def _create_tabix(fname, ftype):
logger = logging.getLogger("pita")
tabix_file = ""
logger.info("Creating tabix index for %s", os.path.basename(fname))
logger.debug("Preparing %s for tabix", fname)
tmp = NamedTemporaryFile(prefix="pita", delete=False)
preset = "gff"
if ftype == "bed":
cmd = "sort -k1,1 -k2g,2 {0} | grep -v track | grep -v \"^#\" > {1}"
preset = "bed"
elif ftype in ["gff", "gff3", "gtf"]:
cmd = "sort -k1,1 -k4g,4 {0} | grep -v \"^#\" > {1}"
# Sort the input file
logger.debug(cmd.format(fname, tmp.name))
sp.call(cmd.format(fname, tmp.name), shell=True)
# Compress using bgzip
logger.debug("compressing %s", tmp.name)
tabix_file = tmp.name + ".gz"
pysam.tabix_compress(tmp.name, tabix_file)
tmp.close()
# Index (using tabix command line, as pysam.index results in a Segmentation fault
logger.debug("indexing %s", tabix_file)
sp.call("tabix {0} -p {1}".format(tabix_file, preset), shell=True)
return tabix_file
def annotate_vcf(self, inVcf, genome, outVcf, JVMmemory=None):
"""
Annotate variants in VCF file with translation consequences using snpEff.
"""
if outVcf.endswith('.vcf.gz'):
tmpVcf = util.file.mkstempfname(prefix='vcf_snpEff-', suffix='.vcf')
elif outVcf.endswith('.vcf'):
tmpVcf = outVcf
else:
raise Exception("invalid input")
args = [
'-treatAllAsProteinCoding', 'false',
'-t',
'-noLog',
'-ud', '0',
'-noStats',
'-noShiftHgvs',
genome,
inVcf
]
with open(tmpVcf, 'wt') as outf:
self.execute('ann', args, JVMmemory=JVMmemory, stdout=outf)
if outVcf.endswith('.vcf.gz'):
pysam.tabix_compress(tmpVcf, outVcf, force=True)
pysam.tabix_index(outVcf, force=True, preset='vcf')
os.unlink(tmpVcf)
def main():
# Read options, args.
parser = optparse.OptionParser()
parser.add_option('-c', '--chr-col', type='int', dest='chrom_col')
parser.add_option('-s', '--start-col', type='int', dest='start_col')
parser.add_option('-e', '--end-col', type='int', dest='end_col')
parser.add_option('-P', '--preset', dest='preset')
(options, args) = parser.parse_args()
input_fname, output_fname = args
tmpfile = tempfile.NamedTemporaryFile()
sort_params = None
if options.chrom_col and options.start_col and options.end_col:
sort_params = [
"sort",
"-k%(i)s,%(i)s" % {'i': options.chrom_col},
"-k%(i)i,%(i)in" % {'i': options.start_col},
"-k%(i)i,%(i)in" % {'i': options.end_col}
]
elif options.preset == "bed":
sort_params = ["sort", "-k1,1", "-k2,2n", "-k3,3n"]
elif options.preset == "vcf":
sort_params = ["sort", "-k1,1", "-k2,2n"]
elif options.preset == "gff":
sort_params = ["sort", "-s", "-k1,1", "-k4,4n"] # stable sort on start column
# Skip any lines starting with "#" and "track"
grepped = subprocess.Popen(["grep", "-e", "^\"#\"", "-e", "^track", "-v", input_fname], stderr=subprocess.PIPE, stdout=subprocess.PIPE)
after_sort = subprocess.Popen(sort_params, stdin=grepped.stdout, stderr=subprocess.PIPE, stdout=tmpfile)
grepped.stdout.close()
output, err = after_sort.communicate()
pysam.tabix_compress(tmpfile.name, output_fname, force=True)
def main():
# Read options, args.
parser = optparse.OptionParser()
(options, args) = parser.parse_args()
input_fname, output_fname = args
pysam.tabix_compress(input_fname, output_fname, force=True)
def testIndexPresetCompressed(self):
'''test indexing via preset.'''
pysam.tabix_compress(self.tmpfilename, self.tmpfilename + ".gz")
pysam.tabix_index(self.tmpfilename + ".gz", preset=self.preset)
checkBinaryEqual(self.tmpfilename + ".gz", self.filename)
checkBinaryEqual(self.tmpfilename + ".gz.tbi", self.filename_idx)
def make_bias_track(args, bases = 500000, splitsize = 1000):
"""function to compute bias track
"""
if args.out is None:
if args.bed is not None:
args.out = '.'.join(os.path.basename(args.bed).split('.')[0:-1])
else:
args.out = '.'.join(os.path.basename(args.fasta).split('.')[0:-1])
params = _BiasParams(args.fasta, args.pwm)
if args.bed is None:
chunks = ChunkList.convertChromSizes(params.chrs, splitsize = splitsize)
sets = chunks.split(items = bases/splitsize)
else:
chunks = ChunkList.read(args.bed)
chunks.merge()
sets = chunks.split(bases = bases)
maxQueueSize = max(2,int(2 * bases / np.mean([chunk.length() for chunk in chunks])))
pool = mp.Pool(processes = max(1,args.cores-1))
out_handle = open(args.out + '.Scores.bedgraph','w')
out_handle.close()
write_queue = mp.JoinableQueue(maxsize = maxQueueSize)
write_process = mp.Process(target = _writeBias, args=(write_queue, args.out))
write_process.start()
for j in sets:
tmp = pool.map(_biasHelper, zip(j,itertools.repeat(params)))
for track in tmp:
write_queue.put(track)
pool.close()
pool.join()
write_queue.put('STOP')
write_process.join()
pysam.tabix_compress(args.out + '.Scores.bedgraph', args.out + '.Scores.bedgraph.gz', force = True)
shell_command('rm ' + args.out + '.Scores.bedgraph')
pysam.tabix_index(args.out + '.Scores.bedgraph.gz', preset = "bed", force = True)
def ensureIndexed(bedPath, preset="bed", trySorting=True):
if not bedPath.endswith(".gz"):
if not os.path.exists(bedPath + ".gz"):
logging.info("bgzf compressing {}".format(bedPath))
pysam.tabix_compress(bedPath, bedPath + ".gz")
if not os.path.exists(bedPath + ".gz"):
raise Exception(
"Failed to create compress {preset} file for {file}; make sure the {preset} file is "
"sorted and the directory is writeable".format(preset=preset, file=bedPath)
)
bedPath += ".gz"
if not os.path.exists(bedPath + ".tbi"):
logging.info("creating tabix index for {}".format(bedPath))
pysam.tabix_index(bedPath, preset=preset)
if not os.path.exists(bedPath + ".tbi"):
raise Exception(
"Failed to create tabix index file for {file}; make sure the {preset} file is "
"sorted and the directory is writeable".format(preset=preset, file=bedPath)
)
line = pysam.Tabixfile(bedPath).fetch().next()
if len(line.strip().split("\t")) < 6 and preset == "bed":
raise AnnotationError(
"BED files need to have at least 6 (tab-delimited) fields (including "
"chrom, start, end, name, score, strand; score is unused)"
)
if len(line.strip().split("\t")) < 9 and preset == "gff":
raise AnnotationError("GFF/GTF files need to have at least 9 tab-delimited fields")
return bedPath
def get_cov(args, bases = 50000, splitsize = 1000):
"""function to get coverages
"""
if not args.out:
if args.bed is None:
args.out = '.'.join(os.path.basename(args.bam).split('.')[0:-1])
else:
args.out = '.'.join(os.path.basename(args.bed).split('.')[0:-1])
if args.bed is None:
chrs = read_chrom_sizes_from_bam(args.bam)
chunks = ChunkList.convertChromSizes(chrs, splitsize = splitsize)
sets = chunks.split(items = bases/splitsize)
else:
chunks = ChunkList.read(args.bed)
chunks.merge()
sets = chunks.split(bases = bases)
maxQueueSize = max(2,int(2 * bases / np.mean([chunk.length() for chunk in chunks])))
pool1 = mp.Pool(processes = max(1,args.cores-1))
out_handle = open(args.out + '.cov.bedgraph','w')
out_handle.close()
write_queue = mp.JoinableQueue(maxsize = maxQueueSize)
write_process = mp.Process(target = _writeCov, args=(write_queue, args.out))
write_process.start()
for j in sets:
tmp = pool1.map(_covHelper, zip(j,itertools.repeat(args)))
for track in tmp:
write_queue.put(track)
pool1.close()
pool1.join()
write_queue.put('STOP')
write_process.join()
pysam.tabix_compress(args.out + '.cov.bedgraph', args.out + '.cov.bedgraph.gz', force = True)
shell_command('rm ' + args.out + '.cov.bedgraph')
pysam.tabix_index(args.out + '.cov.bedgraph.gz', preset = "bed", force = True)
def eff_vcf(self, inVcf, outVcf, genome, java_flags='-Xmx2g',
in_format='vcf', out_format='vcf', eff_options=''):
"""
TODO: docstring here
"""
if outVcf.endswith('.vcf.gz'):
tmpVcf = util.file.mkstempfname(prefix='vcf_snpEff-', suffix='.vcf')
else:
tmpVcf = outVcf
args = ' '.join([
'eff',
'-c', '{}/snpEff.config'.format(self.executable_path()),
'-i', in_format,
'-o', out_format,
genome,
'-treatAllAsProteinCoding false',
'-noLog',
'-ud 0',
'-noStats',
eff_options
])
if inVcf.endswith('.gz'):
pre_pipe = "zcat {} | ".format(inVcf)
else:
pre_pipe = "cat {} | ".format(inVcf)
post_pipe = " > {}".format(tmpVcf)
self.execute(args, java_flags=java_flags, pre_pipe=pre_pipe,
post_pipe=post_pipe)
if outVcf.endswith('.vcf.gz'):
pysam.tabix_compress(tmpVcf, outVcf, force=True)
pysam.tabix_index(outVcf, force=True, preset='vcf')
os.unlink(tmpVcf)
def annotate_vcf(self, inVcf, genomes, outVcf, emailAddress, JVMmemory=None):
"""
Annotate variants in VCF file with translation consequences using snpEff.
"""
if outVcf.endswith('.vcf.gz'):
tmpVcf = util.file.mkstempfname(prefix='vcf_snpEff-', suffix='.vcf')
elif outVcf.endswith('.vcf'):
tmpVcf = outVcf
else:
raise Exception("invalid input")
sortedAccessionString = ", ".join(sorted(genomes))
databaseId = hashlib.sha256(sortedAccessionString.encode('utf-8')).hexdigest()[:55]
genomeToUse = ""
# if we don't have the genome, by name (snpEff official) or by hash (custom)
if (not self.has_genome(databaseId)):
if (not self.has_genome(genomes[0])):
_log.info("Checking for snpEff database online...")
# check to see if it is available for download, and if so install it
for row in self.available_databases():
if (genomes[0].lower() in row['Genome'].lower()) or (
genomes[0].lower() in row['Bundle'].lower()
) or (
genomes[0].lower() in row['Organism'].lower()
):
self.download_db(row['Genome'])
# backward compatability for where a single genome name is provided
if self.has_genome(genomes[0]):
genomeToUse = genomes[0]
else:
# if the hash of the accessions passed in is not present in the genomes db
if not self.has_genome(databaseId):
self.create_db(genomes, emailAddress, JVMmemory)
if self.has_genome(databaseId):
genomeToUse = databaseId
if not genomeToUse:
raise Exception()
args = [
'-treatAllAsProteinCoding', 'false', '-t', '-noLog', '-ud', '0', '-noStats', '-noShiftHgvs', genomeToUse,
os.path.realpath(inVcf)
]
command_ps = self.execute('ann', args, JVMmemory=JVMmemory)
if command_ps.returncode == 0:
with open(tmpVcf, 'wt') as outf:
outf.write(command_ps.stdout.decode("utf-8"))
if outVcf.endswith('.vcf.gz'):
pysam.tabix_compress(tmpVcf, outVcf, force=True)
pysam.tabix_index(outVcf, force=True, preset='vcf')
os.unlink(tmpVcf)
else:
raise subprocess.CalledProcessError(cmd=command_ps.args, returncode=command_ps.returncode, output=command_ps.stdout)
def annotate_vcf(self, inVcf, genomes, outVcf, emailAddress, JVMmemory=None):
"""
Annotate variants in VCF file with translation consequences using snpEff.
"""
if outVcf.endswith('.vcf.gz'):
tmpVcf = util.file.mkstempfname(prefix='vcf_snpEff-', suffix='.vcf')
elif outVcf.endswith('.vcf'):
tmpVcf = outVcf
else:
raise Exception("invalid input")
sortedAccessionString = ", ".join([util.genbank.parse_accession_str(acc) for acc in sorted(genomes)])
databaseId = hashlib.sha256(sortedAccessionString.encode('utf-8')).hexdigest()[:55]
genomeToUse = ""
# if we don't have the genome, by name (snpEff official) or by hash (custom)
if (not self.has_genome(databaseId)):
if (not self.has_genome(genomes[0])):
_log.info("Checking for snpEff database online...")
# check to see if it is available for download, and if so install it
for row in self.available_databases():
if (genomes[0].lower() in row['Genome'].lower()) or (
genomes[0].lower() in row['Bundle'].lower()
) or (
genomes[0].lower() in row['Organism'].lower()
):
self.download_db(row['Genome'])
# backward compatability for where a single genome name is provided
if self.has_genome(genomes[0]):
genomeToUse = genomes[0]
else:
# if the hash of the accessions passed in is not present in the genomes db
if not self.has_genome(databaseId):
self.create_db(genomes, emailAddress, JVMmemory)
if self.has_genome(databaseId):
genomeToUse = databaseId
if not genomeToUse:
raise Exception()
args = [
'-treatAllAsProteinCoding', 'false', '-t', '-noLog', '-ud', '0', '-noStats', '-noShiftHgvs', genomeToUse,
os.path.realpath(inVcf)
]
command_ps = self.execute('ann', args, JVMmemory=JVMmemory)
if command_ps.returncode == 0:
with open(tmpVcf, 'wt') as outf:
outf.write(command_ps.stdout.decode("utf-8"))
if outVcf.endswith('.vcf.gz'):
pysam.tabix_compress(tmpVcf, outVcf, force=True)
pysam.tabix_index(outVcf, force=True, preset='vcf')
os.unlink(tmpVcf)
else:
raise subprocess.CalledProcessError(cmd=command_ps.args, returncode=command_ps.returncode, output=command_ps.stdout)
def testEmptyFileVCFGZWithoutIndex(self):
with get_temp_context("tmp_testEmptyFileWithoutIndex.vcf") as fn:
with open(fn, "w"):
pass
pysam.tabix_compress(fn, fn + ".gz", force=True)
self.assertRaises(ValueError, pysam.VariantFile, fn + ".gz")
def indexFile(input_file):
sys.stdout.write('Compressing file... ')
sys.stdout.flush()
pysam.tabix_compress(input_file, input_file + '.gz', force=True)
sys.stdout.write('OK\n')
sys.stdout.write('Indexing output file... ')
sys.stdout.flush()
pysam.tabix_index(input_file + '.gz', seq_col=4, start_col=6, end_col=7, meta_char='#', force=True)
sys.stdout.write('OK\n')
def run_nfr(args):
"""run nfr calling
"""
if args.bam is None and args.ins_track is None:
raise Exception("Must supply either bam file or insertion track")
if not args.out:
args.out = '.'.join(os.path.basename(args.calls).split('.')[0:-3])
if args.fasta is not None:
chrs_fasta = read_chrom_sizes_from_fasta(args.fasta)
pwm = PWM.open(args.pwm)
chunks = ChunkList.read(args.bed, chromDict = chrs_fasta, min_offset = max(pwm.up, pwm.down))
else:
chunks = ChunkList.read(args.bed)
if args.bam is not None:
chrs_bam = read_chrom_sizes_from_bam(args.bam)
chunks.checkChroms(chrs_bam, chrom_source = "BAM file")
chunks.merge()
maxQueueSize = args.cores * 10
params = NFRParameters(args.occ_track, args.calls, args.ins_track, args.bam, max_occ = args.max_occ, max_occ_upper = args.max_occ_upper,
fasta = args.fasta, pwm = args.pwm)
sets = chunks.split(items = args.cores * 5)
pool1 = mp.Pool(processes = max(1,args.cores-1))
nfr_handle = open(args.out + '.nfrpos.bed','w')
nfr_handle.close()
nfr_queue = mp.JoinableQueue()
nfr_process = mp.Process(target = _writeNFR, args=(nfr_queue, args.out))
nfr_process.start()
if params.ins_track is None:
ins_handle = open(args.out + '.ins.bedgraph','w')
ins_handle.close()
ins_queue = mp.JoinableQueue()
ins_process = mp.Process(target = _writeIns, args=(ins_queue, args.out))
ins_process.start()
for j in sets:
tmp = pool1.map(_nfrHelper, zip(j,itertools.repeat(params)))
for result in tmp:
if params.ins_track is None:
nfr_queue.put(result[0])
ins_queue.put(result[1])
else:
nfr_queue.put(result)
pool1.close()
pool1.join()
nfr_queue.put('STOP')
nfr_process.join()
if params.ins_track is None:
ins_queue.put('STOP')
ins_process.join()
pysam.tabix_compress(args.out + '.nfrpos.bed', args.out + '.nfrpos.bed.gz',force = True)
shell_command('rm ' + args.out + '.nfrpos.bed')
pysam.tabix_index(args.out + '.nfrpos.bed.gz', preset = "bed", force = True)
if params.ins_track is None:
pysam.tabix_compress(args.out + '.ins.bedgraph', args.out + '.ins.bedgraph.gz', force = True)
shell_command('rm ' + args.out + '.ins.bedgraph')
pysam.tabix_index(args.out + '.ins.bedgraph.gz', preset = "bed", force = True)
def indexFile(f, options):
sys.stdout.write(f'Compressing output file {f}... ')
sys.stdout.flush()
pysam.tabix_compress(os.path.join(options.output_dir, f), os.path.join(options.output_dir, f + '.gz'), force=True)
sys.stdout.write('OK\n')
sys.stdout.write(f'Indexing output file {f}... ')
sys.stdout.flush()
pysam.tabix_index(os.path.join(options.output_dir, f + '.gz'), seq_col=4, start_col=6, end_col=7, meta_char='#',
force=True)
sys.stdout.write('OK\n')
def _index_with_tabix(self):
"""Compress and index output file by Tabix"""
pysam.tabix_compress(self._fn + '_tmp', self._fn + '.gz', force=True)
pysam.tabix_index(self._fn + '.gz',
seq_col=self.idx_chrom,
start_col=self.idx_start,
end_col=self.idx_end,
meta_char='#',
force=True)
def testEmptyFileVCFGZWithoutIndex(self):
with get_temp_context("tmp_testEmptyFileWithoutIndex.vcf") as fn:
with open(fn, "w"):
pass
pysam.tabix_compress(fn,
fn + ".gz",
force=True)
self.assertRaises(ValueError, pysam.VariantFile, fn + ".gz")
def convert_VariantFile_to_IndexedVariantFile(vf_path, ivf_path):
make_basedir(ivf_path)
tmp_path = get_tmp_path(ivf_path)
pysam.tabix_compress(vf_path, tmp_path, force=True)
os.rename(tmp_path, ivf_path)
pysam.tabix_index(
filename=ivf_path, force=True,
seq_col=0, start_col=1, end_col=1, # note: `pysam.tabix_index` calls the first column `0`, but cmdline `tabix` calls it `1`.
line_skip=1, # skip header
)
def convert_VariantFile_to_IndexedVariantFile(vf_path, ivf_path):
make_basedir(ivf_path)
tmp_path = get_tmp_path(ivf_path)
pysam.tabix_compress(vf_path, tmp_path, force=True)
os.rename(tmp_path, ivf_path)
pysam.tabix_index(
filename=ivf_path, force=True,
seq_col=0, start_col=1, end_col=1, # note: `pysam.tabix_index` calls the first column `0`, but cmdline `tabix` calls it `1`.
line_skip=1, # skip header
)
def testEmptyFileVCFGZ(self):
with open("tmp_testEmptyFile.vcf", "w"):
pass
pysam.tabix_compress("tmp_testEmptyFile.vcf",
"tmp_testEmptyFile.vcf.gz")
self.assertRaises(ValueError, pysam.VariantFile,
"tmp_testEmptyFile.vcf.gz")
os.unlink("tmp_testEmptyFile.vcf")
os.unlink("tmp_testEmptyFile.vcf.gz")
def testEmptyFileVCFGZ(self):
with open("tmp_testEmptyFile.vcf", "w"):
pass
pysam.tabix_compress("tmp_testEmptyFile.vcf",
"tmp_testEmptyFile.vcf.gz")
self.assertRaises(ValueError, pysam.VariantFile,
"tmp_testEmptyFile.vcf.gz")
os.unlink("tmp_testEmptyFile.vcf")
os.unlink("tmp_testEmptyFile.vcf.gz")
def bamTobed(bamInput=None, bedOutput=None, compress=True):
# generate temp file for sorting and indexing
bedOutput_path = os.path.realpath(bedOutput)
this_pid = os.getpid()
tmp_split = os.path.splitext(bedOutput_path)
tmp_bedOutput = tmp_split[0] + "-temp-" + str(this_pid) + tmp_split[1]
bai = bamInput + ".bai"
if not os.path.exists(bai):
message = "Index file " + bai + " do not exist!"
raise commonError(message)
bedWrite = open(tmp_bedOutput, "w")
input_file = pysam.Samfile(bamInput, "rb")
chr_reference = input_file.references
for read1, read2 in read_pair_generator(input_file):
read1Start = read1.reference_start
read1End = read1.reference_end
read2Start = read2.reference_start
read2End = read2.reference_end
if not read1.is_reverse: # read1 is forward strand, read2 is reverse strand
rstart = read1Start # 0-based left-most site
rend = read2End
else: # read1 is reverse strand, read2 is forward strand
rstart = read2Start # 0-based left-most site
rend = read1End
if (rstart < 0) or (rend < 0) or (rstart >= rend): continue
tmp_str = chr_reference[read1.tid] + "\t" + str(rstart) + "\t" + str(
rend) + "\n"
bedWrite.write(tmp_str)
bedWrite.close()
print("Fragments generated, waitting for sorting......")
bedData = pybedtools.BedTool(tmp_bedOutput)
bedData.sort(output=bedOutput)
os.remove(tmp_bedOutput)
print("Fragments sorted.")
if compress:
print("Waitting for compressing and indexing......")
bedgzfile = bedOutput + ".gz"
pysam.tabix_compress(bedOutput, bedgzfile, force=False)
pysam.tabix_index(bedgzfile, preset="bed", zerobased=True)
print("Indexing bedgz file finished!")
return True
def run_merge(args):
if not args.out:
args.out = '.'.join(os.path.basename(args.nucpos).split('.')[0:-3])
occ = NucList.read(args.occpeaks, "occ", args.min_occ)
nuc = NucList.read(args.nucpos, "nuc", args.min_occ)
new = merge(occ, nuc, args.sep)
out = open(args.out + '.nucmap_combined.bed','w')
out.write(new.asBed())
out.close()
pysam.tabix_compress(args.out + '.nucmap_combined.bed', args.out + '.nucmap_combined.bed.gz',force = True)
shell_command('rm ' + args.out + '.nucmap_combined.bed')
pysam.tabix_index(args.out + '.nucmap_combined.bed.gz', preset = "bed", force = True)
def run_diff(args, bases=500000):
"""run differential occupancy calling
"""
chrs = read_chrom_sizes_from_bam(args.bam)
pwm = PWM.open(args.pwm)
chunks = ChunkList.read(args.bed,
chromDict=chrs,
min_offset=args.flank + args.upper / 2 +
max(pwm.up, pwm.down))
chunks.merge()
maxQueueSize = max(
2, int(100 * bases / np.mean([chunk.length() for chunk in chunks])))
#get fragmentsizes
fragment_dist1 = FragmentMixDistribution(0, upper=args.upper)
fragment_dist1.fragmentsizes = FragmentSizes(
0, args.upper, vals=FragmentSizes.open(args.sizes1).get(0, args.upper))
fragment_dist1.modelNFR()
fragment_dist2 = FragmentMixDistribution(0, upper=args.upper)
fragment_dist2.fragmentsizes = FragmentSizes(
0, args.upper, vals=FragmentSizes.open(args.sizes2).get(0, args.upper))
fragment_dist2.modelNFR()
params = OccupancyParameters(fragment_dist,
args.upper,
args.fasta,
args.pwm,
sep=args.nuc_sep,
min_occ=args.min_occ,
flank=args.flank,
bam=args.bam,
ci=args.confidence_interval)
sets = chunks.split(bases=bases)
pool1 = mp.Pool(processes=max(1, args.cores - 1))
diff_handle = open(args.out + '.occdiff.bed', 'w')
diff_handle.close()
diff_queue = mp.JoinableQueue()
diff_process = mp.Process(target=_writeDiff, args=(diff_queue, args.out))
diff_process.start()
nuc_dist = np.zeros(args.upper)
for j in sets:
tmp = pool1.map(_occHelper, zip(j, itertools.repeat(params)))
for result in tmp:
diff_queue.put(result[1])
pool1.close()
pool1.join()
diff_queue.put('STOP')
diff_process.join()
pysam.tabix_compress(args.out + '.occdiff.bed',
args.out + '.occdiff.bed.gz',
force=True)
shell_command('rm ' + args.out + '.occdiff.bed')
pysam.tabix_index(args.out + '.occdiff.bed.gz', preset="bed", force=True)
def testEmptyFileVCFGZWithoutIndex(self):
with open("tests/tmp_testEmptyFileWithoutIndex.vcf", "w"):
pass
pysam.tabix_compress("tests/tmp_testEmptyFileWithoutIndex.vcf",
"tests/tmp_testEmptyFileWithoutIndex.vcf.gz",
force=True)
self.assertRaises(ValueError, pysam.VariantFile,
"tests/tmp_testEmptyFileWithoutIndex.vcf.gz")
os.unlink("tests/tmp_testEmptyFileWithoutIndex.vcf")
os.unlink("tests/tmp_testEmptyFileWithoutIndex.vcf.gz")
def bgzip_file(original_file, new_file, delete_original=False, force=True):
"""
:param original_file:
:param new_file:
:param force:
:return:
"""
pysam.tabix_compress(original_file, new_file, force)
if delete_original:
delete_file(original_file)
def testEmptyFileVCFGZWithoutIndex(self):
with open("tmp_testEmptyFileWithoutIndex.vcf", "w"):
pass
pysam.tabix_compress("tmp_testEmptyFileWithoutIndex.vcf",
"tmp_testEmptyFileWithoutIndex.vcf.gz",
force=True)
self.assertRaises(ValueError, pysam.VariantFile,
"tmp_testEmptyFileWithoutIndex.vcf.gz")
os.unlink("tmp_testEmptyFileWithoutIndex.vcf")
os.unlink("tmp_testEmptyFileWithoutIndex.vcf.gz")
def indexFile(options):
sys.stdout.write('Compressing output file ... ')
sys.stdout.flush()
pysam.tabix_compress(options.output, options.output + '.gz', force=True)
sys.stdout.write('OK\n')
sys.stdout.write('Indexing output file ... ')
sys.stdout.flush()
pysam.tabix_index(options.output + '.gz',
seq_col=1,
start_col=2,
end_col=2,
meta_char='#',
force=True)
sys.stdout.write('OK\n')
def run_nfr(args):
"""run nfr calling
"""
if args.bam is None and args.ins_track is None:
raise Exception("Must supply either bam file or insertion track")
if not args.out:
args.out = '.'.join(os.path.basename(args.calls).split('.')[0:-3])
chunks = ChunkList.read(args.bed)
chunks.merge()
maxQueueSize = args.cores * 10
params = NFRParameters(args.occ_track, args.calls, args.ins_track, args.bam, max_occ = args.max_occ, max_occ_upper = args.max_occ_upper,
fasta = args.fasta, pwm = args.pwm)
sets = chunks.split(items = args.cores * 5)
pool1 = mp.Pool(processes = max(1,args.cores-1))
nfr_handle = open(args.out + '.nfrpos.bed','w')
nfr_handle.close()
nfr_queue = mp.JoinableQueue()
nfr_process = mp.Process(target = _writeNFR, args=(nfr_queue, args.out))
nfr_process.start()
if params.ins_track is None:
ins_handle = open(args.out + '.ins.bedgraph','w')
ins_handle.close()
ins_queue = mp.JoinableQueue()
ins_process = mp.Process(target = _writeIns, args=(ins_queue, args.out))
ins_process.start()
for j in sets:
tmp = pool1.map(_nfrHelper, zip(j,itertools.repeat(params)))
for result in tmp:
if params.ins_track is None:
nfr_queue.put(result[0])
ins_queue.put(result[1])
else:
nfr_queue.put(result)
pool1.close()
pool1.join()
nfr_queue.put('STOP')
nfr_process.join()
if params.ins_track is None:
ins_queue.put('STOP')
ins_process.join()
pysam.tabix_compress(args.out + '.nfrpos.bed', args.out + '.nfrpos.bed.gz',force = True)
shell_command('rm ' + args.out + '.nfrpos.bed')
pysam.tabix_index(args.out + '.nfrpos.bed.gz', preset = "bed", force = True)
if params.ins_track is None:
pysam.tabix_compress(args.out + '.ins.bedgraph', args.out + '.ins.bedgraph.gz', force = True)
shell_command('rm ' + args.out + '.ins.bedgraph')
pysam.tabix_index(args.out + '.ins.bedgraph.gz', preset = "bed", force = True)
def run_merge(args):
if not args.out:
args.out = '.'.join(os.path.basename(args.nucpos).split('.')[0:-3])
occ = NucList.read(args.occpeaks, "occ", args.min_occ)
nuc = NucList.read(args.nucpos, "nuc", args.min_occ)
new = merge(occ, nuc, args.sep)
out = open(args.out + '.nucmap_combined.bed', 'w')
out.write(new.asBed())
out.close()
pysam.tabix_compress(args.out + '.nucmap_combined.bed',
args.out + '.nucmap_combined.bed.gz',
force=True)
shell_command('rm ' + args.out + '.nucmap_combined.bed')
pysam.tabix_index(args.out + '.nucmap_combined.bed.gz',
preset="bed",
force=True)
def indexFile(options):
filename=options.output
if not options.ensembl is None:
sys.stdout.write('Compressing output file... ')
sys.stdout.flush()
pysam.tabix_compress(filename,filename+'.gz',force=True)
sys.stdout.write('OK\n')
sys.stdout.write('Indexing output file... ')
sys.stdout.flush()
pysam.tabix_index(filename+'.gz', seq_col=2, start_col=4, end_col=5, meta_char='#',force=True)
sys.stdout.write('OK\n')
else:
print 'Compressing file...'
pysam.tabix_compress(filename,filename+'.gz',force=True)
print 'Indexing file...'
pysam.tabix_index(filename+'.gz', seq_col=1, start_col=2, end_col=2, meta_char='#',force=True)
def indexFile(f):
sys.stdout.write(f'Compressing output file {f}... ')
sys.stdout.flush()
assert os.path.exists(f), f"{f} does not exist"
pysam.tabix_compress(f, f + '.gz', force=True)
sys.stdout.write('OK\n')
if os.path.exists(f):
os.remove(f)
sys.stdout.write(f'Indexing output file {f}.gz... ')
sys.stdout.flush()
pysam.tabix_index(f + '.gz',
seq_col=4,
start_col=6,
end_col=7,
meta_char='#',
force=True)
sys.stdout.write('OK\n')
def bgzip_index(original_file, new_file, file_format):
"""
:param original_file:
:param new_file:
:param file_format:
:return:
"""
if file_format.lower() == 'fa':
tabix_compress(original_file, new_file)
faidx(new_file)
delete_file(original_file)
elif file_format.lower() == 'vcf':
tabix_index(original_file, preset="vcf", force=True)
else:
raise G2GValueError("Unknown file format: {0}".format(file_format))
def bgzip_index(original_file, new_file, file_format):
"""
:param original_file:
:param new_file:
:param file_format:
:return:
"""
if file_format.lower() == 'fa':
tabix_compress(original_file, new_file)
faidx(new_file)
delete_file(original_file)
elif file_format.lower() == 'vcf':
tabix_index(original_file, preset="vcf", force=True)
else:
raise G2GValueError("Unknown file format: {0}".format(file_format))
def main():
# Read options, args.
parser = optparse.OptionParser()
parser.add_option('-c', '--chr-col', type='int', dest='chrom_col')
parser.add_option('-s', '--start-col', type='int', dest='start_col')
parser.add_option('-e', '--end-col', type='int', dest='end_col')
parser.add_option('-P', '--preset', dest='preset')
(options, args) = parser.parse_args()
input_fname, output_fname = args
tmpfile = tempfile.NamedTemporaryFile()
sort_params = None
if options.chrom_col and options.start_col and options.end_col:
sort_params = [
"sort",
"-k%(i)s,%(i)s" % {
'i': options.chrom_col
},
"-k%(i)i,%(i)in" % {
'i': options.start_col
},
"-k%(i)i,%(i)in" % {
'i': options.end_col
}
]
elif options.preset == "bed":
sort_params = ["sort", "-k1,1", "-k2,2n", "-k3,3n"]
elif options.preset == "vcf":
sort_params = ["sort", "-k1,1", "-k2,2n"]
elif options.preset == "gff":
sort_params = ["sort", "-s", "-k1,1",
"-k4,4n"] # stable sort on start column
# Skip any lines starting with "#" and "track"
grepped = subprocess.Popen(
["grep", "-e", "^\"#\"", "-e", "^track", "-v", input_fname],
stderr=subprocess.PIPE,
stdout=subprocess.PIPE)
after_sort = subprocess.Popen(sort_params,
stdin=grepped.stdout,
stderr=subprocess.PIPE,
stdout=tmpfile)
grepped.stdout.close()
output, err = after_sort.communicate()
pysam.tabix_compress(tmpfile.name, output_fname, force=True)
def bgzip(in_fn, remove = True):
"""
convert file to bgzipped format
"""
if is_gz_file(in_fn):
tmp_out_fn = in_fn.replace(".gz", "")
out_fn = in_fn.replace(".gz", ".bgz")
ungzip(in_fn, tmp_out_fn)
else:
tmp_out_fn = in_fn
out_fn = in_fn + ".bgz"
pysam.tabix_compress(tmp_out_fn, out_fn, force = True)
if remove:
os.unlink(tmp_out_fn)
return out_fn
def _make_assembly_vcf(self):
tmp_vcf = self.final_assembly_vcf + '.tmp'
cmd = ' '.join([
self.samtools_exe, 'mpileup',
'-t INFO/DPR,DV',
'-A',
'-f', self.final_assembly_fa,
'-u',
'-v',
self.final_assembly_bam,
'>',
tmp_vcf
])
common.syscall(cmd, verbose=self.verbose)
cmd = ' '.join([
self.bcftools_exe, 'call -m',
tmp_vcf,
'|',
self.bcftools_exe, 'query',
r'''-f '%CHROM\t%POS\t%REF\t%ALT\t%DP\t%DPR]\n' ''',
'>',
self.final_assembly_read_depths + '.tmp'
])
common.syscall(cmd, verbose=self.verbose)
pysam.tabix_compress(self.final_assembly_read_depths + '.tmp', self.final_assembly_read_depths)
pysam.tabix_index(self.final_assembly_read_depths, seq_col=0, start_col=1, end_col=1)
os.unlink(self.final_assembly_read_depths + '.tmp')
cmd = ' '.join([
self.bcftools_exe, 'call -m -v',
tmp_vcf,
'|',
self.bcftools_exe, 'filter',
'-i', '"MIN(DP)>=' + str(self.bcf_min_dp),
' & MIN(DV)>=' + str(self.bcf_min_dv),
' & MIN(DV/DP)>=' + str(self.bcf_min_dv_over_dp),
' & QUAL >=', str(self.bcf_min_qual), '"',
'-o', self.final_assembly_vcf
])
common.syscall(cmd, verbose=self.verbose)
os.unlink(tmp_vcf)
def main():
# Read options, args.
usage = "Usage: %prog [options] tabular_input_file bgzip_output_file"
parser = optparse.OptionParser(usage=usage)
parser.add_option('-c', '--chr-col', type='int', default=0, dest='chrom_col')
parser.add_option('-s', '--start-col', type='int', default=1, dest='start_col')
parser.add_option('-e', '--end-col', type='int', default=1, dest='end_col')
(options, args) = parser.parse_args()
if len(args) != 2:
parser.print_usage()
exit(1)
input_fname, output_fname = args
output_dir = os.path.dirname(output_fname)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
pysam.tabix_compress(input_fname, output_fname, force=True)
# Column indices are 0-based.
pysam.tabix_index(output_fname, seq_col=options.chrom_col, start_col=options.start_col, end_col=options.end_col)
def __init__(self, fileName, samples):
self.samples = samples
self.sampleIndexes = []
self.nColumns = 0
# Compress with bgzip
if not fileName.endswith('.gz'):
if not os.path.isfile(fileName+'.gz'):
pysam.tabix_compress(fileName, fileName+'.gz')
fileName += '.gz'
# Build tabix index
if not os.path.isfile(fileName+'.tbi'):
pysam.tabix_index(fileName, preset='vcf')
nLines = 0
fp = gzip.open(fileName, 'r')
line = fp.readline()
while line:
nLines += 1
if line.startswith('##'):
line = fp.readline()
elif line.startswith('#'): # Header line
break
else:
line = None # Content line, no header line found
else:
raise ValueError("Header not found.")
# Get the column index of selected samples
headers = line[1:].rstrip().split(FS)
self.nColumns = len(headers)
if self.nColumns <= 9:
raise ValueError("Not enough columns in header.")
for name in self.samples:
if name in headers[9:]:
self.sampleIndexes.append(headers.index(name))
else:
raise ValueError("Sample %s not found in header." % name)
self.tabix = pysam.Tabixfile(fileName)
self.chroms = self.tabix.contigs
self.fileName = fileName
def convert_VariantFile_to_IndexedVariantFile(vf_path: str,
ivf_path: str) -> None:
make_basedir(ivf_path)
tmp_path = get_tmp_path(ivf_path)
tmp_path = '{}/cvt-{}'.format(
os.path.dirname(tmp_path), os.path.basename(
tmp_path)) # Avoid using the same tmp path as augment-phenos
pysam.tabix_compress(vf_path, tmp_path, force=True)
os.rename(tmp_path, ivf_path)
pysam.tabix_index(
filename=ivf_path,
force=True,
seq_col=0,
start_col=1,
end_col=
1, # note: `pysam.tabix_index` calls the first column `0`, but cmdline `tabix` calls it `1`.
line_skip=1, # skip header
)
def make_bias_track(args, bases=500000, splitsize=1000):
"""function to compute bias track
"""
if args.out is None:
if args.bed is not None:
args.out = '.'.join(os.path.basename(args.bed).split('.')[0:-1])
else:
args.out = '.'.join(os.path.basename(args.fasta).split('.')[0:-1])
params = _BiasParams(args.fasta, args.pwm)
if args.bed is None:
chunks = ChunkList.convertChromSizes(params.chrs, splitsize=splitsize)
sets = chunks.split(items=bases // splitsize)
else:
chunks = ChunkList.read(args.bed)
chunks.checkChroms(list(params.chrs.keys()))
chunks.merge()
sets = chunks.split(bases=bases)
maxQueueSize = max(
2, int(2 * bases / np.mean([chunk.length() for chunk in chunks])))
pool = mp.Pool(processes=max(1, args.cores - 1))
out_handle = open(args.out + '.Scores.bedgraph', 'w')
out_handle.close()
write_queue = mp.JoinableQueue(maxsize=maxQueueSize)
write_process = mp.Process(target=_writeBias, args=(write_queue, args.out))
write_process.start()
for j in sets:
tmp = pool.map(_biasHelper, list(zip(j, itertools.repeat(params))))
for track in tmp:
write_queue.put(track)
pool.close()
pool.join()
write_queue.put('STOP')
write_process.join()
pysam.tabix_compress(args.out + '.Scores.bedgraph',
args.out + '.Scores.bedgraph.gz',
force=True)
shell_command('rm ' + args.out + '.Scores.bedgraph')
pysam.tabix_index(args.out + '.Scores.bedgraph.gz',
preset="bed",
force=True)
def get_ins(args, bases=50000, splitsize=1000):
"""function to get insertions
"""
if not args.out:
if args.bed is None:
args.out = '.'.join(os.path.basename(args.bam).split('.')[0:-1])
else:
args.out = '.'.join(os.path.basename(args.bed).split('.')[0:-1])
if args.bed is None:
chrs = read_chrom_sizes_from_bam(args.bam)
chunks = ChunkList.convertChromSizes(chrs, splitsize=splitsize)
sets = chunks.split(items=bases / splitsize)
else:
chunks = ChunkList.read(args.bed)
chunks.merge()
sets = chunks.split(bases=bases)
maxQueueSize = max(
2, int(2 * bases / np.mean([chunk.length() for chunk in chunks])))
pool1 = mp.Pool(processes=max(1, args.cores - 1))
out_handle = open(args.out + '.ins.bedgraph', 'w')
out_handle.close()
write_queue = mp.JoinableQueue(maxsize=maxQueueSize)
write_process = mp.Process(target=_writeIns, args=(write_queue, args.out))
write_process.start()
for j in sets:
if args.smooth:
tmp = pool1.map(_insHelperSmooth,
list(zip(j, itertools.repeat(args))))
else:
tmp = pool1.map(_insHelper, list(zip(j, itertools.repeat(args))))
for track in tmp:
write_queue.put(track)
pool1.close()
pool1.join()
write_queue.put('STOP')
write_process.join()
pysam.tabix_compress(args.out + '.ins.bedgraph',
args.out + '.ins.bedgraph.gz',
force=True)
shell_command('rm ' + args.out + '.ins.bedgraph')
pysam.tabix_index(args.out + '.ins.bedgraph.gz', preset="bed", force=True)
def _make_vcf_and_read_depths_files(self):
tmp_vcf = self.vcf_file + '.tmp'
cmd = ' '.join([
self.samtools_exe, 'mpileup',
'-t INFO/AD',
'-A',
'-f', self.ref_fa,
'-u',
'-v',
self.bam,
'>',
tmp_vcf
])
common.syscall(cmd, verbose=True, verbose_filehandle=self.log_fh)
cmd = ' '.join([
self.bcftools_exe, 'call -m',
tmp_vcf,
'|',
self.bcftools_exe, 'query',
r'''-f '%CHROM\t%POS\t%REF\t%ALT\t%DP\t%AD]\n' ''',
'>',
self.read_depths_file + '.tmp'
])
common.syscall(cmd, verbose=True, verbose_filehandle=self.log_fh)
pysam.tabix_compress(self.read_depths_file + '.tmp', self.read_depths_file)
pysam.tabix_index(self.read_depths_file, seq_col=0, start_col=1, end_col=1)
os.unlink(self.read_depths_file + '.tmp')
cmd = ' '.join([
self.bcftools_exe, 'call -m -v',
tmp_vcf,
'|',
self.bcftools_exe, 'filter',
'-i', '"SUM(AD)>=5 & MIN(AD)/DP>=0.1"',
'-o', self.vcf_file
])
common.syscall(cmd, verbose=True, verbose_filehandle=self.log_fh)
os.unlink(tmp_vcf)
def compress_tabix(self, **kwargs):
force = kwargs.get('force', False)
timing = kwargs.get('timing', False)
t1 = time.time()
if self.__filename_compressed_exists and not force:
sys.stderr.write("%s exists\n" % self.filename_compressed)
return False
else:
try:
pysam.tabix_compress(self.filename, self.filename_compressed,
force=force)
self.__filename_compressed_exists = True
t2 = time.time()
if timing:
sys.stderr.write('Total time: %s\n' % (t2 - t1))
return True
except:
sys.stderr.write('Unexpected error during compression: %s\n' %
sys.exc_info()[1])
return False
def __init__(self, fileNames):
"""
Constructor. Takes the name of a vcf file.
"""
for fileName in fileNames:
if ".gz" not in fileName:
try:
pysam.tabix_compress(fileName, fileName + ".gz")
fileName += ".gz"
except IOError:
pass
try:
pysam.tabix_index(fileName, preset="vcf")
except IOError:
pass
self.vcfFiles = [
pysam.Tabixfile(fileName) for fileName in fileNames
]
def bgzip_index(inFile, outFile, params={}, tabixPath=None):
assert not inFile.endswith('.gz') and outFile.endswith('.gz')
log.debug("compressing with bgzip %s -> %s" % (inFile, outFile))
if tabixPath:
cmdline = "%s/bgzip -c %s > %s" % (tabixPath, inFile, outFile)
assert not os.system(cmdline)
else:
pysam.tabix_compress(self.tmpFile, self.outFile, force=True)
log.debug("indexing with tabix: %s" % outFile)
if tabixPath:
cmdline = "%s/tabix %s -f" % (tabixPath, outFile)
if params.get('seq_col') != None:
cmdline += ' -s %d' % params['seq_col']
if params.get('start_col') != None:
cmdline += ' -b %d' % params['start_col']
if params.get('end_col') != None:
cmdline += ' -e %d' % params['end_col']
if params.get('preset') != None:
cmdline += ' -p %s' % params['preset']
if params.get('meta_char') != None:
cmdline += ' -c %s' % params['meta_char']
if params.get('line_skip') != None:
cmdline += ' -S %d' % params['line_skip']
if params.get('zerobased') != None:
cmdline += ' -0'
assert not os.system(cmdline)
else:
assert not params.get(
'line_skip'
), "error: pysam does not seem to support this option, even though their documentation talks about it"
pysam.tabix_index(self.outFile,
force=True,
seq_col=params.get('seq_col'),
start_col=params.get('start_col'),
end_col=params.get('end_col'),
preset=params.get('preset'),
meta_char=params.get('meta_char', '#'),
zerobased=params.get('zerobased', False))
return outFile
def index_gtf(file_path, output_path, sort=True, force=True):
# type: (pathlib.Path, pathlib.Path) -> None
"""Compresses and indexes a gtf file using bgzip and tabix."""
# Sort file before compressing and indexing.
if sort:
sorted_path = _append_suffix(output_path, '.srt')
sort_gtf(file_path, output_path=sorted_path)
else:
sorted_path = file_path
# Gzip and index file.
pysam.tabix_compress(
native_str(sorted_path),
filename_out=native_str(output_path),
force=force)
pysam.tabix_index(native_str(output_path), preset='gff', force=force)
# Clean up sort temp file.
if sort:
sorted_path.unlink()
def compress_depth_file( filename, outfile=None, delete_file=True):
"""compresses a tab file.
Args:
filename (str): file to compress
outfile ( str): name of compressed file, default is [filename].gz
delete_file (bool): delete original file after compression, default is True
Returns:
filename (str): filename of compressed file
"""
if ( outfile is None):
outfile = "{}.gz".format( filename )
pysam.tabix_compress( filename, outfile , force=True )
if ( delete_file ):
os.unlink( filename )
return outfile
def genotype_single_sample(bam, vcf_in, out_dir):
lib_info_json = bam + ".json"
sample = fetchId(bam)
out_vcf = os.path.join(out_dir, sample + ".gt.vcf")
with open(vcf_in, "r") as inf, open(out_vcf, "w") as outf:
single.sso_genotype(bam_string=bam,
vcf_in=inf,
vcf_out=outf,
min_aligned=20,
split_weight=1,
disc_weight=1,
num_samp=1000000,
lib_info_path=lib_info_json,
debug=False,
ref_fasta=None,
sum_quals=False,
max_reads=1000,
cores=None,
batch_size=None)
out_gz = out_vcf + ".gz"
tabix_compress(out_vcf, out_gz, force=True)
tabix_index(out_gz, force=True, preset="vcf")
return out_gz
def output_gff2(gff2_lines, fn):
out = open(fn + '_tmp', 'w')
out.write('##gff-version 2\n')
for c in map(str, range(1, 23)) + ['X', 'Y', 'MT']:
if c not in gff2_lines:
continue
gff2_lines[c] = sorted(gff2_lines[c], key=itemgetter(3, 4))
for x in gff2_lines[c]:
x = map(str, x)
out.write('\t'.join(x) + '\n')
out.close()
pysam.tabix_compress(fn + '_tmp', fn + '.gz', force=True)
pysam.tabix_index(fn + '.gz',
seq_col=0,
start_col=3,
end_col=4,
meta_char='#',
force=True)
os.remove(fn + '_tmp')
def run_diff(args, bases = 500000):
"""run differential occupancy calling
"""
chrs = read_chrom_sizes_from_bam(args.bam)
pwm = PWM.open(args.pwm)
chunks = ChunkList.read(args.bed, chromDict = chrs, min_offset = args.flank + args.upper/2 + max(pwm.up,pwm.down))
chunks.merge()
maxQueueSize = max(2,int(100 * bases / np.mean([chunk.length() for chunk in chunks])))
#get fragmentsizes
fragment_dist1 = FragmentMixDistribution(0, upper = args.upper)
fragment_dist1.fragmentsizes = FragmentSizes(0, args.upper, vals = FragmentSizes.open(args.sizes1).get(0,args.upper))
fragment_dist1.modelNFR()
fragment_dist2 = FragmentMixDistribution(0, upper = args.upper)
fragment_dist2.fragmentsizes = FragmentSizes(0, args.upper, vals = FragmentSizes.open(args.sizes2).get(0,args.upper))
fragment_dist2.modelNFR()
params = OccupancyParameters(fragment_dist, args.upper, args.fasta, args.pwm, sep = args.nuc_sep, min_occ = args.min_occ,
flank = args.flank, bam = args.bam, ci = args.confidence_interval)
sets = chunks.split(bases = bases)
pool1 = mp.Pool(processes = max(1,args.cores-1))
diff_handle = open(args.out + '.occdiff.bed','w')
diff_handle.close()
diff_queue = mp.JoinableQueue()
diff_process = mp.Process(target = _writeDiff, args=(diff_queue, args.out))
diff_process.start()
nuc_dist = np.zeros(args.upper)
for j in sets:
tmp = pool1.map(_occHelper, zip(j,itertools.repeat(params)))
for result in tmp:
diff_queue.put(result[1])
pool1.close()
pool1.join()
diff_queue.put('STOP')
diff_process.join()
pysam.tabix_compress(args.out + '.occdiff.bed', args.out + '.occdiff.bed.gz',force = True)
shell_command('rm ' + args.out + '.occdiff.bed')
pysam.tabix_index(args.out + '.occdiff.bed.gz', preset = "bed", force = True)
def bgzip_index(inFile, outFile, params={}, tabixPath=None):
assert not inFile.endswith('.gz') and outFile.endswith('.gz')
log.debug("compressing with bgzip %s -> %s" % (inFile, outFile))
if tabixPath:
cmdline = "%s/bgzip -c %s > %s" % (tabixPath, inFile, outFile)
assert not os.system(cmdline)
else:
pysam.tabix_compress(self.tmpFile, self.outFile, force=True)
log.debug("indexing with tabix: %s" % outFile)
if tabixPath:
cmdline = "%s/tabix %s -f" % (tabixPath, outFile)
if params.get('seq_col')!=None:
cmdline += ' -s %d' % params['seq_col']
if params.get('start_col')!=None:
cmdline += ' -b %d' % params['start_col']
if params.get('end_col')!=None:
cmdline += ' -e %d' % params['end_col']
if params.get('preset')!=None:
cmdline += ' -p %s' % params['preset']
if params.get('meta_char')!=None:
cmdline += ' -c %s' % params['meta_char']
if params.get('line_skip')!=None:
cmdline += ' -S %d' % params['line_skip']
if params.get('zerobased')!=None:
cmdline += ' -0'
assert not os.system(cmdline)
else:
assert not params.get('line_skip'), "error: pysam does not seem to support this option, even though their documentation talks about it"
pysam.tabix_index(self.outFile, force=True,
seq_col=params.get('seq_col'),
start_col=params.get('start_col'), end_col=params.get('end_col'),
preset=params.get('preset'), meta_char=params.get('meta_char','#'),
zerobased=params.get('zerobased',False))
return outFile
def _make_vcf_and_read_depths_files(self):
if not os.path.exists(self.ref_fa + '.fai'):
pysam.faidx(self.ref_fa)
tmp_vcf = self.vcf_file + '.tmp'
with open(tmp_vcf, 'w') as f:
print(pysam.mpileup(
'-t',
'INFO/AD,INFO/ADF,INFO/ADR',
'-L',
'99999999',
'-A',
'-f',
self.ref_fa,
'-u',
'-v',
self.bam,
),
end='',
file=f)
got = vcfcall_ariba.vcfcall_ariba(tmp_vcf, self.outprefix,
self.min_var_read_depth,
self.min_second_var_read_depth,
self.max_allele_freq)
if got != 0:
raise Error('Error parsing vcf file. Cannot contine')
pysam.tabix_compress(self.outprefix + '.read_depths',
self.read_depths_file)
pysam.tabix_index(self.read_depths_file,
seq_col=0,
start_col=1,
end_col=1)
os.unlink(self.outprefix + '.read_depths')
os.unlink(tmp_vcf)
def run_occ(args):
"""run occupancy calling
"""
if args.fasta:
chrs = read_chrom_sizes_from_fasta(args.fasta)
else:
chrs = read_chrom_sizes_from_bam(args.bam)
pwm = PWM.open(args.pwm)
chunks = ChunkList.read(args.bed, chromDict = chrs, min_offset = args.flank + args.upper/2 + max(pwm.up,pwm.down) + args.nuc_sep/2)
chunks.slop(chrs, up = args.nuc_sep/2, down = args.nuc_sep/2)
chunks.merge()
maxQueueSize = args.cores*10
fragment_dist = FragmentMixDistribution(0, upper = args.upper)
if args.sizes is not None:
tmp = FragmentSizes.open(args.sizes)
fragment_dist.fragmentsizes = FragmentSizes(0, args.upper, vals = tmp.get(0,args.upper))
else:
fragment_dist.getFragmentSizes(args.bam, chunks)
fragment_dist.modelNFR()
fragment_dist.plotFits(args.out + '.occ_fit.eps')
fragment_dist.fragmentsizes.save(args.out + '.fragmentsizes.txt')
params = OccupancyParameters(fragment_dist, args.upper, args.fasta, args.pwm, sep = args.nuc_sep, min_occ = args.min_occ,
flank = args.flank, bam = args.bam, ci = args.confidence_interval, step = args.step)
sets = chunks.split(items = args.cores * 5)
pool1 = mp.Pool(processes = max(1,args.cores-1))
out_handle1 = open(args.out + '.occ.bedgraph','w')
out_handle1.close()
out_handle2 = open(args.out + '.occ.lower_bound.bedgraph','w')
out_handle2.close()
out_handle3 = open(args.out + '.occ.upper_bound.bedgraph','w')
out_handle3.close()
write_queue = mp.JoinableQueue(maxsize = maxQueueSize)
write_process = mp.Process(target = _writeOcc, args=(write_queue, args.out))
write_process.start()
peaks_handle = open(args.out + '.occpeaks.bed','w')
peaks_handle.close()
peaks_queue = mp.JoinableQueue()
peaks_process = mp.Process(target = _writePeaks, args=(peaks_queue, args.out))
peaks_process.start()
nuc_dist = np.zeros(args.upper)
for j in sets:
tmp = pool1.map(_occHelper, zip(j,itertools.repeat(params)))
for result in tmp:
nuc_dist += result[0]
write_queue.put(result[1])
peaks_queue.put(result[2])
pool1.close()
pool1.join()
write_queue.put('STOP')
peaks_queue.put('STOP')
write_process.join()
peaks_process.join()
pysam.tabix_compress(args.out + '.occpeaks.bed', args.out + '.occpeaks.bed.gz',force = True)
shell_command('rm ' + args.out + '.occpeaks.bed')
pysam.tabix_index(args.out + '.occpeaks.bed.gz', preset = "bed", force = True)
for i in ('occ','occ.lower_bound','occ.upper_bound'):
pysam.tabix_compress(args.out + '.' + i + '.bedgraph', args.out + '.'+i+'.bedgraph.gz',force = True)
shell_command('rm ' + args.out + '.' + i + '.bedgraph')
pysam.tabix_index(args.out + '.' + i + '.bedgraph.gz', preset = "bed", force = True)
dist_out = FragmentSizes(0, args.upper, vals = nuc_dist)
dist_out.save(args.out + '.nuc_dist.txt')
print "Making figure"
#make figure
fig = plt.figure()
plt.plot(range(0,args.upper),dist_out.get(0,args.upper),label = "Nucleosome Distribution")
plt.xlabel("Fragment Size")
plt.ylabel("Frequency")
fig.savefig(args.out+'.nuc_dist.eps')
plt.close(fig)
def run(self):
if not os.path.exists(self.outputDirectory):
os.makedirs(self.outputDirectory)
# Clean out, make and re-populate references directory
# For now, assume a single, statically-named referenceSet
print("Converting references...", file=sys.stderr)
shutil.rmtree(self.refsetsDirectory, ignore_errors=True)
os.makedirs(self.refsetsDirectory)
shutil.copy(
os.path.join(self.inputDirectory, "referenceset_hg37.json"),
os.path.join(self.refsetsDirectory, "hg37.json"))
os.makedirs(self.hg37Directory)
for refFile in self.referenceFiles:
refBase = os.path.splitext(refFile)[0]
destFastaFilename = os.path.join(
self.hg37Directory, refBase) + ".fa"
shutil.copy(os.path.join(self.inputDirectory, refBase) + ".fa",
destFastaFilename)
pysam.tabix_compress(destFastaFilename, destFastaFilename + ".gz")
refFasta = pysam.FastaFile(destFastaFilename + ".gz")
refFasta.close()
os.remove(destFastaFilename)
shutil.copy(
os.path.join(self.inputDirectory, refBase) + ".json",
os.path.join(self.hg37Directory, refBase) + ".json")
# Clean out, make and repopulate dataset directories
shutil.rmtree(self.datasetsDirectory, ignore_errors=True)
os.makedirs(self.datasetsDirectory)
for ds in self.datasets:
dsdir = os.path.join(self.datasetsDirectory, ds)
os.makedirs(dsdir)
# Reads
print("Converting reads...", file=sys.stderr)
dsReadsdir = os.path.join(dsdir, "reads")
os.makedirs(dsReadsdir)
for readFile in self.datasetReads[ds]:
destFile = os.path.join(
dsReadsdir,
readFile.split('_')[1].split('.')[0]) + ".bam"
readSrc = pysam.AlignmentFile(
os.path.join(self.inputDirectory, readFile), "r")
readDest = pysam.AlignmentFile(destFile, "wb",
header=readSrc.header)
destFilePath = readDest.filename
for readData in readSrc:
readDest.write(readData)
readDest.close()
readSrc.close()
pysam.index(destFilePath)
# Variants
print("Converting variants...", file=sys.stderr)
dsVariantsdir = os.path.join(dsdir, "variants")
os.makedirs(dsVariantsdir)
for vgroup in self.datasetVariants[ds].keys():
vgroupdir = os.path.join(dsVariantsdir, vgroup)
os.makedirs(vgroupdir)
for variantFile in self.datasetVariants[ds][vgroup]:
destFile = os.path.join(
vgroupdir, variantFile.split('_')[2])
shutil.copy(
os.path.join(
self.inputDirectory, variantFile), destFile)
# Pysam's tabix_index automatically compresses the file
# in place, creates a tabix index.
pysam.tabix_index(destFile, preset="vcf")
print("done converting compliance data.", file=sys.stderr)
def run(self):
if not os.path.exists(self.outputDirectory):
os.makedirs(self.outputDirectory)
self.repo.open("w")
self.repo.initialise()
referenceFileName = "ref_brca1.fa"
inputRef = os.path.join(
self.inputDirectory, referenceFileName)
outputRef = os.path.join(
self.outputDirectory, referenceFileName)
shutil.copy(inputRef, outputRef)
fastaFilePath = os.path.join(
self.outputDirectory,
referenceFileName + '.gz')
pysam.tabix_compress(
outputRef, fastaFilePath)
with open(
os.path.join(
self.inputDirectory, "ref_brca1.json")) as refMetadataFile:
refMetadata = json.load(refMetadataFile)
with open(
os.path.join(
self.inputDirectory,
"referenceset_hg37.json")) as refMetadataFile:
refSetMetadata = json.load(refMetadataFile)
referenceSet = references.HtslibReferenceSet(
refSetMetadata['assemblyId'])
referenceSet.populateFromFile(fastaFilePath)
referenceSet.setAssemblyId(refSetMetadata['assemblyId'])
referenceSet.setDescription(refSetMetadata['description'])
referenceSet.setNcbiTaxonId(refSetMetadata['ncbiTaxonId'])
referenceSet.setIsDerived(refSetMetadata['isDerived'])
referenceSet.setSourceUri(refSetMetadata['sourceUri'])
referenceSet.setSourceAccessions(refSetMetadata['sourceAccessions'])
for reference in referenceSet.getReferences():
reference.setNcbiTaxonId(refMetadata['ncbiTaxonId'])
reference.setSourceAccessions(
refMetadata['sourceAccessions'])
self.repo.insertReferenceSet(referenceSet)
dataset = datasets.Dataset("brca1")
self.repo.insertDataset(dataset)
hg00096Individual = biodata.Individual(dataset, "HG00096")
with open(
os.path.join(
self.inputDirectory,
"individual_HG00096.json")) as jsonString:
hg00096Individual.populateFromJson(jsonString.read())
self.repo.insertIndividual(hg00096Individual)
hg00096BioSample = biodata.BioSample(dataset, "HG00096")
with open(
os.path.join(
self.inputDirectory,
"bioSample_HG00096.json")) as jsonString:
hg00096BioSample.populateFromJson(jsonString.read())
hg00096BioSample.setIndividualId(hg00096Individual.getId())
self.repo.insertBioSample(hg00096BioSample)
hg00099Individual = biodata.Individual(dataset, "HG00099")
with open(
os.path.join(
self.inputDirectory,
"individual_HG00099.json")) as jsonString:
hg00099Individual.populateFromJson(jsonString.read())
self.repo.insertIndividual(hg00099Individual)
hg00099BioSample = biodata.BioSample(dataset, "HG00099")
with open(
os.path.join(
self.inputDirectory,
"bioSample_HG00099.json")) as jsonString:
hg00099BioSample.populateFromJson(jsonString.read())
hg00099BioSample.setIndividualId(hg00099Individual.getId())
self.repo.insertBioSample(hg00099BioSample)
hg00101Individual = biodata.Individual(dataset, "HG00101")
with open(
os.path.join(
self.inputDirectory,
"individual_HG00101.json")) as jsonString:
hg00101Individual.populateFromJson(jsonString.read())
self.repo.insertIndividual(hg00101Individual)
hg00101BioSample = biodata.BioSample(dataset, "HG00101")
with open(
os.path.join(
self.inputDirectory,
"bioSample_HG00101.json")) as jsonString:
hg00101BioSample.populateFromJson(jsonString.read())
hg00101BioSample.setIndividualId(hg00101Individual.getId())
self.repo.insertBioSample(hg00101BioSample)
readFiles = [
"brca1_HG00096.sam",
"brca1_HG00099.sam",
"brca1_HG00101.sam"]
for readFile in readFiles:
name = readFile.split('_')[1].split('.')[0]
readSrc = pysam.AlignmentFile(
os.path.join(self.inputDirectory, readFile), "r")
readDest = pysam.AlignmentFile(
os.path.join(
self.outputDirectory,
name + ".bam"),
"wb", header=readSrc.header)
destFilePath = readDest.filename
for readData in readSrc:
readDest.write(readData)
readDest.close()
readSrc.close()
pysam.index(destFilePath)
readGroupSet = reads.HtslibReadGroupSet(dataset, name)
readGroupSet.populateFromFile(destFilePath, destFilePath + ".bai")
readGroupSet.setReferenceSet(referenceSet)
bioSamples = [hg00096BioSample, hg00099BioSample, hg00101BioSample]
for readGroup in readGroupSet.getReadGroups():
for bioSample in bioSamples:
if bioSample.getLocalId() == readGroup.getSampleName():
readGroup.setBioSampleId(bioSample.getId())
self.repo.insertReadGroupSet(readGroupSet)
ontologyMapFileName = "so-xp-simple.obo"
inputOntologyMap = os.path.join(
self.inputDirectory, ontologyMapFileName)
outputOntologyMap = os.path.join(
self.outputDirectory, ontologyMapFileName)
shutil.copy(inputOntologyMap, outputOntologyMap)
sequenceOntology = ontologies.Ontology("so-xp-simple")
sequenceOntology.populateFromFile(outputOntologyMap)
sequenceOntology._id = "so-xp-simple"
self.repo.insertOntology(sequenceOntology)
self.repo.addOntology(sequenceOntology)
vcfFiles = [
"brca1_1kgPhase3_variants.vcf",
"brca1_WASH7P_annotation.vcf",
"brca1_OR4F_annotation.vcf"]
for vcfFile in vcfFiles:
self.addVariantSet(
vcfFile,
dataset,
referenceSet,
sequenceOntology,
bioSamples)
seqAnnFile = "brca1_gencodev19.gff3"
seqAnnSrc = os.path.join(self.inputDirectory, seqAnnFile)
seqAnnDest = os.path.join(self.outputDirectory, "gencodev19.db")
dbgen = generate_gff3_db.Gff32Db(seqAnnSrc, seqAnnDest)
dbgen.run()
gencode = sequenceAnnotations.Gff3DbFeatureSet(dataset, "gencodev19")
gencode.setOntology(sequenceOntology)
gencode.populateFromFile(seqAnnDest)
gencode.setReferenceSet(referenceSet)
self.repo.insertFeatureSet(gencode)
self.repo.commit()
print("Done converting compliance data.", file=sys.stderr)
def run_nuc(args):
"""run occupancy calling
"""
vmat = VMat.open(args.vmat)
if args.fasta:
chrs = read_chrom_sizes_from_fasta(args.fasta)
else:
chrs = read_chrom_sizes_from_bam(args.bam)
pwm = PWM.open(args.pwm)
chunks = ChunkList.read(args.bed,
chromDict=chrs,
min_offset=vmat.mat.shape[1] + vmat.upper // 2 +
max(pwm.up, pwm.down) + args.nuc_sep // 2,
min_length=args.nuc_sep * 2)
chunks.slop(chrs, up=args.nuc_sep // 2, down=args.nuc_sep // 2)
chunks.merge()
maxQueueSize = args.cores * 10
if args.sizes is not None:
fragment_dist = FragmentSizes.open(args.sizes)
else:
fragment_dist = FragmentSizes(0, upper=vmat.upper)
fragment_dist.calculateSizes(args.bam, chunks)
params = NucParameters(vmat=vmat,
fragmentsizes=fragment_dist,
bam=args.bam,
fasta=args.fasta,
pwm=args.pwm,
occ_track=args.occ_track,
sd=args.sd,
nonredundant_sep=args.nuc_sep,
redundant_sep=args.redundant_sep,
min_z=args.min_z,
min_lr=args.min_lr,
atac=args.atac)
sets = chunks.split(items=args.cores * 5)
pool1 = mp.Pool(processes=max(1, args.cores - 1))
if args.write_all:
outputs = [
'nucpos', 'nucpos.redundant', 'nucleoatac_signal',
'nucleoatac_signal.smooth', 'nucleoatac_background',
'nucleoatac_raw'
]
else:
outputs = [
'nucpos', 'nucpos.redundant', 'nucleoatac_signal',
'nucleoatac_signal.smooth'
]
handles = {}
write_queues = {}
write_processes = {}
for i in outputs:
if i not in ['nucpos', 'nucpos.redundant', 'nfrpos']:
handles[i] = open(args.out + '.' + i + '.bedgraph', 'w')
else:
handles[i] = open(args.out + '.' + i + '.bed', 'w')
handles[i].close()
write_queues[i] = mp.JoinableQueue(maxsize=maxQueueSize)
write_processes[i] = mp.Process(target=_writeFuncs[i],
args=(write_queues[i], args.out))
write_processes[i].start()
for j in sets:
tmp = pool1.map(_nucHelper, list(zip(j, itertools.repeat(params))))
for result in tmp:
for i in outputs:
write_queues[i].put(result[i])
pool1.close()
pool1.join()
for i in outputs:
write_queues[i].put('STOP')
for i in outputs:
write_processes[i].join()
if i not in ['nucpos', 'nucpos.redundant']:
pysam.tabix_compress(args.out + '.' + i + '.bedgraph',
args.out + '.' + i + '.bedgraph.gz',
force=True)
shell_command('rm ' + args.out + '.' + i + '.bedgraph')
pysam.tabix_index(args.out + '.' + i + '.bedgraph.gz',
preset="bed",
force=True)
else:
pysam.tabix_compress(args.out + '.' + i + '.bed',
args.out + '.' + i + '.bed.gz',
force=True)
shell_command('rm ' + args.out + '.' + i + '.bed')
pysam.tabix_index(args.out + '.' + i + '.bed.gz',
preset="bed",
force=True)
def testCompression(self):
'''see also issue 106'''
pysam.tabix_compress(self.tmpfilename, self.tmpfilename + ".gz")
checkBinaryEqual(self.tmpfilename, self.tmpfilename + ".gz")
