def demultiplex_fastq(outdir, samplesheet, fastq1, fastq2=None):
"""Demultiplex a bcl-converted illumina fastq file. Assumes it has the index sequence
in the header a la CASAVA 1.8+
"""
outfiles = {}
counts = {}
sdata = HiSeqRun.parse_samplesheet(samplesheet)
reads = [1]
if fastq2 is not None:
reads.append(2)
# For each Lane-Index combination, create a file and open a filehandle
for sd in sdata:
lane = sd['Lane']
index = sd['Index']
if lane not in outfiles:
outfiles[lane] = {}
counts[lane] = {}
outfiles[lane][index] = []
counts[lane][index] = 0
for read in reads:
fname = "tmp_{}_{}_L00{}_R{}_001.fastq.gz".format(sd['SampleID'],
index,
lane,
read)
outfiles[lane][index].append(FastQWriter(os.path.join(outdir,fname)))
# Parse the input file(s) and write the records to the appropriate output files
fhs = [FastQParser(fastq1)]
if fastq2 is not None:
fhs.append(FastQParser(fastq2))
for r, fh in enumerate(fhs):
for record in fh:
header = parse_header(record[0])
lane = str(header['lane'])
index = header['index']
if lane in outfiles and index in outfiles[lane]:
outfiles[lane][index][r].write(record)
counts[lane][index] += 1
# Close filehandles and replace the handles with the file names
for lane in outfiles.keys():
for index in outfiles[lane].keys():
for r, fh in enumerate(outfiles[lane][index]):
fh.close()
fname = fh.name()
# If no sequences were written, remove the temporary file and the entry from the results
if counts[lane][index] == 0:
os.unlink(fname)
del outfiles[lane][index]
break
# Rename the temporary file to a persistent name
nname = fname.replace("tmp_","")
os.rename(fname,nname)
outfiles[lane][index][r] = nname
return outfiles
def test_parse_samplesheet(self):
"""Write and parse a csv-file
"""
# Assert non-existing file raises exception
with self.assertRaises(IOError):
HiSeqRun.parse_samplesheet(os.path.join(self.rootdir,'non-existing-samplesheet'))
# Write a csv file with some bogus values
sdata = td.generate_samplesheet_data()
samplesheet = os.path.join(self.rootdir,'SampleSheet.csv')
HiSeqRun.write_samplesheet(sdata,samplesheet)
# Assert that the written data corresponds to the generated data
with open(samplesheet) as fh:
# Assert that header is correct
self.assertListEqual(HiSeqRun._samplesheet_header(),
fh.next().strip().split(","),
"Written header does not match expected header")
for entry in sdata:
# Assert that all rows have the correct values in the correct columns
self.assertListEqual([str(e) for e in entry],
fh.next().strip().split(","),
"Written data row does not match entry in generated samplesheet")
# Assert that all rows from samplesheet has been consumed
with self.assertRaises(StopIteration):
fh.next()
# Assert that the parsed data matches the generated data
data = HiSeqRun.parse_samplesheet(samplesheet)
self.assertEqual(len(sdata),
len(data),
"Number of parsed entries does not match number of generated entries")
for d in data:
self.assertListEqual([str(e) for e in sdata.pop(0)],
[d[col] for col in HiSeqRun._samplesheet_header()],
"Parsed data row does not match entry in generated samplesheet")
# Assert that filtering on lane returns expected output
lanes = list(set([d["Lane"] for d in data]))
obs_lane_data = HiSeqRun.parse_samplesheet(samplesheet,lane=lanes[-1])
exp_lane_data = [d for d in data if str(d["Lane"]) == str(lanes[-1])]
self.assertListEqual(sorted(obs_lane_data),
sorted(exp_lane_data),
"Parsed data row does not match entry in generated samplesheet")
def demultiplex_fastq(outdir, samplesheet, fastq1, fastq2=None):
"""Demultiplex a bcl-converted illumina fastq file. Assumes it has the index sequence
in the header a la CASAVA 1.8+
"""
outfiles = {}
counts = {}
sdata = HiSeqRun.parse_samplesheet(samplesheet)
reads = [1]
if fastq2 is not None:
reads.append(2)
# For each Lane-Index combination, create a file and open a filehandle
for sd in sdata:
lane = sd['Lane']
index = sd['Index']
if lane not in outfiles:
outfiles[lane] = {}
counts[lane] = {}
outfiles[lane][index] = []
counts[lane][index] = 0
for read in reads:
fname = "tmp_{}_{}_L00{}_R{}_001.fastq.gz".format(
sd['SampleID'], index, lane, read)
outfiles[lane][index].append(
FastQWriter(os.path.join(outdir, fname)))
# Parse the input file(s) and write the records to the appropriate output files
fhs = [FastQParser(fastq1)]
if fastq2 is not None:
fhs.append(FastQParser(fastq2))
for r, fh in enumerate(fhs):
for record in fh:
header = parse_header(record[0])
lane = str(header['lane'])
index = header['index']
if lane in outfiles and index in outfiles[lane]:
outfiles[lane][index][r].write(record)
counts[lane][index] += 1
# Close filehandles and replace the handles with the file names
for lane in outfiles.keys():
for index in outfiles[lane].keys():
for r, fh in enumerate(outfiles[lane][index]):
fh.close()
fname = fh.name()
# If no sequences were written, remove the temporary file and the entry from the results
if counts[lane][index] == 0:
os.unlink(fname)
del outfiles[lane][index]
break
# Rename the temporary file to a persistent name
nname = fname.replace("tmp_", "")
os.rename(fname, nname)
outfiles[lane][index][r] = nname
return outfiles
def get_expected(csv_file, lane):
"""Extract the expected barcodes in a lane from a supplied csv samplesheet
"""
rows = HiSeqRun.parse_samplesheet(csv_file, lane=lane)
return [r["Index"] for r in rows]
def get_expected(csv_file, lane):
"""Extract the expected barcodes in a lane from a supplied csv samplesheet
"""
rows = HiSeqRun.parse_samplesheet(csv_file,lane=lane)
return [r["Index"] for r in rows]
