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PyCogent 1.9 documentation

# Loading nucleotide, protein sequences

## `LoadSeqs` from a file

### As an alignment

The function `LoadSeqs()` creates either a sequence collection or an alignment depending on the keyword argument `aligned` (the default is `True`).

    >>> from cogent import LoadSeqs, DNA
    >>> aln = LoadSeqs('data/long_testseqs.fasta', moltype=DNA)
    >>> type(aln)
    <class 'cogent.core.alignment.Alignment'>

This example and some of the following use the `long_testseqs.fasta` file.

### As a sequence collection (unaligned)

Setting the `LoadSeqs()` function keyword argument `aligned=False` returns a sequence collection.

    >>> from cogent import LoadSeqs, DNA
    >>> seqs = LoadSeqs('data/long_testseqs.fasta', moltype=DNA, aligned=False)
    >>> print type(seqs)
    <class 'cogent.core.alignment.SequenceCollection'>

Note

An alignment can be sliced, but a `SequenceCollection` can not.

### Specifying the file format

`LoadSeqs()` uses the filename suffix to infer the file format. This can be overridden using the `format` argument.

    >>> from cogent import LoadSeqs, DNA
    >>> aln = LoadSeqs('data/long_testseqs.fasta', moltype=DNA,
    ...                  format='fasta')
    ...
    >>> aln
    5 x 2532 dna alignment: Human[TGTGGCACAAA...

## `LoadSeqs` from a series of strings

    >>> from cogent import LoadSeqs
    >>> seqs = ['>seq1','AATCG-A','>seq2','AATCGGA']
    >>> seqs_loaded = LoadSeqs(data=seqs)
    >>> print seqs_loaded
    >seq1
    AATCG-A
    >seq2
    AATCGGA

## `LoadSeqs` from a dict of strings

    >>> from cogent import LoadSeqs
    >>> seqs = {'seq1': 'AATCG-A','seq2': 'AATCGGA'}
    >>> seqs_loaded = LoadSeqs(data=seqs)

## Specifying the sequence molecular type

Simple case of loading a `list` of aligned amino acid sequences in FASTA format, with and without molecule type specification. When the `MolType` is not specified it defaults to ASCII.

    >>> from cogent import LoadSeqs
    >>> from cogent import DNA, PROTEIN
    >>> protein_seqs = ['>seq1','DEKQL-RG','>seq2','DDK--SRG']
    >>> proteins_loaded = LoadSeqs(data=protein_seqs)
    >>> proteins_loaded.MolType
    MolType(('a', 'b', 'c', 'd', 'e', ...
    >>> print proteins_loaded
    >seq1
    DEKQL-RG
    >seq2
    DDK--SRG

    >>> proteins_loaded = LoadSeqs(data=protein_seqs, moltype=PROTEIN)
    >>> print proteins_loaded
    >seq1
    DEKQL-RG
    >seq2
    DDK--SRG

## Stripping label characters on loading

Load a list of aligned nucleotide sequences, while specifying the DNA molecule type and stripping the comments from the label. In this example, stripping is accomplished by passing a function that removes everything after the first whitespace to the `label_to_name` parameter.

    >>> from cogent import LoadSeqs, DNA
    >>> DNA_seqs = ['>sample1 Mus musculus','AACCTGC--C','>sample2 Gallus gallus','AAC-TGCAAC']
    >>> loaded_seqs = LoadSeqs(data=DNA_seqs, moltype=DNA, label_to_name=lambda x: x.split()[0])
    >>> print loaded_seqs
    >sample1
    AACCTGC--C
    >sample2
    AAC-TGCAAC

## Using alternative constructors for the Alignment object

An example of using an alternative constructor is given below. A constructor is passed to the aligned parameter in lieu of `True` or `False`.

    >>> from cogent import LoadSeqs
    >>> from cogent.core.alignment import DenseAlignment
    >>> seqs = ['>seq1','AATCG-A','>seq2','AATCGGA']
    >>> seqs_loaded = LoadSeqs(data=seqs,aligned=DenseAlignment)
    >>> print seqs_loaded
    >seq1
    AATCG-A
    >seq2
    AATCGGA

## Loading sequences using format parsers

`LoadSeqs` is just a convenience interface to format parsers. It can sometimes be more effective to use the parsers directly, say when you don’t want to load everything into memory.

### Loading FASTA sequences from an open file or list of lines

To load FASTA formatted sequences directly, you can use the `MinimalFastaParser`.

Note

This returns the sequences as strings.

    >>> from cogent.parse.fasta import MinimalFastaParser
    >>> f=open('data/long_testseqs.fasta')
    >>> seqs = [(name, seq) for name, seq in MinimalFastaParser(f)]
    >>> print seqs
    [('Human', 'TGTGGCACAAATAC...

### Handling overloaded FASTA sequence labels

The FASTA label field is frequently overloaded, with different information fields present in the field and separated by some delimiter. This can be flexibly addressed using the `LabelParser`. By creating a custom label parser, we can decided which part we use as the sequence name. We show how convert a field into something specific.

    >>> from cogent.parse.fasta import LabelParser
    >>> def latin_to_common(latin):
    ...     return {'Homo sapiens': 'human',
    ...             'Pan troglodtyes': 'chimp'}[latin]
    >>> label_parser = LabelParser("%(species)s",
    ...             [[1, "species", latin_to_common]], split_with=':')
    >>> for label in ">abcd:Homo sapiens:misc", ">abcd:Pan troglodtyes:misc":
    ...     label = label_parser(label)
    ...     print label, type(label)
    human <class 'cogent.parse.fasta.RichLabel'>
    chimp <class 'cogent.parse.fasta.RichLabel'>

The `RichLabel` objects have an `Info` object as an attribute, allowing specific reference to all the specified label fields.

    >>> from cogent.parse.fasta import MinimalFastaParser, LabelParser
    >>> fasta_data = ['>gi|10047090|ref|NP_055147.1| small muscle protein, X-linked [Homo sapiens]',
    ...  'MNMSKQPVSNVRAIQANINIPMGAFRPGAGQPPRRKECTPEVEEGVPPTSDEEKKPIPGAKKLPGPAVNL',
    ... 'SEIQNIKSELKYVPKAEQ',
    ... '>gi|10047092|ref|NP_037391.1| neuronal protein [Homo sapiens]',
    ... 'MANRGPSYGLSREVQEKIEQKYDADLENKLVDWIILQCAEDIEHPPPGRAHFQKWLMDGTVLCKLINSLY',
    ... 'PPGQEPIPKISESKMAFKQMEQISQFLKAAETYGVRTTDIFQTVDLWEGKDMAAVQRTLMALGSVAVTKD']
    ...
    >>> label_to_name = LabelParser("%(ref)s",
    ...                              [[1,"gi", str],
    ...                               [3, "ref", str],
    ...                               [4, "description", str]],
    ...                               split_with="|")
    ...
    >>> for name, seq in MinimalFastaParser(fasta_data, label_to_name=label_to_name):
    ...     print name
    ...     print name.Info.gi
    ...     print name.Info.description
    NP_055147.1
    10047090
     small muscle protein, X-linked [Homo sapiens]
    NP_037391.1
    10047092
     neuronal protein [Homo sapiens]