def parse_fastx_sam_parallel(fastx_infile, sam_infile):
""" Parse fastx and resulting sam file in parallel - generator yielding (name, seq, alignment_list) tuples.
The sam file may contain multiple alignments per read. Program checks that the readnames match.
"""
fastx_generator = basic_seq_utilities.name_seq_generator_from_fasta_fastq(fastx_infile)
sam_generator = iter(HTSeq.bundle_multiple_alignments(HTSeq.SAM_Reader(sam_infile)))
if_finished_fastx, if_finished_sam = False, False
while True:
try: name, seq = fastx_generator.next()
except StopIteration: if_finished_fastx = True
try: alns = sam_generator.next()
except StopIteration: if_finished_sam = True
# if both finished, good, we're doine
if if_finished_fastx and if_finished_sam:
raise StopIteration
# if one file was finished but the other wasn't, error!
elif if_finished_fastx or if_finished_sam:
raise DeepseqError("Parsing seq/aln files in parallel - inconsistent finished states! "
+"(If finished: %s %s, %s %s)"%(fastx_infile, if_finished_fastx, sam_infile, if_finished_sam))
# if all the files still contained data, yield it
else:
name = name.split()[0]
name2 = alns[0].read.name.split()[0]
if not name2 == name:
raise DeepseqError("Non-matching readnames between files! %s in %s, %s in %s"%(fastx_infile, name,
sam_infile, name2))
yield (name, seq, alns)
def set_up_IO(fileIN,fileOUT,gff,downstream,upstream):
'''Function that will open all the file required for the alignment processing
'''
## Open alignment
alignIN = HTSeq.SAM_Reader(fileIN)
alignIN = HTSeq.bundle_multiple_alignments(alignIN)
## Open GFF file
annotation = HTSeq.GFF_Reader(gff,end_included = True)
## Open output file - write the header
countTable = open(fileOUT,'w')
coordinates = '\t'.join(i for i in map(str,range(-upstream,downstream)))
countTable.write('name\t{coord}\n'.format(coord = coordinates))
return alignIN, annotation, countTable
def set_up_IO(fileIN, fileOUT, gff, downstream, upstream):
'''Function that will open all the file required for the alignment processing
'''
## Open alignment
alignIN = HTSeq.SAM_Reader(fileIN)
alignIN = HTSeq.bundle_multiple_alignments(alignIN)
## Open GFF file
annotation = HTSeq.GFF_Reader(gff, end_included=True)
## Open output file - write the header
countTable = open(fileOUT, 'w')
coordinates = '\t'.join(i for i in map(str, range(-upstream, downstream)))
countTable.write('name\t{coord}\n'.format(coord=coordinates))
return alignIN, annotation, countTable
def test_sam_parser_comparison(self):
file = f"{resources}/Lib304_test.sam"
ours = SAM_reader().bundle_multi_alignments(file)
theirs = HTSeq.bundle_multiple_alignments(HTSeq.BAM_Reader(file))
for our_bundle, their_bundle in zip(ours, theirs):
self.assertEqual(len(our_bundle), len(their_bundle))
for our, their in zip(our_bundle, their_bundle):
self.assertEqual(our['chrom'], their.iv.chrom)
self.assertEqual(our['start'], their.iv.start)
self.assertEqual(our['end'], their.iv.end)
self.assertEqual(our['name'], their.read.name)
self.assertEqual(our['nt5'],
chr(their.read.seq[0])) # See note above
self.assertEqual(our['strand'], their.iv.strand)
if our['strand'] == '-': # See note above
self.assertEqual(
our['seq'][::-1].translate(helpers.complement),
their.read.seq)
else:
self.assertEqual(our['seq'], their.read.seq)
#sort you SAM file by read ID, so that multiple mappings are in adjacent lines and the write a script to filter the best one
#Written by Simon Anders
import sys, re
import HTSeq
insam = HTSeq.SAM_Reader(sys.stdin)
# Go through all reads, with their alignments bundled up:
for bundle in HTSeq.bundle_multiple_alignments(insam):
bestAlmt = None
# Go through all alignments of a given read, looking
# for the one with the best alignment score
for almt in bundle:
if bestAlmt is None:
bestAlmt = almt
elif almt.aQual > bestAlmt.aQual:
bestAlmt = almt
elif almt.aQual == bestAlmt:
# If there are more than one best alignment,
# better skip the read
bestAlmt = None
if bestAlmt is not None:
# Change the NH field to 1 and print the line
print re.sub("NH:i:\d+", "NH:i:1", bestAlmt.original_sam_line)
#call this script with the command sort samfile.sam | python chooseBest.py > filtered.sam
#sort you SAM file by read ID, so that multiple mappings are in adjacent lines and the write a script to filter the best one
#Written by Simon Anders
import sys, re
import HTSeq
insam = HTSeq.SAM_Reader( sys.stdin )
# Go through all reads, with their alignments bundled up:
for bundle in HTSeq.bundle_multiple_alignments( insam ):
bestAlmt = None
# Go through all alignments of a given read, looking
# for the one with the best alignment score
for almt in bundle:
if bestAlmt is None:
bestAlmt = almt
elif almt.aQual > bestAlmt.aQual:
bestAlmt = almt
elif almt.aQual == bestAlmt:
# If there are more than one best alignment,
# better skip the read
bestAlmt = None
if bestAlmt is not None:
# Change the NH field to 1 and print the line
print re.sub( "NH:i:\d+", "NH:i:1", bestAlmt.original_sam_line )
#call this script with the command sort samfile.sam | python chooseBest.py > filtered.sam
