==========================

  A potentially useful measure of evolutionary conservation is the
information content of a sequence.
  A useful introduction to information theory targeted towards molecular
biologists can be found at
http://www.lecb.ncifcrf.gov/~toms/paper/primer/. For our purposes, we
will be looking at the information content of a consesus sequence, or a
portion of a consensus sequence. We calculate information content at a
particular column in a multiple sequence alignment using the following
formula:

                              N          (   ) 
                               a         (P  ) 
                              --         ( ij) 
                        IC  = \   P   log(---) 
                          j   /    ij    (   ) 
                              --         (Q  ) 
                              i=1        ( i ) 

  where:


   - IC_j -- The information content for the j-th column in an
   alignment. 
   - N_a -- The number of letters in the alphabet. 
   - P_ij -- The frequency of a particular letter i in the j-th column
   (i. e. if G occurred 3 out of 6 times in an aligment column, this
   would be 0.5) 
   - Q_i -- The expected frequency of a letter i. This is an optional
   argument, usage of which is left at the user's discretion. By
   default, it is automatically assigned to 0.05 = 1/20 for a protein
   alphabet, and 0.25 = 1/4 for a nucleic acid alphabet. This is for
   geting the information content without any assumption of prior
   distributions. When assuming priors, or when using a non-standard
   alphabet, you should supply the values for Q_i. 

  Well, now that we have an idea what information content is being
calculated in Biopython, let's look at how to get it for a particular
region of the alignment.
  First, we need to use our alignment to get an alignment summary
object, which we'll assume is called summary_align (see section ??) for
instructions on how to get this. Once we've got this object, calculating
the information content for a region is as easy as:
<<info_content = summary_align.information_content(5, 30,
chars_to_ignore=["N"])
>>

  Wow, that was much easier then the formula above made it look! The
variable info_content now contains a float value specifying the
information content over the specified region (from 5 to 30 of the
alignment). We specifically ignore the ambiguity residue 'N' when
calculating the information content, since this value is not included in
our alphabet (so we shouldn't be interested in looking at it!).
  As mentioned above, we can also calculate relative information content
by supplying a dictionary with the expected frequencies:
<<expect_freq = {"A": 0.3, "G": 0.2, "T": 0.3, "C": 0.2}
  info_content = summary_align.information_content(
      5, 30, e_freq_table=e_freq_table, chars_to_ignore=["N"]
  )
>>

  Now, info_content will contain the relative information content over
the region in relation to the expected frequencies.
  The value return is calculated using base 2 as the logarithm base in
the formula above. You can modify this by passing the parameter log_base
as the base you want:
<<info_content = summary_align.information_content(
      5, 30, log_base=10, chars_to_ignore=["N"]
  )
>>

  By default nucleotide or amino acid residues with a frequency of 0 in
a column are not take into account when the relative information column
for that column is computed. If this is not the desired result, you can
use pseudo_count instead.
<<info_content = summary_align.information_content(
      5, 30, chars_to_ignore=["N"], pseudo_count=1
  )
>>

  In this case, the observed frequency P_ij of a particular letter i in
the j-th column is computed as follow :

                                 n   + kX Q  
                                  ij       i 
                           P   = ----------- 
                            ij               
                                   N  + k    
                                    j        

  where:


   - k -- the pseudo count you pass as argument. 
   - k -- the pseudo count you pass as argument. 
   - Q_i -- The expected frequency of the letter i as described above. 

  Well, now you are ready to calculate information content. If you want
to try applying this to some real life problems, it would probably be
best to dig into the literature on information content to get an idea of
how it is used. Hopefully your digging won't reveal any mistakes made in
coding this function!


20.4 Substitution Matrices
*=*=*=*=*=*=*=*=*=*=*=*=*=


  Substitution matrices are an extremely important part of everyday
bioinformatics work. They provide the scoring terms for classifying how
likely two different residues are to substitute for each other. This is
essential in doing sequence comparisons. The book "Biological Sequence
Analysis" by Durbin et al. provides a really nice introduction to
Substitution Matrices and their uses. Some famous substitution matrices
are the PAM and BLOSUM series of matrices.
  Biopython provides a ton of common substitution matrices, and also
provides functionality for creating your own substitution matrices.


20.4.1 Using common substitution matrices
=========================================



20.4.2 Calculating a substitution matrix from a multiple sequence
=================================================================
alignment
=========

  You can create your own substitution matrix from an alignment. In this
example, we'll first read a protein sequence alignment from the Clustalw
file protein.aln (8) (also available online here (9))
<<>>> from Bio import AlignIO
  >>> filename = "protein.aln"
  >>> alignment = AlignIO.read(filename, "clustal")
>>

  Section ?? contains more information on doing this.
  The substitutions property of the alignment stores the number of times
different residues substitute for each other: 
<<>>> observed_frequencies = alignment.substitutions
>>

  To make the example more readable, we'll select only amino acids with
polar charged side chains:
<<>>> observed_frequencies = observed_frequencies.select("DEHKR")
  >>> print(observed_frequencies)
         D      E      H      K      R
  D 2360.0  255.5    7.5    0.5   25.0
  E  255.5 3305.0   16.5   27.0    2.0
  H    7.5   16.5 1235.0   16.0    8.5
  K    0.5   27.0   16.0 3218.0  116.5
  R   25.0    2.0    8.5  116.5 2079.0
  <BLANKLINE>
>>
   Rows and columns for other amino acids were removed from the matrix.
  Next, we normalize the matrix: 
<<>>> import numpy
  >>> observed_frequencies /= numpy.sum(observed_frequencies)
>>

  Summing over rows or columns gives the relative frequency of
occurrence of each residue: 
<<>>> residue_frequencies = numpy.sum(observed_frequencies, 0)
  >>> print(residue_frequencies.format("%.4f"))
  D 0.2015
  E 0.2743
  H 0.0976
  K 0.2569
  R 0.1697
  <BLANKLINE>
  >>> numpy.sum(residue_frequencies)
  1.0
>>

  The expected frequency of residue pairs is then 
<<>>> expected_frequencies = numpy.dot(residue_frequencies[:, None],
residue_frequencies[None, :])
  >>> print(expected_frequencies.format("%.4f"))
         D      E      H      K      R
  D 0.0406 0.0553 0.0197 0.0518 0.0342
  E 0.0553 0.0752 0.0268 0.0705 0.0465
  H 0.0197 0.0268 0.0095 0.0251 0.0166
  K 0.0518 0.0705 0.0251 0.0660 0.0436
  R 0.0342 0.0465 0.0166 0.0436 0.0288
  <BLANKLINE>
>>
   Here, residue_frequencies[:, None] creates a 2D array consisting of a
single column with the values of residue_frequencies, and
residue_frequencies[None, :] a 2D array with these values as a single
row. Taking their dot product (inner product) creates a matrix of
expected frequencies where each entry consists of two
residue_frequencies values multiplied with each other. For example,
expected_frequencies['D', 'E'] is equal to residue_frequencies['D'] *
residue_frequencies['E'].
  We can now calculate the log-odds matrix by dividing the observed
frequencies by the expected freqencies and taking the logarithm: 
<<>>> m = numpy.log2(observed_frequencies/expected_frequencies)
  >>> print(m)
        D    E    H     K    R
  D   2.1 -1.5 -5.1 -10.4 -4.2
  E  -1.5  1.7 -4.4  -5.1 -8.3
  H  -5.1 -4.4  3.3  -4.4 -4.7
  K -10.4 -5.1 -4.4   1.9 -2.3
  R  -4.2 -8.3 -4.7  -2.3  2.5
  <BLANKLINE>
>>

  This matrix can be used as the substitution matrix when performing
alignments. For example, 
<<>>> from Bio.Align import PairwiseAligner
  >>> aligner = PairwiseAligner()
  >>> aligner.substitution_matrix = m
  >>> aligner.gap_score = -3.0
  >>> alignments = aligner.align("DEHEK", "DHHKK")
  >>> print(alignments[0])
  DEHEK
  |.|.|
  DHHKK
  <BLANKLINE>
  >>> print("%.2f" % alignments.score)
  -2.18
  >>> score = m['D','D'] + m['E','H'] + m['H','H'] + m['E','K'] +
m['K','K']
  >>> print("%.2f" % score)
  -2.18
>>



20.5 BioSQL -- storing sequences in a relational database
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*

    BioSQL (10) is a joint effort between the OBF (11) projects
(BioPerl, BioJava etc) to support a shared database schema for storing
sequence data. In theory, you could load a GenBank file into the
database with BioPerl, then using Biopython extract this from the
database as a record object with features - and get more or less the
same thing as if you had loaded the GenBank file directly as a SeqRecord
using Bio.SeqIO (Chapter ??).
  Biopython's BioSQL module is currently documented at
http://biopython.org/wiki/BioSQL which is part of our wiki pages.   
-----------------------------------


 (1) https://raw.githubusercontent.com/biopython/biopython/master/Tests/
   GenBank/NC_005816.gb
 (2) http://www.biopython.org/docs/1.79/api/Bio.SeqIO.QualityIO.html
 (3) https://raw.githubusercontent.com/biopython/biopython/master/Tests/
   GenBank/NC_005816.fna
 (4) https://matplotlib.org
 (5) https://raw.githubusercontent.com/biopython/biopython/master/Doc/ex
   amples/ls_orchid.fasta
 (6) https://raw.githubusercontent.com/biopython/biopython/master/Doc/ex
   amples/ls_orchid.fasta
 (7) https://raw.githubusercontent.com/biopython/biopython/master/Doc/ex
   amples/ls_orchid.fasta
 (8) examples/protein.aln
 (9) https://raw.githubusercontent.com/biopython/biopython/master/Doc/ex
   amples/protein.aln
 (10) https://www.biosql.org/
 (11) https://www.open-bio.org/wiki/Main_Page


Chapter 21  The Biopython testing framework
*******************************************

  Biopython has a regression testing framework (the file run_tests.py)
based on unittest (1), the standard unit testing framework for Python.
Providing comprehensive tests for modules is one of the most important
aspects of making sure that the Biopython code is as bug-free as
possible before going out. It also tends to be one of the most
undervalued aspects of contributing. This chapter is designed to make
running the Biopython tests and writing good test code as easy as
possible. Ideally, every module that goes into Biopython should have a
test (and should also have documentation!). All our developers, and
anyone installing Biopython from source, are strongly encouraged to run
the unit tests.


21.1 Running the tests
*=*=*=*=*=*=*=*=*=*=*=


  When you download the Biopython source code, or check it out from our
source code repository, you should find a subdirectory call Tests. This
contains the key script run_tests.py, lots of individual scripts named
test_XXX.py, and lots of other subdirectories which contain input files
for the test suite.
  As part of building and installing Biopython you will typically run
the full test suite at the command line from the Biopython source top
level directory using the following:
<<$ python setup.py test
>>

  This is actually equivalent to going to the Tests subdirectory and
running:
<<$ python run_tests.py
>>

  You'll often want to run just some of the tests, and this is done like
this:
<<$ python run_tests.py test_SeqIO.py test_AlignIO.py
>>

  When giving the list of tests, the .py extension is optional, so you
can also just type:
<<$ python run_tests.py test_SeqIO test_AlignIO
>>

  To run the docstring tests (see section ??), you can use
<<$ python run_tests.py doctest
>>

  You can also skip any tests which have been setup with an explicit
online component by adding --offline, e.g.
<<$ python run_tests.py --offline
>>

  By default, run_tests.py runs all tests, including the docstring
tests.
  If an individual test is failing, you can also try running it
directly, which may give you more information.
  Tests based on Python's standard unittest framework will import
unittest and then define unittest.TestCase classes, each with one or
more sub-tests as methods starting with test_ which check some specific
aspect of the code.


21.1.1 Running the tests using Tox
==================================

  Like most Python projects, you can also use Tox (2) to run the tests
on multiple Python versions, provided they are already installed in your
system.
  We do not provide the configuration tox.ini file in our code base
because of difficulties pinning down user-specific settings (e.g.
executable names of the Python versions). You may also only be
interested in testing Biopython only against a subset of the Python
versions that we support.
  If you are interested in using Tox, you could start with the example
tox.ini shown below:
<<[tox]
  envlist = pypy,py36,py37

  [testenv]
  changedir = Tests
  commands = {envpython} run_tests.py --offline
  deps =
      numpy
      reportlab
>>

  Using the template above, executing tox will test your Biopython code
against PyPy, Python 3.6 and Python3.7. It assumes that those Pythons'
executables are named accordingly: "python3.7" for Python 3.7, and so
on.


21.2 Writing tests
*=*=*=*=*=*=*=*=*=


  Let's say you want to write some tests for a module called Biospam.
This can be a module you wrote, or an existing module that doesn't have
any tests yet. In the examples below, we assume that Biospam is a module
that does simple math.
  Each Biopython test consists of a script containing the test itself,
and optionally a directory with input files used by the test:


   1. test_Biospam.py -- The actual test code for your module. 
   2. Biospam [optional]-- A directory where any necessary input files
   will be located. If you have any output files that should be manually
   reviewed, output them here (but this is discouraged) to prevent
   clogging up the main Tests directory. In general, use a temporary
   file/folder. 

  Any script with a test_ prefix in the Tests directory will be found
and run by run_tests.py. Below, we show an example test script
test_Biospam.py. If you put this script in the Biopython Tests
directory, then run_tests.py will find it and execute the tests
contained in it:
<<$ python run_tests.py
  test_Ace ... ok
  test_AlignIO ... ok
  test_BioSQL ... ok
  test_BioSQL_SeqIO ... ok
  test_Biospam ... ok
  test_CAPS ... ok
  test_Clustalw ... ok
  ...
  ----------------------------------------------------------------------
  Ran 107 tests in 86.127 seconds
>>



21.2.1 Writing a test using unittest
====================================

  The unittest-framework has been included with Python since version
2.1, and is documented in the Python Library Reference (which I know you
are keeping under your pillow, as recommended). There is also online
documentaion for unittest (3). If you are familiar with the unittest
system (or something similar like the nose test framework), you
shouldn't have any trouble. You may find looking at the existing
examples within Biopython helpful too.
  Here's a minimal unittest-style test script for Biospam, which you can
copy and paste to get started:
<<import unittest
  from Bio import Biospam


  class BiospamTestAddition(unittest.TestCase):
      def test_addition1(self):
          result = Biospam.addition(2, 3)
          self.assertEqual(result, 5)

      def test_addition2(self):
          result = Biospam.addition(9, -1)
          self.assertEqual(result, 8)


  class BiospamTestDivision(unittest.TestCase):
      def test_division1(self):
          result = Biospam.division(3.0, 2.0)
          self.assertAlmostEqual(result, 1.5)

      def test_division2(self):
          result = Biospam.division(10.0, -2.0)
          self.assertAlmostEqual(result, -5.0)


  if __name__ == "__main__":
      runner = unittest.TextTestRunner(verbosity=2)
      unittest.main(testRunner=runner)
>>

  In the division tests, we use assertAlmostEqual instead of assertEqual
to avoid tests failing due to roundoff errors; see the unittest chapter
in the Python documentation for details and for other functionality
available in unittest (online reference (4)).
  These are the key points of unittest-based tests:


   - Test cases are stored in classes that derive from unittest.TestCase
   and cover one basic aspect of your code

   - You can use methods setUp and tearDown for any repeated code which
   should be run before and after each test method. For example, the
   setUp method might be used to create an instance of the object you
   are testing, or open a file handle. The tearDown should do any
   "tidying up", for example closing the file handle.

   - The tests are prefixed with test_ and each test should cover one
   specific part of what you are trying to test. You can have as many
   tests as you want in a class.

   - At the end of the test script, you can use 
   <<if __name__ == "__main__":
         runner = unittest.TextTestRunner(verbosity=2)
         unittest.main(testRunner=runner)
   >>
  to execute the tests when the script is run by itself (rather than
   imported from run_tests.py). If you run this script, then you'll see
   something like the following:
   <<$ python test_BiospamMyModule.py
     test_addition1 (__main__.TestAddition) ... ok
     test_addition2 (__main__.TestAddition) ... ok
     test_division1 (__main__.TestDivision) ... ok
     test_division2 (__main__.TestDivision) ... ok

     -------------------------------------------------------------------
   ---
     Ran 4 tests in 0.059s

     OK
   >>


   - To indicate more clearly what each test is doing, you can add
   docstrings to each test. These are shown when running the tests,
   which can be useful information if a test is failing.
   <<import unittest
     from Bio import Biospam


     class BiospamTestAddition(unittest.TestCase):
         def test_addition1(self):
             """An addition test"""
             result = Biospam.addition(2, 3)
             self.assertEqual(result, 5)

         def test_addition2(self):
             """A second addition test"""
             result = Biospam.addition(9, -1)
             self.assertEqual(result, 8)


     class BiospamTestDivision(unittest.TestCase):
         def test_division1(self):
             """Now let's check division"""
             result = Biospam.division(3.0, 2.0)
             self.assertAlmostEqual(result, 1.5)

         def test_division2(self):
             """A second division test"""
             result = Biospam.division(10.0, -2.0)
             self.assertAlmostEqual(result, -5.0)


     if __name__ == "__main__":
         runner = unittest.TextTestRunner(verbosity=2)
         unittest.main(testRunner=runner)
   >>

 Running the script will now show you:
   <<$ python test_BiospamMyModule.py
     An addition test ... ok
     A second addition test ... ok
     Now let's check division ... ok
     A second division test ... ok

     -------------------------------------------------------------------
   ---
     Ran 4 tests in 0.001s

     OK
   >>


  If your module contains docstring tests (see section ??), you may want
to include those in the tests to be run. You can do so as follows by
modifying the code under if __name__ == "__main__": to look like this:
<<if __name__ == "__main__":
      unittest_suite =
unittest.TestLoader().loadTestsFromName("test_Biospam")
      doctest_suite = doctest.DocTestSuite(Biospam)
      suite = unittest.TestSuite((unittest_suite, doctest_suite))
      runner = unittest.TextTestRunner(sys.stdout, verbosity=2)
      runner.run(suite)
>>

  This is only relevant if you want to run the docstring tests when you
execute python test_Biospam.py if it has some complex run-time
dependency checking.
  In general instead include the docstring tests by adding them to the
run_tests.py as explained below.


21.3 Writing doctests
*=*=*=*=*=*=*=*=*=*=*


  Python modules, classes and functions support built in documentation
using docstrings. The doctest framework (5) (included with Python)
allows the developer to embed working examples in the docstrings, and
have these examples automatically tested.
  Currently only part of Biopython includes doctests. The run_tests.py
script takes care of running the doctests. For this purpose, at the top
of the run_tests.py script is a manually compiled list of modules to
skip, important where optional external dependencies which may not be
installed (e.g. the Reportlab and NumPy libraries). So, if you've added
some doctests to the docstrings in a Biopython module, in order to have
them excluded in the Biopython test suite, you must update run_tests.py
to include your module. Currently, the relevant part of run_tests.py
looks as follows:
<<# Following modules have historic failures. If you fix one of these
  # please remove here!
  EXCLUDE_DOCTEST_MODULES = [
      "Bio.PDB",
      "Bio.PDB.AbstractPropertyMap",
      "Bio.Phylo.Applications._Fasttree",
      "Bio.Phylo._io",
      "Bio.Phylo.TreeConstruction",
      "Bio.Phylo._utils",
  ]

  # Exclude modules with online activity
  # They are not excluded by default, use --offline to exclude them
  ONLINE_DOCTEST_MODULES = ["Bio.Entrez", "Bio.ExPASy", "Bio.TogoWS"]

  # Silently ignore any doctests for modules requiring numpy!
  if numpy is None:
      EXCLUDE_DOCTEST_MODULES.extend(
          [
              "Bio.Affy.CelFile",
              "Bio.Cluster",
              # ...
          ]
      )
>>

  Note that we regard doctests primarily as documentation, so you should
stick to typical usage. Generally complicated examples dealing with
error conditions and the like would be best left to a dedicated unit
test.
  Note that if you want to write doctests involving file parsing,
defining the file location complicates matters. Ideally use relative
paths assuming the code will be run from the Tests directory, see the
Bio.SeqIO doctests for an example of this.
  To run the docstring tests only, use 
<<$ python run_tests.py doctest
>>

  Note that the doctest system is fragile and care is needed to ensure
your output will match on all the different versions of Python that
Biopython supports (e.g. differences in floating point numbers).


21.4 Writing doctests in the Tutorial
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*


  This Tutorial you are reading has a lot of code snippets, which are
often formatted like a doctest. We have our own system in file
test_Tutorial.py to allow tagging code snippets in the Tutorial source
to be run as Python doctests. This works by adding special %doctest
comment lines before each Python block, e.g.
<<
  %doctest
  \begin{minted}{pycon}
  >>> from Bio.Seq import Seq
  >>> s = Seq("ACGT")
  >>> len(s)
  4
  \end{minted}
>>

  Often code examples are not self-contained, but continue from the
previous Python block. Here we use the magic comment %cont-doctest as
shown here:
<<
  %cont-doctest
  \begin{minted}{pycon}
  >>> s == "ACGT"
  True
  \end{minted}
>>

  The special %doctest comment line can take a working directory
(relative to the Doc/ folder) to use if you have any example data files,
e.g. %doctest examples will use the Doc/examples folder, while %doctest
../Tests/GenBank will use the Tests/GenBank folder.
  After the directory argument, you can specify any Python dependencies
which must be present in order to run the test by adding lib:XXX to
indicate import XXX must work, e.g. %doctest examples lib:numpy
  You can run the Tutorial doctests via:
<<$ python test_Tutorial.py
>>

  or:
<<$ python run_tests.py test_Tutorial.py
>>

-----------------------------------


 (1) https://docs.python.org/3/library/unittest.html
 (2) https://tox.readthedocs.org/en/latest/
 (3) https://docs.python.org/3/library/unittest.html
 (4) https://docs.python.org/3/library/unittest.html
 (5) https://docs.python.org/3/library/doctest.html


Chapter 22  Advanced
********************



22.1 Parser Design
*=*=*=*=*=*=*=*=*=


  Many of the older Biopython parsers were built around an
event-oriented design that includes Scanner and Consumer objects.
  Scanners take input from a data source and analyze it line by line,
sending off an event whenever it recognizes some information in the
data. For example, if the data includes information about an organism
name, the scanner may generate an organism_name event whenever it
encounters a line containing the name.
  Consumers are objects that receive the events generated by Scanners.
Following the previous example, the consumer receives the organism_name
event, and the processes it in whatever manner necessary in the current
application.
  This is a very flexible framework, which is advantageous if you want
to be able to parse a file format into more than one representation. For
example, the Bio.GenBank module uses this to construct either SeqRecord
objects or file-format-specific record objects.
  More recently, many of the parsers added for Bio.SeqIO and Bio.AlignIO
take a much simpler approach, but only generate a single object
representation (SeqRecord and MultipleSeqAlignment objects
respectively). In some cases the Bio.SeqIO parsers actually wrap another
Biopython parser - for example, the Bio.SwissProt parser produces
SwissProt format specific record objects, which get converted into
SeqRecord objects.


22.2 Substitution Matrices
*=*=*=*=*=*=*=*=*=*=*=*=*=


  Please note that Bio.SubsMat was deprecated in Release 1.78. As an
alternative, please consider using Bio.Align.substitution_matrices
(described in section ??).


22.2.1 SubsMat
==============

  This module provides a class and a few routines for generating
substitution matrices, similar to BLOSUM or PAM matrices, but based on
user-provided data. Additionally, you may select a matrix from
MatrixInfo.py, a collection of established substitution matrices.
  The SeqMat class derives from a dictionary. The dictionary is of the
form {(i1,j1):n1, (i1,j2):n2,...,(ik,jk):nk} where i, j are alphabet
letters, and n is a value.


   1. Attributes 

      1. self.alphabet: a string consisting of the alphabet letters.

      2. self.ab_list: a list of the alphabet's letters, sorted. Needed
      mainly for internal purposes 


   2. Methods



      1. 
      <<__init__(self, data=None, alphabet=None, mat_name="",
      build_later=0)
      >>




         1. data: can be either a dictionary, or another SeqMat
         instance. 
         2. alphabet: an iterable (e.g., a string) over the alphabet
         letters.

         3. mat_name: matrix name, such as "BLOSUM62" or "PAM250"

         4. build_later: default false. If true, user may supply only
         alphabet and empty dictionary, if intending to build the matrix
         later. This skips the sanity check of alphabet size vs. matrix
         size.


      2. 
      <<entropy(self, obs_freq_mat)
      >>



         1. obs_freq_mat: an observed frequency matrix. Returns the
         matrix's entropy, based on the frequency in obs_freq_mat. The
         matrix instance should be LO or SUBS. 


      3. 
      <<sum(self)
      >>
     Calculates the sum of values for each letter in the matrix's
      alphabet, and returns it as a dictionary of the form {i1: s1, i2:
      s2,...,in:sn}, where: 

         - i: an alphabet letter; 
         - s: sum of all values in a half-matrix for that letter; 
         - n: number of letters in alphabet. 


      4. 
      <<format(self, fmt="%4d", topfmt="%4s", alphabet=None, full=False)
      >>

    Creates a string representation of the matrix. fmt is the format
      field for the matrix values; letterfmt is the format field for the
      bottom row (in case of a half matrix) or the top row (in case of a
      full matrix), containing matrix letters. Example output for a
      3-letter alphabet matrix:
      <<A 23
        B 12 34
        C 7  22  27
          A   B   C
      >>

    The alphabet optional argument is an iterable (e.g. a string) over
      all letters in the alphabet. If supplied, the order of letters
      along the axes is taken from the string, rather than by
      alphabetical order.


   3. Usage
 The following section is laid out in the order by which most people
   wish to generate a log-odds matrix. Of course, interim matrices can
   be generated and investigated. Most people just want a log-odds
   matrix, that's all.



      1. Generating an Accepted Replacement Matrix
    Initially, you should generate an accepted replacement matrix (ARM)
      from your data. The values in ARM are the counted number of
      replacements according to your data. The data could be a set of
      pairs or multiple alignments. So for instance if Alanine was
      replaced by Cysteine 10 times, and Cysteine by Alanine 12 times,
      the corresponding ARM entries would be:
      <<('A','C'): 10, ('C','A'): 12
      >>

    as order doesn't matter, user can already provide only one entry:
      <<('A','C'): 22
      >>

    A SeqMat instance may be initialized with either a full (first
      method of counting: 10, 12) or half (the latter method, 22)
      matrices. A full protein alphabet matrix would be of the size
      20x20 = 400. A half matrix of that alphabet would be 20x20/2 +
      20/2 = 210. That is because same-letter entries don't change. (The
      matrix diagonal). Given an alphabet size of N:


         1. Full matrix size: N*N

         2. Half matrix size: N(N+1)/2 

    The SeqMat constructor automatically generates a half-matrix, if a
      full matrix is passed.
    At this point, if all you wish to do is generate a log-odds matrix,
      please go to the section titled Example of Use. The following text
      describes the nitty-gritty of internal functions, to be used by
      people who wish to investigate their nucleotide/amino-acid
      frequency data more thoroughly.

      2. Generating the observed frequency matrix (OFM)
    Use: 
      <<OFM = SubsMat._build_obs_freq_mat(ARM)
      >>

    The OFM is generated from the ARM, only instead of replacement
      counts, it contains replacement frequencies.

      3. Generating an expected frequency matrix (EFM)
    Use:
      <<EFM = SubsMat._build_exp_freq_mat(OFM, exp_freq_table)
      >>



         1. exp_freq_table: should be a FreqTable instance. See
         section ?? for detailed information on FreqTable. Briefly, the
         expected frequency table has the frequencies of appearance for
         each letter in the alphabet. It is implemented as a dictionary
         with the alphabet letters as keys, and each letter's frequency
         as a value. Values sum to 1. 

    The expected frequency table can (and generally should) be generated
      from the observed frequency matrix. So in most cases you will
      generate exp_freq_table using:
      <<>>> exp_freq_table = SubsMat._exp_freq_table_from_obs_freq(OFM)
        >>> EFM = SubsMat._build_exp_freq_mat(OFM, exp_freq_table)
      >>

    But you can supply your own exp_freq_table, if you wish

      4. Generating a substitution frequency matrix (SFM)
    Use:
      <<SFM = SubsMat._build_subs_mat(OFM, EFM)
      >>

    Accepts an OFM, EFM. Provides the division product of the
      corresponding values.

      5. Generating a log-odds matrix (LOM)
    Use: 
      <<LOM = SubsMat._build_log_odds_mat(SFM, logbase=10, factor=10.0,
      round_digit=1)
      >>



         1. Accepts an SFM.

         2. logbase: base of the logarithm used to generate the log-odds
         values.

         3. factor: factor used to multiply the log-odds values. Each
         entry is generated by log(LOM[key])*factor And rounded to the
         round_digit place after the decimal point, if required.



   4. Example of use
 As most people would want to generate a log-odds matrix, with minimum
   hassle, SubsMat provides one function which does it all:
   <<make_log_odds_matrix(
         acc_rep_mat, exp_freq_table=None, logbase=10, factor=10.0,
   round_digit=0
     )
   >>



      1. acc_rep_mat: user provided accepted replacements matrix 
      2. exp_freq_table: expected frequencies table. Used if provided,
      if not, generated from the acc_rep_mat. 
      3. logbase: base of logarithm for the log-odds matrix. Default
      base 10. 
      4. round_digit: number after decimal digit to which result should
      be rounded. Default zero. 




22.2.2 FreqTable
================

<<FreqTable.FreqTable(dict)
>>




   1. Attributes:


      1. alphabet: A string containing the letters in the alphabet. 
      2. data: frequency dictionary 
      3. count: count dictionary (in case counts are provided). 


   2. Functions: 

      1. read_count(f): read a count file from stream f. Then convert to
      frequencies. 
      2. read_freq(f): read a frequency data file from stream f. Of
      course, we then don't have the counts, but it is usually the
      letter frequencies which are interesting. 


   3. Example of use: The expected count of the residues in the database
   is sitting in a file, whitespace delimited, in the following format
   (example given for an alphabet consisting of three letters):
   <<A   35
     B   65
     C   100
   >>

 And will be read using the FreqTable.read_count(file_handle) function.
 An equivalent frequency file:
   <<A  0.175
     B  0.325
     C  0.5
   >>

 Conversely, the residue frequencies or counts can be passed as a
   dictionary. Example of a count dictionary (same alphabet of three
   letters):
   <<{"A": 35, "B": 65, "C": 100}
   >>

 Which means that an expected data count would give a 0.5 frequency for
   'C', a 0.325 probability of 'B' and a 0.175 probability of 'A' out of
   200 total, sum of A, B and C)
 A frequency dictionary for the same data would be:
   <<{"A": 0.175, "B": 0.325, "C": 0.5}
   >>

 Summing up to 1.
 When passing a dictionary as an argument, you should indicate whether
   it is a count or a frequency dictionary. Therefore the FreqTable
   class constructor requires two arguments: the dictionary itself, and
   FreqTable.COUNT or FreqTable.FREQ indicating counts or frequencies,
   respectively.
 Read expected counts. readCount will already generate the frequencies
   Any one of the following may be done to geerate the frequency table
   (ftab):
   <<>>> from SubsMat import *
     >>> ftab = FreqTable.FreqTable(my_frequency_dictionary,
   FreqTable.FREQ)
     >>> ftab = FreqTable.FreqTable(my_count_dictionary,
   FreqTable.COUNT)
     >>> ftab = FreqTable.read_count(open("myCountFile"))
     >>> ftab = FreqTable.read_frequency(open("myFrequencyFile"))
   >>



Chapter 23  Where to go from here -- contributing to Biopython
**************************************************************



23.1 Bug Reports + Feature Requests
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*


  Getting feedback on the Biopython modules is very important to us.
Open-source projects like this benefit greatly from feedback,
bug-reports (and patches!) from a wide variety of contributors.
  The main forums for discussing feature requests and potential bugs are
the Biopython mailing list (1) and issues or pull requests on GitHub.
  Additionally, if you think you've found a new bug, you can submit it
to our issue tracker at https://github.com/biopython/biopython/issues
(this replaced the older Open Bioinformatics Foundation hosted RedMine
tracker). This way, it won't get buried in anyone's Inbox and forgotten
about.


23.2 Mailing lists and helping newcomers
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=


  We encourage all our uses to sign up to the main Biopython mailing
list. Once you've got the hang of an area of Biopython, we'd encourage
you to help answer questions from beginners. After all, you were a
beginner once.


23.3 Contributing Documentation
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*


  We're happy to take feedback or contributions - either via a
bug-report or on the Mailing List. While reading this tutorial, perhaps
you noticed some topics you were interested in which were missing, or
not clearly explained. There is also Biopython's built in documentation
(the docstrings, these are also online (2)), where again, you may be
able to help fill in any blanks.


23.4 Contributing cookbook examples
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*

   As explained in Chapter ??, Biopython now has a wiki collection of
user contributed "cookbook" examples,
http://biopython.org/wiki/Category:Cookbook -- maybe you can add to
this?


23.5 Maintaining a distribution for a platform
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=


  We currently provide source code archives (suitable for any OS, if you
have the right build tools installed), and Windows Installers which are
just click and run. This covers all the major operating systems.
  Most major Linux distributions have volunteers who take these source
code releases, and compile them into packages for Linux users to easily
install (taking care of dependencies etc). This is really great and we
are of course very grateful. If you would like to contribute to this
work, please find out more about how your Linux distribution handles
this.
  Below are some tips for certain platforms to maybe get people started
with helping out:



 Windows  -- Windows products typically have a nice graphical installer
   that installs all of the essential components in the right place. We
   use Distutils to create a installer of this type fairly easily.
 You must first make sure you have a C compiler on your Windows
   computer, and that you can compile and install things (this is the
   hard bit - see the Biopython installation instructions for info on
   how to do this).
 Once you are setup with a C compiler, making the installer just
   requires doing:
   <<$ python setup.py bdist_wininst
   >>

 Now you've got a Windows installer. Congrats! At the moment we have no
   trouble shipping installers built on 32 bit windows. If anyone would
   like to look into supporting 64 bit Windows that would be great.

 RPMs  -- RPMs are pretty popular package systems on some Linux
   platforms. There is lots of documentation on RPMs available at
   http://www.rpm.org to help you get started with them. To create an
   RPM for your platform is really easy. You just need to be able to
   build the package from source (having a C compiler that works is thus
   essential) -- see the Biopython installation instructions for more
   info on this.
 To make the RPM, you just need to do:
   <<$ python setup.py bdist_rpm
   >>

 This will create an RPM for your specific platform and a source RPM in
   the directory dist. This RPM should be good and ready to go, so this
   is all you need to do! Nice and easy.

 Macintosh  -- Since Apple moved to Mac OS X, things have become much
   easier on the Mac. We generally treat it as just another Unix
   variant, and installing Biopython from source is just as easy as on
   Linux. The easiest way to get all the GCC compilers etc installed is
   to install Apple's X-Code. We might be able to provide click and run
   installers for Mac OS X, but to date there hasn't been any demand.

  Once you've got a package, please test it on your system to make sure
it installs everything in a good way and seems to work properly. Once
you feel good about it, make a pull request on GitHub and write to our
Biopython mailing list (3). You've done it. Thanks!


23.6 Contributing Unit Tests
*=*=*=*=*=*=*=*=*=*=*=*=*=*=


  Even if you don't have any new functionality to add to Biopython, but
you want to write some code, please consider extending our unit test
coverage. We've devoted all of Chapter ?? to this topic.


23.7 Contributing Code
*=*=*=*=*=*=*=*=*=*=*=


  There are no barriers to joining Biopython code development other than
an interest in creating biology-related code in Python. The best place
to express an interest is on the Biopython mailing lists -- just let us
know you are interested in coding and what kind of stuff you want to
work on. Normally, we try to have some discussion on modules before
coding them, since that helps generate good ideas -- then just feel free
to jump right in and start coding!
  The main Biopython release tries to be fairly uniform and
interworkable, to make it easier for users. You can read about some of
(fairly informal) coding style guidelines we try to use in Biopython in
the contributing documentation at
http://biopython.org/wiki/Contributing. We also try to add code to the
distribution along with tests (see Chapter ?? for more info on the
regression testing framework) and documentation, so that everything can
stay as workable and well documented as possible (including docstrings).
This is, of course, the most ideal situation, under many situations
you'll be able to find other people on the list who will be willing to
help add documentation or more tests for your code once you make it
available. So, to end this paragraph like the last, feel free to start
working!
  Please note that to make a code contribution you must have the legal
right to contribute it and license it under the Biopython license. If
you wrote it all yourself, and it is not based on any other code, this
shouldn't be a problem. However, there are issues if you want to
contribute a derivative work - for example something based on GPL or
LPGL licenced code would not be compatible with our license. If you have
any queries on this, please discuss the issue on the mailing list or
GitHub.
  Another point of concern for any additions to Biopython regards any
build time or run time dependencies. Generally speaking, writing code to
interact with a standalone tool (like BLAST, EMBOSS or ClustalW) doesn't
present a big problem. However, any dependency on another library - even
a Python library (especially one needed in order to compile and install
Biopython like NumPy) would need further discussion.
  Additionally, if you have code that you don't think fits in the
distribution, but that you want to make available, we maintain Script
Central (http://biopython.org/wiki/Scriptcentral) which has pointers to
freely available code in Python for bioinformatics.
  Hopefully this documentation has got you excited enough about
Biopython to try it out (and most importantly, contribute!). Thanks for
reading all the way through!   
-----------------------------------


 (1) http://biopython.org/wiki/Mailing_lists
 (2) http://biopython.org/DIST/docs/api
 (3) http://biopython.org/wiki/Mailing_lists


Chapter 24  Appendix: Useful stuff about Python
***********************************************

  If you haven't spent a lot of time programming in Python, many
questions and problems that come up in using Biopython are often related
to Python itself. This section tries to present some ideas and code that
come up often (at least for us!) while using the Biopython libraries. If
you have any suggestions for useful pointers that could go here, please
contribute!


24.1 What the heck is a handle?
*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*


  Handles are mentioned quite frequently throughout this documentation,
and are also fairly confusing (at least to me!). Basically, you can
think of a handle as being a "wrapper" around text information.
  Handles provide (at least) two benefits over plain text information:


   1. They provide a standard way to deal with information stored in
   different ways. The text information can be in a file, or in a string
   stored in memory, or the output from a command line program, or at
   some remote website, but the handle provides a common way of dealing
   with information in all of these formats.

   2. They allow text information to be read incrementally, instead of
   all at once. This is really important when you are dealing with huge
   text files which would use up all of your memory if you had to load
   them all. 

  Handles can deal with text information that is being read
(e. g. reading from a file) or written (e. g. writing information to a
file). In the case of a "read" handle, commonly used functions are
read(), which reads the entire text information from the handle, and
readline(), which reads information one line at a time. For "write"
handles, the function write() is regularly used.
  The most common usage for handles is reading information from a file,
which is done using the built-in Python function open. Here, we handle
to the file m_cold.fasta which you can download here (1) (or find
included in the Biopython source code as Doc/examples/m_cold.fasta).
<<>>> handle = open("m_cold.fasta", "r")
  >>> handle.readline()
  ">gi|8332116|gb|BE037100.1|BE037100 MP14H09 MP Mesembryanthemum ...\n"
>>

  Handles are regularly used in Biopython for passing information to
parsers. For example, since Biopython 1.54 the main functions in
Bio.SeqIO and Bio.AlignIO have allowed you to use a filename instead of
a handle:
<<from Bio import SeqIO

  for record in SeqIO.parse("m_cold.fasta", "fasta"):
      print(record.id, len(record))
>>

  On older versions of Biopython you had to use a handle, e.g.
<<from Bio import SeqIO

  handle = open("m_cold.fasta", "r")
  for record in SeqIO.parse(handle, "fasta"):
      print(record.id, len(record))
  handle.close()
>>

  This pattern is still useful - for example suppose you have a gzip
compressed FASTA file you want to parse:
<<import gzip
  from Bio import SeqIO

  handle = gzip.open("m_cold.fasta.gz", "rt")
  for record in SeqIO.parse(handle, "fasta"):
      print(record.id, len(record))
  handle.close()
>>

  With our parsers for plain text files, it is essential to use gzip in
text mode (the default is binary mode).
  See Section ?? for more examples like this, including reading bzip2
compressed files.


24.1.1 Creating a handle from a string
======================================

  One useful thing is to be able to turn information contained in a
string into a handle. The following example shows how to do this using
cStringIO from the Python standard library:
<<>>> my_info = "A string\n with multiple lines."
  >>> print(my_info)
  A string
   with multiple lines.
  >>> from io import StringIO
  >>> my_info_handle = StringIO(my_info)
  >>> first_line = my_info_handle.readline()
  >>> print(first_line)
  A string
  <BLANKLINE>
  >>> second_line = my_info_handle.readline()
  >>> print(second_line)
   with multiple lines.
>>

-----------------------------------


 (1) https://raw.githubusercontent.com/biopython/biopython/master/Doc/ex
   amples/m_cold.fasta


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