PYCHOPPER(1)

NAME
pychopper - package documentation

COMMAND LINE TOOLS

  Command line tools

FULL API REFERENCE

  pychopper

  pychopper package

  Subpackages

  pychopper.phmm_data package

  Module contents

  pychopper.primer_data package

  Module contents

  pychopper.scripts package

  Submodules

  pychopper.scripts.pychopper module

    pychopper.scripts.pychopper.main()
        Parse command line arguments.

  Module contents

  pychopper.tests package

  Submodules

  pychopper.tests.test_detector module

    class pychopper.tests.test_detector.TestDetector(methodName='runTest')
        Bases: TestCase

        Create an instance of the class that will use the named test method when executed. Raises a ValueError if the instance does not have a method with the specified name.

        testPairAlign()

        testScoreCutoff()

  pychopper.tests.test_regression_simple module

    class pychopper.tests.test_regression_simple.TestIntegration(methodName='runTest')
        Bases: TestCase

        Create an instance of the class that will use the named test method when executed. Raises a ValueError if the instance does not have a method with the specified name.

        testIntegration()
            Integration test.

        testIntegration_umi()
            Integration test.

  Module contents

  Submodules

  pychopper.alignment_hits module

    pychopper.alignment_hits.process_hits(hits, max_score)
        Process alignment hits by removing overlaps

  pychopper.chopper module

    pychopper.chopper.analyse_hits(hits, config)
        Segment reads based on alignment hits using dynamic programming. The algorithm is based on the rule that each primer alignment hit can be used only once. Hence if a segment is included, the next one has to be excluded.

    pychopper.chopper.chopper_edlib(reads, primers, config, max_ed, cutoff, pool, min_batch)
        Segment using the edlib/parasail backend

    pychopper.chopper.chopper_phmm(reads, phmm_file, config, cutoff, threads, pool, min_batch)
        Segment using the profile HMM backend

    pychopper.chopper.segments_to_reads(read, segments, keep_primers, bam_tags, detect_umis)
        Convert segments to output reads with annotation

  pychopper.common_structures module

    class pychopper.common_structures.Hit(Ref, RefStart, RefEnd, Query, QueryStart, QueryEnd, Score)
        Bases: tuple

        Create new instance of Hit(Ref, RefStart, RefEnd, Query, QueryStart, QueryEnd, Score)

        Query
            Alias for field number 3

        QueryEnd
            Alias for field number 5

        QueryStart
            Alias for field number 4

        Ref Alias for field number 0

        RefEnd
            Alias for field number 2

        RefStart
            Alias for field number 1

        Score
            Alias for field number 6

    class pychopper.common_structures.Segment(Left, Start, End, Right, Strand, Len)
        Bases: tuple

        Create new instance of Segment(Left, Start, End, Right, Strand, Len)

        End Alias for field number 2

        Left
            Alias for field number 0

        Len Alias for field number 5

        Right
            Alias for field number 3

        Start
            Alias for field number 1

        Strand
            Alias for field number 4

    class pychopper.common_structures.Seq(Id, Name, Seq, Qual, Umi)
        Bases: tuple

        Create new instance of Seq(Id, Name, Seq, Qual, Umi)

        Id  Alias for field number 0

        Name
            Alias for field number 1

        Qual
            Alias for field number 3

        Seq Alias for field number 2

        Umi Alias for field number 4

  pychopper.edlib_backend module

    pychopper.edlib_backend.find_locations(reads, all_primers, max_ed, pool, min_batch)
        Find alignment hits of all primers in all reads using the edlib/parasail backend

    pychopper.edlib_backend.find_umi_single(params)
        Find UMI in a single reads using the edlib/parasail backend

  pychopper.hmmer_backend module

    pychopper.hmmer_backend.find_locations(reads, phmm_file, E, pool, min_batch)
        Find alignment hits of all primers in all reads using the pHMM/nhmmscan backend

  pychopper.parasail_backend module

    pychopper.parasail_backend.first_cigar(cigar)
        Extract details of the first operation in a cigar string.

    pychopper.parasail_backend.pair_align(reference, query, query_name, subs_mat, params)
        Perform pairwise local alignment using parsail-python

    pychopper.parasail_backend.process_alignment(aln, query, query_name, aln_params)
        Process an alignment, extracting score, start and end.

    pychopper.parasail_backend.refine_locations(read, all_primers, locations, aln_params={'gap_extend': 1, 'gap_open': 1, 'match': 1, 'mismatch': -2}, subs_mat=<parasail.bindings_v2.Matrix object>)
        Refine alignment edges based on local alignment

  pychopper.report module

    class pychopper.report.Report(pdf)
        Bases: object

        Class for plotting utilities on the top of matplotlib. Plots are saved in the specified file through the PDF backend.

        Parameters

            •   self -- object.

            •   pdf -- Output pdf.

        Returns
            The report object.

        Return type
            Report <#pychopper.report.Report>

        close()
            Close PDF backend. Do not forget to call this at the end of your script or your output will be damaged!

            Parameters
                self -- object

            Returns
                None

            Return type
                object

        plot_arrays(data_map, title='', xlab='', ylab='', marker='.', legend_loc='best', legend=True, vlines=None, vlcolor='green', vlwitdh=0.5)
            Plot multiple pairs of data arrays.

            Parameters

                •   self -- object.

                •   data_map -- A dictionary with labels as keys and tupples of data arrays (x,y) as values.

                •   title -- Figure title.

                •   xlab -- X axis label.

                •   ylab -- Y axis label.

                •   marker -- Marker passed to the plot function.

                •   legend_loc -- Location of legend.

                •   legend -- Plot legend if True

                •   vlines -- Dictionary with labels and positions of vertical lines to draw.

                •   vlcolor -- Color of vertical lines drawn.

                •   vlwidth -- Width of vertical lines drawn.

            Returns
                None

            Return type
                object

        plot_bars_simple(data_map, title='', xlab='', ylab='', alpha=0.6, xticks_rotation=0, auto_limit=False)
            Plot simple bar chart from input dictionary.

            Parameters

                •   self -- object.

                •   data_map -- A dictionary with labels as keys and data as values.

                •   title -- Figure title.

                •   xlab -- X axis label.

                •   ylab -- Y axis label.

                •   alpha -- Alpha value.

                •   xticks_rotation -- Rotation value for x tick labels.

                •   auto_limit -- Set y axis limits automatically.

            Returns
                None

            Return type
                object

        plot_histograms(data_map, title='', xlab='', ylab='', bins=50, alpha=0.7, legend_loc='best', legend=True, vlines=None)
            Plot histograms of multiple data arrays.

            Parameters

                •   self -- object.

                •   data_map -- A dictionary with labels as keys and data arrays as values.

                •   title -- Figure title.

                •   xlab -- X axis label.

                •   ylab -- Y axis label.

                •   bins -- Number of bins.

                •   alpha -- Transparency value for histograms.

                •   legend_loc -- Location of legend.

                •   legend -- Plot legend if True.

                •   vlines -- Dictionary with labels and positions of vertical lines to draw.

            Returns
                None

            Return type
                object

        save_close()
            Utility method to save and close figure.

  pychopper.seq_utils module

Utilities manipulating biological sequences and formats. Extensions to biopython functionality.

    pychopper.seq_utils.base_complement(k)
        Return complement of base.

        Performs the subsitutions: A<=>T, C<=>G, X=>X for both upper and lower case. The return value is identical to the argument for all other values.

        Parameters
            k -- A base.

        Returns
            Complement of base.

        Return type
            str

    pychopper.seq_utils.errs_tab(n)
        Generate list of error rates for qualities less than equal than n.

    pychopper.seq_utils.get_primers(primers)
        Load primers from fasta file

    pychopper.seq_utils.get_runid(desc)
        Parse out runid from sequence description.

    pychopper.seq_utils.mean_qual(quals, qround=False, tab=[1.0, 0.7943282347242815, 0.6309573444801932, 0.5011872336272722, 0.3981071705534972, 0.31622776601683794, 0.251188643150958, 0.19952623149688797, 0.15848931924611134, 0.12589254117941673, 0.1, 0.07943282347242814, 0.06309573444801933, 0.05011872336272722, 0.039810717055349734, 0.03162277660168379, 0.025118864315095794, 0.0199526231496888, 0.015848931924611134, 0.012589254117941675, 0.01, 0.007943282347242814, 0.00630957344480193, 0.005011872336272725, 0.003981071705534973, 0.0031622776601683794, 0.0025118864315095794, 0.001995262314968879, 0.001584893192461114, 0.0012589254117941675, 0.001, 0.0007943282347242813, 0.000630957344480193, 0.0005011872336272725, 0.00039810717055349735, 0.00031622776601683794, 0.00025118864315095795, 0.00019952623149688788, 0.00015848931924611142, 0.00012589254117941674, 0.0001, 7.943282347242822e-05, 6.309573444801929e-05, 5.011872336272725e-05, 3.9810717055349695e-05, 3.1622776601683795e-05, 2.5118864315095822e-05, 1.9952623149688786e-05, 1.584893192461114e-05, 1.2589254117941661e-05, 1e-05, 7.943282347242822e-06, 6.30957344480193e-06, 5.011872336272725e-06, 3.981071705534969e-06, 3.162277660168379e-06, 2.5118864315095823e-06, 1.9952623149688787e-06, 1.584893192461114e-06, 1.2589254117941661e-06, 1e-06, 7.943282347242822e-07, 6.30957344480193e-07, 5.011872336272725e-07, 3.981071705534969e-07, 3.162277660168379e-07, 2.5118864315095823e-07, 1.9952623149688787e-07, 1.584893192461114e-07, 1.2589254117941662e-07, 1e-07, 7.943282347242822e-08, 6.30957344480193e-08, 5.011872336272725e-08, 3.981071705534969e-08, 3.162277660168379e-08, 2.511886431509582e-08, 1.9952623149688786e-08, 1.5848931924611143e-08, 1.2589254117941661e-08, 1e-08, 7.943282347242822e-09, 6.309573444801943e-09, 5.011872336272715e-09, 3.981071705534969e-09, 3.1622776601683795e-09, 2.511886431509582e-09, 1.9952623149688828e-09, 1.584893192461111e-09, 1.2589254117941663e-09, 1e-09, 7.943282347242822e-10, 6.309573444801942e-10, 5.011872336272714e-10, 3.9810717055349694e-10, 3.1622776601683795e-10, 2.511886431509582e-10, 1.9952623149688828e-10, 1.584893192461111e-10, 1.2589254117941662e-10, 1e-10, 7.943282347242822e-11, 6.309573444801942e-11, 5.011872336272715e-11, 3.9810717055349695e-11, 3.1622776601683794e-11, 2.5118864315095823e-11, 1.9952623149688828e-11, 1.5848931924611107e-11, 1.2589254117941662e-11, 1e-11, 7.943282347242821e-12, 6.309573444801943e-12, 5.011872336272715e-12, 3.9810717055349695e-12, 3.1622776601683794e-12, 2.5118864315095823e-12, 1.9952623149688827e-12, 1.584893192461111e-12, 1.258925411794166e-12, 1e-12, 7.943282347242822e-13, 6.309573444801942e-13, 5.011872336272715e-13, 3.981071705534969e-13, 3.162277660168379e-13, 2.511886431509582e-13, 1.9952623149688827e-13, 1.584893192461111e-13])
        Calculate average basecall quality of a read. Receive the ascii quality scores of a read and return the average quality for that read First convert Phred scores to probabilities, calculate average error probability convert average back to Phred scale

    pychopper.seq_utils.readfq(fastq, sample=None, min_qual=0, rfq_sup={})
        Read fastx files.

        This is a generator function that yields sequtils.Seq objects. Optionally filter by a minimum mean quality (min_qual). Optionally subsample the fastx file using sample (0.0 - 1.0)

    pychopper.seq_utils.record_size(read, in_format='fastq')
        Calculate record size.

    pychopper.seq_utils.revcomp_seq(seq)
        Reverse complement sequence record

    pychopper.seq_utils.reverse_complement(seq)
        Reverse complement sequence.

        Param
            input sequence string.

        Returns
            reverse-complemented string.

    pychopper.seq_utils.writefq(r, fh)
        Write read to fastq file

  pychopper.utils module

    pychopper.utils.batch(iterable, size)

    pychopper.utils.check_command(cmd)

    pychopper.utils.check_min_hmmer_version(major, minor)

    pychopper.utils.count_fastq_records(fname, size=128000000, opener=<built-in function open>)

    pychopper.utils.hit2bed(hit, read)

    pychopper.utils.parse_config_string(s)

  Module contents

    •   Index <>

    •   Module Index <>

    •   Search Page <>

Author
ONT Applications Group

Copyright
2016, Oxford Nanopore Technologies Ltd.