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dualguide_UMI_fastqgz_to_counts.py
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dualguide_UMI_fastqgz_to_counts.py
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import os
import gzip
import sys
from Bio import Seq
import multiprocessing
import itertools
import argparse
import pandas as pd
import string
def writeToCounts(fileTup):
print('\t'.join(fileTup[:-2]))
sys.stdout.flush()
mismatchDicts = fileTup[-2]
testRun = fileTup[-1]
statsCounts = {'A sgRNA not mapped': 0,
'B sgRNA not mapped': 0,
'A sgRNA multiple mappings': 0,
'B sgRNA multiple mappings': 0,
'UMI not mapped':0,
'UMI multiple mappings':0,
'All sgRNAs uniquely map': 0,
'A sgRNA and B sgRNA do not match': 0,
'A sgRNA and B sgRNA match': 0}
pairCounts_sgRNAs = dict()
pairCounts_double = dict()
with gzip.open(fileTup[0]) as infile_r1:
with gzip.open(fileTup[1]) as infile_r2:
with gzip.open(fileTup[2]) as infile_r3:
for i, (r1,r2,r3) in enumerate(zip(infile_r1, infile_r2, infile_r3)):
if i%4 == 1:
##trimming is adjusted based on sequencing strategy
protospacer_a_r1 = matchBarcode(mismatchDicts['protospacer_a_r1'], r1[:19].strip().decode("utf-8"), allowOneMismatch = False)
protospacer_b_r2 = matchBarcode(mismatchDicts['protospacer_b_r2'], r3[:19].strip().decode("utf-8"), allowOneMismatch = False)
umi = matchBarcode(mismatchDicts['umi'], r2[:8].strip().decode("utf-8"), allowOneMismatch = False)
if protospacer_a_r1 == 'none' \
or protospacer_b_r2 == 'none' \
or protospacer_a_r1 == 'multiple' \
or protospacer_b_r2 == 'multiple' \
or umi == 'none' or umi =='multiple':
if protospacer_a_r1 == 'none':
statsCounts['A sgRNA not mapped'] += 1
if protospacer_b_r2 == 'none':
statsCounts['B sgRNA not mapped'] += 1
if protospacer_a_r1 == 'multiple':
statsCounts['A sgRNA multiple mappings'] += 1
if protospacer_b_r2 == 'multiple':
statsCounts['B sgRNA multiple mappings'] += 1
if umi == 'none':
statsCounts['UMI not mapped'] += 1
if umi == 'multiple':
statsCounts['UMI multiple mappings'] += 1
else:
statsCounts['All sgRNAs uniquely map'] += 1
combinedSgId = protospacer_a_r1 + '++' + protospacer_b_r2
if combinedSgId not in pairCounts_sgRNAs:
pairCounts_sgRNAs[combinedSgId] = 0
pairCounts_sgRNAs[combinedSgId] += 1
if protospacer_a_r1 != protospacer_b_r2 :
statsCounts['A sgRNA and B sgRNA do not match'] += 1
else:
statsCounts['A sgRNA and B sgRNA match'] += 1
combinedSgId_UMI = protospacer_a_r1 + '++' + protospacer_b_r2 + '++' + str(umi)
if combinedSgId_UMI not in pairCounts_double:
pairCounts_double[combinedSgId_UMI] = 0
pairCounts_double[combinedSgId_UMI] += 1
if testRun and i == testLines:
break
with open(fileTup[3] + '.all.aligned.counts.txt', 'w') as outfile:
for pair in sorted(pairCounts_sgRNAs.keys()):
outfile.write(pair + '\t' + str(pairCounts_sgRNAs[pair]) + '\n')
with open(fileTup[3] + '.AB.match.counts.txt', 'w') as outfile:
for pair in sorted(pairCounts_double.keys()):
outfile.write(pair + '\t' + str(pairCounts_double[pair]) + '\n')
numReads = (i+1)/4
print(fileTup[3], numReads, 'reads')
print('Percent A sgRNAs mapping', 100.0 - (statsCounts['A sgRNA not mapped'] * 100.0 / numReads))
print('Percent B sgRNAs mapping', 100.0 - (statsCounts['B sgRNA not mapped'] * 100.0 / numReads))
print('Percent UMIs mapping', 100.0 - (statsCounts['UMI not mapped'] * 100.0 / numReads))
print('Percent all sgRNAs mapping', statsCounts['All sgRNAs uniquely map'] * 100.0 / numReads)
print('Percent A sgRNA and B sgRNA mismatch', statsCounts['A sgRNA and B sgRNA do not match'] * 100.0 / statsCounts['All sgRNAs uniquely map'])
print('Percent both A and B match', statsCounts['A sgRNA and B sgRNA match'] * 100.0 / statsCounts['All sgRNAs uniquely map'])
print('Total percent matching and mapping reads', statsCounts['A sgRNA and B sgRNA match'] * 100.0 / numReads)
sys.stdout.flush()
def getMismatchDict(table, column, trim_range = None, allowOneMismatch = True):
mismatch_dict = dict()
if trim_range:
col = table[column].apply(lambda seq: seq[trim_range[0]:trim_range[1]])
else:
col = table[column]
for sgRNA, seq in col.iteritems():
if seq in mismatch_dict:
print('clash with 0 mismatches', sgRNA, seq)
mismatch_dict[seq] = 'multiple'
else:
mismatch_dict[seq] = sgRNA
if allowOneMismatch:
for position in range(len(seq)):
mismatchSeq = seq[:position] + 'N' + seq[position + 1:]
if mismatchSeq in mismatch_dict:
print('clash with 1 mismatch', sgRNA, mismatchSeq)
mismatch_dict[seq] = 'multiple'
else:
mismatch_dict[mismatchSeq] = sgRNA
return mismatch_dict
def matchBarcode(mismatch_dict, barcode, allowOneMismatch = True):
if barcode in mismatch_dict:
match = mismatch_dict[barcode]
elif allowOneMismatch:
match = 'none'
for position in range(len(barcode)):
mismatchSeq = barcode[:position] + 'N' + barcode[position + 1:]
if mismatchSeq in mismatch_dict:
if match == 'none':
match = mismatch_dict[mismatchSeq]
else:
match = 'multiple'
else:
match = 'none'
return match
testLines = 100000
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Process raw sequencing data from screens to counts files in parallel.')
parser.add_argument('Guide_Table', help='Table of sgRNA pairs in the library.')
parser.add_argument('UMI_Table', help='Table of sgRNA pairs in the library.')
parser.add_argument('Out_File_Path', help='Directory where output files should be written.')
parser.add_argument('Seq_File_Names', nargs='+', help='Name(s) of sequencing file(s). Unix wildcards can be used to select multiple files at once. The script will search for all *.fastq.gz, *.fastq, and *.fa(/fasta/fna) files with the given wildcard name.')
parser.add_argument('--test', action='store_true', default=False, help='Run the entire script on only the first %d reads of each file. Be sure to delete or move all test files before re-running script as they will not be overwritten.' % testLines)
args = parser.parse_args()
outputDirectory = args.Out_File_Path
inputFileList = sorted(args.Seq_File_Names)
guideTable = pd.read_csv(args.Guide_Table, sep=',', header=0)[['sgID_AB','sgID_A','protospacer_A','sgID_B','protospacer_B']]
guideTable = guideTable.set_index('sgID_AB').dropna()
##convert protospacers to mimick reads according to sequencing strategy
##assuming R1=19, R2=8 (UMI), R3=19
guideTable['protospacer_A']=guideTable['protospacer_A'].str[1:20].str.upper()
trans=str.maketrans('ATGC', 'TACG')
guideTable['protospacer_B']=guideTable['protospacer_B'].str[1:20].str.upper().str.translate(trans).str[::-1]
print('sgRNAs in library', len(guideTable))
##assigne umiTable
umiTable = pd.read_csv(args.UMI_Table, sep=',', header=0)[['UMI']]
combinedMismatchDicts = {'protospacer_a_r1': getMismatchDict(guideTable, 'protospacer_A', allowOneMismatch=False),
'protospacer_b_r2': getMismatchDict(guideTable, 'protospacer_B', allowOneMismatch=False),
'umi': getMismatchDict(umiTable, 'UMI', allowOneMismatch=True),
}
fileTups = []
for i, fastqfile in enumerate(inputFileList):
if i%3 == 0:
r1file = fastqfile
elif i%3 == 1:
r2file = fastqfile
elif i%3 == 2:
r3file = fastqfile
outputfile = os.path.join(outputDirectory, os.path.split(fastqfile)[-1].split('_R')[0])
fileTups.append((r1file, r2file, r3file, outputfile, combinedMismatchDicts, args.test))
pool = multiprocessing.Pool(len(fileTups))
pool.map(writeToCounts, fileTups)
pool.close()
pool.join()