# HG changeset patch # User devteam # Date 1400517277 14400 # Node ID 556ceed246997dfaba4724a14bfe14da441cee54 Imported from capsule None diff -r 000000000000 -r 556ceed24699 short_reads_figure_high_quality_length.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/short_reads_figure_high_quality_length.py Mon May 19 12:34:37 2014 -0400 @@ -0,0 +1,165 @@ +#!/usr/bin/env python + +import os, sys, math, tempfile, zipfile, re +from rpy import * + +assert sys.version_info[:2] >= ( 2, 4 ) + +def stop_err( msg ): + sys.stderr.write( "%s\n" % msg ) + sys.exit() + +def unzip( filename ): + zip_file = zipfile.ZipFile( filename, 'r' ) + tmpfilename = tempfile.NamedTemporaryFile().name + for name in zip_file.namelist(): + file( tmpfilename, 'a' ).write( zip_file.read( name ) ) + zip_file.close() + return tmpfilename + +def __main__(): + infile_score_name = sys.argv[1].strip() + outfile_R_name = sys.argv[2].strip() + + try: + score_threshold = int( sys.argv[3].strip() ) + except: + stop_err( 'Threshold for quality score must be numerical.' ) + + infile_is_zipped = False + if zipfile.is_zipfile( infile_score_name ): + infile_is_zipped = True + infile_name = unzip( infile_score_name ) + else: + infile_name = infile_score_name + + # detect whether it's tabular or fasta format + seq_method = None + data_type = None + for i, line in enumerate( file( infile_name ) ): + line = line.rstrip( '\r\n' ) + if not line or line.startswith( '#' ): + continue + if data_type == None: + if line.startswith( '>' ): + data_type = 'fasta' + continue + elif len( line.split( '\t' ) ) > 0: + fields = line.split() + for score in fields: + try: + int( score ) + data_type = 'tabular' + seq_method = 'solexa' + break + except: + break + elif data_type == 'fasta': + fields = line.split() + for score in fields: + try: + int( score ) + seq_method = '454' + break + except: + break + if i == 100: + break + + if data_type is None: + stop_err( 'This tool can only use fasta data or tabular data.' ) + if seq_method is None: + stop_err( 'Invalid data for fasta format.') + + cont_high_quality = [] + invalid_lines = 0 + invalid_scores = 0 + if seq_method == 'solexa': + for i, line in enumerate( open( infile_name ) ): + line = line.rstrip( '\r\n' ) + if not line or line.startswith( '#' ): + continue + locs = line.split( '\t' ) + for j, base in enumerate( locs ): + nuc_errors = base.split() + try: + nuc_errors[0] = int( nuc_errors[0] ) + nuc_errors[1] = int( nuc_errors[1] ) + nuc_errors[2] = int( nuc_errors[2] ) + nuc_errors[3] = int( nuc_errors[3] ) + big = max( nuc_errors ) + except: + invalid_scores += 1 + big = 0 + if j == 0: + cont_high_quality.append(1) + else: + if big >= score_threshold: + cont_high_quality[ len( cont_high_quality ) - 1 ] += 1 + else: + cont_high_quality.append(1) + else: # seq_method == '454' + tmp_score = '' + for i, line in enumerate( open( infile_name ) ): + line = line.rstrip( '\r\n' ) + if not line or line.startswith( '#' ): + continue + if line.startswith( '>' ): + if len( tmp_score ) > 0: + locs = tmp_score.split() + for j, base in enumerate( locs ): + try: + base = int( base ) + except: + invalid_scores += 1 + base = 0 + if j == 0: + cont_high_quality.append(1) + else: + if base >= score_threshold: + cont_high_quality[ len( cont_high_quality ) - 1 ] += 1 + else: + cont_high_quality.append(1) + tmp_score = '' + else: + tmp_score = "%s %s" % ( tmp_score, line ) + if len( tmp_score ) > 0: + locs = tmp_score.split() + for j, base in enumerate( locs ): + try: + base = int( base ) + except: + invalid_scores += 1 + base = 0 + if j == 0: + cont_high_quality.append(1) + else: + if base >= score_threshold: + cont_high_quality[ len( cont_high_quality ) - 1 ] += 1 + else: + cont_high_quality.append(1) + + # generate pdf figures + cont_high_quality = array ( cont_high_quality ) + outfile_R_pdf = outfile_R_name + r.pdf( outfile_R_pdf ) + title = "Histogram of continuous high quality scores" + xlim_range = [ 1, max( cont_high_quality ) ] + nclass = max( cont_high_quality ) + if nclass > 100: + nclass = 100 + r.hist( cont_high_quality, probability=True, xlab="Continuous High Quality Score length (bp)", ylab="Frequency (%)", xlim=xlim_range, main=title, nclass=nclass) + r.dev_off() + + if infile_is_zipped and os.path.exists( infile_name ): + # Need to delete temporary file created when we unzipped the infile archive + os.remove( infile_name ) + + if invalid_lines > 0: + print 'Skipped %d invalid lines. ' % invalid_lines + if invalid_scores > 0: + print 'Skipped %d invalid scores. ' % invalid_scores + + r.quit( save="no" ) + +if __name__=="__main__":__main__() \ No newline at end of file diff -r 000000000000 -r 556ceed24699 short_reads_figure_high_quality_length.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/short_reads_figure_high_quality_length.xml Mon May 19 12:34:37 2014 -0400 @@ -0,0 +1,74 @@ + + of high quality score reads + + rpy + R + +short_reads_figure_high_quality_length.py $input1 $output1 $input2 + + + + + + + + + + + + + + + + + + + + + + + + +.. class:: warningmark + +To use this tool, your dataset needs to be in the *Quality Score* format. Click the pencil icon next to your dataset to set the datatype to *Quality Score* (see below for examples). + +----- + +**What it does** + +This tool takes Quality Files generated by Roche (454), Illumina (Solexa), or ABI SOLiD machines and builds a histogram of lengths of high quality reads. + +----- + +**Examples of Quality Data** + +Roche (454) or ABI SOLiD data:: + + >seq1 + 23 33 34 25 28 28 28 32 23 34 27 4 28 28 31 21 28 + +Illumina (Solexa) data:: + + -40 -40 40 -40 -40 -40 -40 40 + +----- + +**Note** + +- Quality score data:: + + >seq1 + 23 33 34 25 28 28 28 32 23 34 27 4 28 28 31 21 28 + +- If the threshold is set to 20: + + - a low quality score 4 in the middle separated two segments of lengths 11 and 5. + + - The histogram will be built based on the numbers (11, 5). + +- For Illumina (Solexa) data, only the maximal of the 4 values will be used. + + + + diff -r 000000000000 -r 556ceed24699 test-data/454.qual --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ 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