def get_raw_signal(arguments):
(mpbs_region, reads_file, organism, window_size, forward_shift,
reverse_shift) = arguments
bam = Samfile(reads_file, "rb")
signal = np.zeros(window_size)
for region in mpbs_region:
mid = (region.final + region.initial) // 2
p1 = mid - window_size // 2
p2 = mid + window_size // 2
if p1 <= 0:
continue
# Fetch raw signal
for read in bam.fetch(region.chrom, p1, p2):
# check if the read is unmapped, according to issue #112
if read.is_unmapped:
continue
if not read.is_reverse:
cut_site = read.pos + forward_shift
if p1 <= cut_site < p2:
signal[cut_site - p1] += 1.0
else:
cut_site = read.aend + reverse_shift - 1
if p1 <= cut_site < p2:
signal[cut_site - p1] += 1.0
return signal
def callbase(bamfile, snpsites, out):
BF = Samfile(bamfile, 'rb') #open your bam file
SF = open(snpsites, 'r') #the file contain snp sites info
RF = open(out, 'w') #resulte file
RF.write('ref_name\tpos\tRbase\tAbase\tA\tT\tC\tG\tN\tothers\n')
for i in SF:
if i.startswith('#'):
continue
else:
line = ParseSNPsitesLine(i)
vcf_pos = line.pos-1 #change 1-base to 0-based
vcf_refname = line.chrom
print 'processing: %s %s...'%(vcf_refname, str(vcf_pos))
At, Tt, Ct, Gt, Nt, othert = 0, 0, 0, 0, 0, 0
for i in BF.pileup(vcf_refname, vcf_pos, vcf_pos+1):
if i.pos == vcf_pos:
vcf_Rbase = line.Rbase
vcf_Abase = line.Abase
for j in i.pileups:
yourbase = j.alignment.seq[j.qpos]
if yourbase == 'A': At += 1
elif yourbase == 'T': Tt += 1
elif yourbase == 'C': Ct += 1
elif yourbase == 'G': Gt += 1
elif yourbase == 'N': Nt += 1
else: othert += 1
RF.write('%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\n'%(vcf_refname, \
str(vcf_pos+1), vcf_Rbase, vcf_Abase, str(At), str(Tt), str(Ct), str(Gt), \
str(Nt), str(othert)))
BF.close()
def single_end_sam_parsing(sam_list, cov, identity_threshold):
match = {}
to_process = []
if sam_list[0] is None:
print "The ene-to-end mapping of SE data produced an error."
else:
to_process.append(sam_list[0])
if sam_list[1] is None:
print "The local mapping mode of SE data produced an error."
else:
to_process.append(sam_list[1])
for single_sam in to_process:
sam = Samfile(single_sam)
for align in sam:
if align.tid != -1:
query_name, query_len, ref_name = align.qname, float(
align.rlen), sam.getrname(align.tid)
if align.cigar is not None:
align_len, query_aligned_len = cigar_parsing(align.cigar)
nm = -1
if (query_aligned_len / query_len) * 100 >= cov:
for coppia in align.tags:
if coppia[0] == "NM":
nm = float(coppia[1])
if align_len != 0 and nm >= 0:
paired_perc_id = ((align_len - nm) / align_len) * 100
if paired_perc_id >= identity_threshold:
match.setdefault(query_name, set())
match[query_name].add(ref_name)
sam.close()
return match
def __init__(self, file_name):
"""
Initializes GenomicSignal.
"""
self.file_name = file_name
self.sg_coefs = None
self.bam = Samfile(file_name, "rb")
def test_process_bam_mismatches():
tbam = os.path.join(DATA, "tmp.bam")
bam = os.path.join(DATA, "ordered_umi.bam")
if os.path.exists(tbam):
os.remove(tbam)
with captured_output() as (out, err):
process_bam(bam, tbam, mismatches=1)
assert os.path.exists(tbam)
it = iter(out.getvalue().split("\n"))
assert it.next().strip() == "1\t9\t10\t4\t2"
assert it.next().strip() == "1\t11\t12\t2\t1"
assert it.next().strip() == "1\t29\t30\t2\t1"
bam_reader = Samfile(tbam)
it = iter(bam_reader)
r = it.next()
assert r.pos == 4
assert r.qname == "read8:UMI_ATTCAGGG"
r = it.next()
assert r.pos == 9
assert r.qname == "read1:UMI_AAAAAGGG"
r = it.next()
assert r.pos == 9
assert r.qname == "read4:UMI_AAAGGGGG"
r = it.next()
assert r.pos == 11
assert r.qname == "read5:UMI_ATTTAGGG"
bam_reader.close()
os.remove(tbam)
def test_against_fixtures():
# load fixtures from numpy array
bampath = "fixture/test.bam"
fastapath = "fixture/ref.fa"
archive = "fixture/regression.npz"
testset = np.load(archive)
for q in stats_types:
if q in stats_types_withref:
x = getattr(pysamstats, "load_" + q)(Samfile(bampath),
fafile=fastapath)
else:
x = getattr(pysamstats, "load_" + q)(Samfile(bampath))
# loop through all fields
for key in testset[q].dtype.names:
expect = testset[q][key]
actual = x[key]
try:
np.testing.assert_array_equal(expect, actual, err_msg=key)
except AssertionError:
print(expect[expect != actual])
print(actual[expect != actual])
raise
def test_pileup_pad():
kwargs_nopad = {'chrom': 'Pf3D7_01_v3',
'start': 0,
'end': 20000,
'one_based': False,
'pad': False}
kwargs_pad = {'chrom': 'Pf3D7_01_v3',
'start': 0,
'end': 20000,
'one_based': False,
'pad': True}
for f, needs_ref in pileup_functions:
debug(f.__name__)
# test no pad
if needs_ref:
a = f(Samfile('fixture/test.bam'), Fastafile('fixture/ref.fa'),
**kwargs_nopad)
else:
a = f(Samfile('fixture/test.bam'), **kwargs_nopad)
eq_(924, a['pos'][0])
eq_(9935, a['pos'][-1])
# test pad
if needs_ref:
a = f(Samfile('fixture/test.bam'), Fastafile('fixture/ref.fa'),
**kwargs_pad)
else:
a = f(Samfile('fixture/test.bam'), **kwargs_pad)
eq_(0, a['pos'][0])
eq_(19999, a['pos'][-1])
assert np.all(np.diff(a['pos']) == 1)
def test_pileup_truncate():
kwargs_notrunc = {'chrom': 'Pf3D7_01_v3',
'start': 2000,
'end': 2100,
'one_based': False,
'truncate': False}
kwargs_trunc = {'chrom': 'Pf3D7_01_v3',
'start': 2000,
'end': 2100,
'one_based': False,
'truncate': True}
for f, needs_ref in pileup_functions:
debug(f.__name__)
# test no truncate
if needs_ref:
a = f(Samfile('fixture/test.bam'), Fastafile('fixture/ref.fa'),
**kwargs_notrunc)
else:
a = f(Samfile('fixture/test.bam'), **kwargs_notrunc)
debug(a[:5])
eq_(1952, a['pos'][0])
eq_(2154, a['pos'][-1])
# test truncate
if needs_ref:
a = f(Samfile('fixture/test.bam'), Fastafile('fixture/ref.fa'),
**kwargs_trunc)
else:
a = f(Samfile('fixture/test.bam'), **kwargs_trunc)
eq_(2000, a['pos'][0])
eq_(2099, a['pos'][-1])
def test_process_bam_mismatches():
tbam = os.path.join(DATA, "tmp.bam")
bam = os.path.join(DATA, "ordered_umi.bam")
if os.path.exists(tbam):
os.remove(tbam)
with captured_output() as (out, err):
process_bam(bam, tbam, mismatches=1)
assert os.path.exists(tbam)
it = iter(out.getvalue().split("\n"))
assert it.next().strip() == "1\t9\t10\t4\t2"
assert it.next().strip() == "1\t11\t12\t2\t1"
assert it.next().strip() == "1\t29\t30\t2\t1"
bam_reader = Samfile(tbam)
it = iter(bam_reader)
r = it.next()
assert r.pos == 4
assert r.qname == "read8:UMI_ATTCAGGG"
r = it.next()
assert r.pos == 9
assert r.qname == "read1:UMI_AAAAAGGG"
r = it.next()
assert r.pos == 9
assert r.qname == "read4:UMI_AAAGGGGG"
r = it.next()
assert r.pos == 11
assert r.qname == "read5:UMI_ATTTAGGG"
bam_reader.close()
os.remove(tbam)
def parse_bam_differential(afn, bfn, regs, step):
"""(internal) Parses bam file in absolute mode. Proceeds by counting reads mapping
onto a segment (chr, start, end). No normalization is done at this step.
"""
abam = Samfile(str(afn), "rb")
bbam = Samfile(str(bfn), "rb")
acount = []
bcount = []
oldchr = "chr1"
for reg in regs:
chr, start, end = reg[:3]
if chr != oldchr:
log("files: %s - %s : %s counted" % (afn, bfn, oldchr))
oldchr = chr
# this could be improved
for s in xrange(start, end, step):
e = s + step
an = abam.count(chr, s, e)
bn = bbam.count(chr, s, e)
acount.append(an)
bcount.append(bn)
acount.append(-1)
bcount.append(-1)
log("files: %s - %s : %s counted (finished)" % (afn, bfn, oldchr))
return acount, bcount
def test_binned_pad_wg():
expected = stat_coverage_binned_refimpl(
Samfile('fixture/test.bam'),
Fastafile('fixture/ref.fa'))
actual = pysamstats.stat_coverage_binned(Samfile('fixture/test.bam'),
Fastafile('fixture/ref.fa'))
compare_iterators(expected, actual)
kwargs = {'window_size': 200,
'window_offset': 100}
for f, needs_ref in binned_functions:
debug(f.__name__)
if needs_ref:
a = f(Samfile('fixture/test.bam'), Fastafile('fixture/ref.fa'),
**kwargs)
else:
a = f(Samfile('fixture/test.bam'), **kwargs)
assert sorted(set(a['chrom'])) == [b'Pf3D7_01_v3', b'Pf3D7_02_v3',
b'Pf3D7_03_v3']
eq_(100, a[a['chrom'] == b'Pf3D7_01_v3']['pos'][0])
eq_(50100, a[a['chrom'] == b'Pf3D7_01_v3']['pos'][-1])
eq_(100, a[a['chrom'] == b'Pf3D7_02_v3']['pos'][0])
eq_(60100, a[a['chrom'] == b'Pf3D7_02_v3']['pos'][-1])
eq_(100, a[a['chrom'] == b'Pf3D7_03_v3']['pos'][0])
eq_(70100, a[a['chrom'] == b'Pf3D7_03_v3']['pos'][-1])
def bam_fill_seq(args):
""" Fill empty sequence with known seqs
"""
if not args.source_bam:
source_bam = args.bam
else:
source_bam = args.source_bam
logging.info('Loading samfile: %s', source_bam)
src_seqs = {1: {}, 2: {}}
src = pysam.Samfile(source_bam)
with src:
for rec in src:
if rec.is_supplementary: # skip supplementary alignment
continue
if rec.is_secondary: # skip supplementary alignment
continue
if rec.query_sequence is None: # empty
continue
if rec.is_read2:
src_seqs[2][rec.qname] = (rec.query_sequence,
rec.query_qualities, rec.is_reverse)
else:
src_seqs[1][rec.qname] = (rec.query_sequence,
rec.query_qualities, rec.is_reverse)
logging.info('Loaded read1 : %s', len(src_seqs[1]))
logging.info('Loaded read2 : %s', len(src_seqs[2]))
sam = Samfile(args.bam)
if args.output.endswith('.bam'):
mode = 'wb'
else:
mode = 'wh'
out = pysam.Samfile(args.output, mode=mode, template=sam)
if args.region:
it = sam.fetch(region=args.region)
else:
it = sam
for rec in it:
qname = rec.qname
if rec.query_sequence is None: # only fill when empty
ret = src_seqs[2 if rec.is_read2 else 1].get(rec.qname)
if ret is not None:
seq, qual, is_rev = ret
if is_rev != rec.is_reverse:
seq = dna_revcomp(seq)
if qual is not None:
qual = list(reversed(qual))
cigar = Cigar(rec.cigartuples)
seq = cigar.hard_clip_seq(seq)
if qual is not None:
qual = cigar.hard_clip_seq(qual)
rec.query_sequence = seq # refill
rec.query_qualities = qual
out.write(rec)
def bam_read_id(url):
'''Read first read id out of a remote bam file.
Note: requires a patched version of pysam
'''
stream = Samfile(url, 'rb')
read = stream.next()
return read.qname
def process_bam(abam, bbam, mismatches=0):
"""Removes duplicate reads characterized by their UMI at any given start location.
Args:
abam (str): Input bam with potential duplicate UMIs
bbam (str): Output bam after removing duplicate UMIs
mismatches (Optional[int]): Allowable edit distance between UMIs
"""
is_indexed(abam)
with Samfile(abam, 'rb') as in_bam, Samfile(bbam, 'wb', template=in_bam) as out_bam:
for chrom in in_bam.references:
print("processing chromosome", chrom, file=sys.stderr)
umi_idx = defaultdict(set)
read_counts = Counter()
for read in in_bam.fetch(chrom):
if read.is_unmapped:
continue
# get the iupac umi sequence
try:
umi = umi_from_name(read.qname)
except UMINotFound:
print("You may be processing alignments that haven't been annotated with UMIs!", file=sys.stderr)
raise
# get actual read start
# read.pos accounts for 5' soft clipping
if read.is_reverse:
# read.alen alignment length accounting for 3' soft clipping
# UMIs are then compared to reads with the same start
read_start = read.pos + read.alen
else:
read_start = read.pos
# add count for this start; counts all reads
read_counts[read_start] += 1
# check if UMI seen
if umi in umi_idx[read_start]:
continue
# check if UMI is similar enough to another that has been seen
if mismatches > 0 and is_similar(umi, umi_idx[read_start], mismatches):
# do not count; group similar UMIs into one
continue
# keep track of unique UMIs - set eliminates duplicates
umi_idx[read_start].add(umi)
out_bam.write(read)
# process before and after counts over chrom
for start, before_count in sorted(read_counts.items()):
print(chrom, start, start + 1, before_count, len(umi_idx[start]), sep="\t")
def bam_variant_aln(args):
samfile = Samfile(args.bam)
for rec in args.vcf:
fp = open(vcf)
reader = pyvcf.Reader(fp)
self.positions = []
for rec in samfile.fetch(vcf):
samfile.getrname(rec.tid)
rec
def _open_alignment_file(in_path):
"""Returns alignment file handle for BAM, SAM, or CRAM"""
input_extension = in_path.split(".")[-1].lower()
if input_extension == "bam":
alignment_file = Samfile(in_path, "rb")
elif input_extension == "sam":
alignment_file = Samfile(in_path, "r")
elif input_extension == "cram":
alignment_file = Samfile(in_path, "rc")
return alignment_file
def __init__(self, file_name):
"""
Initializes BamFile.
Variables:
bam -- Pysam's bam representation.
sg_coefs -- Savitzky-Golay coefficients (list). Should be loaded after class initialization.
"""
self.file_name = file_name
self.bam = Samfile(file_name, "rb")
self.sg_coefs = None
def parse_bam_absolute(fn, regs):
"""(internal) Parses bam file in absolute mode. Proceeds by counting reads mapping
onto a segment (chr, start, end) and normalizes the count by the segment's length.
"""
bam = Samfile(str(fn), "rb")
count = []
for reg in regs:
chr, start, end = reg[:3]
n = bam.count(chr, start, end)
count.append(float(n) / (end - start))
return count
def parse_bam_absolute(fn, regs):
"""(internal) Parses bam file in absolute mode. Proceeds by counting reads mapping
onto a segment (chr, start, end) and normalizes the count by the segment's length.
"""
bam = Samfile(str(fn), "rb")
count = []
for reg in regs:
chr, start, end = reg[:3]
n = bam.count(chr, start, end)
count.append(float(n) / (end - start))
return count
def test_write_hdf5_chrom_dtype():
contig_label = "AS2_scf7180000696055"
bampath = "fixture/longcontignames.bam"
dtypes = [None, {"chrom": "a20"}, {"chrom": "a20"}]
alignments = [Samfile(bampath), Samfile(bampath), bampath]
results = [len(contig_label), 20, 20]
labels = [contig_label, contig_label, contig_label]
for arg in zip(dtypes, alignments, results, labels):
assert check_write_hdf5_chrom_dtype(arg)
def create_table(half_ext, feature_summit_file_name, bam_names, bam_counts, bam_list, output_file_name):
# Initialization
outLoc = "/".join(output_file_name.split("\t")[:-1]) + "/"
command = "mkdir -p "+outLoc
os.system(command)
# Allowed chromosomes
chrList = ["chr"+str(e) for e in range(1,23)+["X"]]
# Fetching regions
featureSummitFile = open(feature_summit_file_name,"r")
regionList = []
for line in featureSummitFile:
ll = line.strip().split("\t")
if(ll[0] not in chrList): continue
region = [ll[0], int(ll[1])-half_ext, int(ll[2])+half_ext]
if(int(region[1]) < 0): continue
regionList.append(region)
featureSummitFile.close()
# Creating table
matrix = []
for i in range(0,len(bam_list)):
inputBamFileName = bam_list[i]
correctFactor = int(bam_counts[i])/1000000
extension = inputBamFileName.split(".")[-1]
if(extension == "bam"):
bamFile = Samfile(inputBamFileName,"rb")
vec = []
for region in regionList:
try: bamSignal = fetchSignal(bamFile, region) / correctFactor
except Exception: bamSignal = 0
vec.append(bamSignal)
elif(extension == "bw" or extension == "bigwig"):
bamFile = pyBigWig.open(inputBamFileName)
vec = []
for region in regionList:
try: bamSignal = fetchSignalBw(bamFile, region) / correctFactor
except Exception: bamSignal = 0
vec.append(bamSignal)
else: print("The tool supports only BAM or BIGWIG files.")
matrix.append(vec)
bamFile.close()
outputFile = open(output_file_name,"w")
outputFile.write("\t".join(bam_names)+"\n")
for j in range(0,len(matrix[0])):
vec = []
for i in range(0,len(matrix)):
try: vec.append(str(matrix[i][j]))
except Exception: vec.append("NA")
outputFile.write("\t".join(vec)+"\n")
outputFile.close()
def get_raw_tracks(args):
# Initializing Error Handler
err = ErrorHandler()
if len(args.input_files) != 2:
err.throw_error("ME_FEW_ARG",
add_msg="You must specify reads and regions file.")
output_fname = os.path.join(args.output_location,
"{}.wig".format(args.output_prefix))
bam = Samfile(args.input_files[0], "rb")
regions = GenomicRegionSet("Interested regions")
regions.read(args.input_files[1])
regions.merge()
reads_file = GenomicSignal()
with open(output_fname, "a") as output_f:
for region in regions:
# Raw counts
signal = [0.0] * (region.final - region.initial)
for read in bam.fetch(region.chrom, region.initial, region.final):
if not read.is_reverse:
cut_site = read.pos + args.forward_shift
if region.initial <= cut_site < region.final:
signal[cut_site - region.initial] += 1.0
else:
cut_site = read.aend + args.reverse_shift - 1
if region.initial <= cut_site < region.final:
signal[cut_site - region.initial] += 1.0
if args.norm:
signal = reads_file.boyle_norm(signal)
perc = scoreatpercentile(signal, 98)
std = np.std(signal)
signal = reads_file.hon_norm_atac(signal, perc, std)
output_f.write("fixedStep chrom=" + region.chrom + " start=" +
str(region.initial + 1) + " step=1\n" +
"\n".join([str(e)
for e in np.nan_to_num(signal)]) + "\n")
output_f.close()
if args.bigWig:
genome_data = GenomeData(args.organism)
chrom_sizes_file = genome_data.get_chromosome_sizes()
bw_filename = os.path.join(args.output_location,
"{}.bw".format(args.output_prefix))
os.system(" ".join([
"wigToBigWig", output_fname, chrom_sizes_file, bw_filename,
"-verbose=0"
]))
os.remove(output_fname)
def removeEdgeMismatches(self, bamFile, minDistance, minBaseQual):
startTime = Helper.getTime()
minDistance = int(minDistance)
counter = 0
j = 0
num_lines = len(self.variantDict)
Helper.info(
" [%s] remove Missmatches from the first %s bp from read edges" %
(startTime.strftime("%c"), str(minDistance)), self.logFile,
self.textField)
bamFile = Samfile(bamFile, "rb")
for varKey in self.variantDict.keys():
variant = self.variantDict[varKey]
counter += 1
if counter % 10000 == 0:
Helper.status('%s mm parsed ' % counter, self.logFile,
self.textField, "grey")
keepSNP = False
varPos = variant.position - 1
iter = bamFile.pileup(variant.chromosome, variant.position - 1,
variant.position)
#walks up the region wich overlap this position
for x in iter:
if x.pos == varPos:
for pileupread in x.pileups: #walk through the single reads
if not pileupread.is_del and not pileupread.is_refskip:
distance = abs(
pileupread.alignment.alen -
pileupread.query_position
) if pileupread.alignment.is_reverse else pileupread.query_position
if distance >= minDistance:
#check readBase and Base Quality
if pileupread.alignment.query_sequence[
pileupread.
query_position] == variant.alt and pileupread.alignment.query_qualities[
pileupread.
query_position] >= minBaseQual:
#if pileupread.alignment.query_sequence[pileupread.query_position] == variant.alt:
keepSNP = True
if keepSNP == False:
j += 1
del self.variantDict[varKey]
Helper.status('%s of %svariants were deleted' % (j, num_lines),
self.logFile, self.textField, "black")
Helper.printTimeDiff(startTime, self.logFile, self.textField)
bamFile.close()
def main():
bam = Samfile("bedtools/tests/data/NA18152.bam", "rb")
rmsk = IntervalFile("bedtools/tests/data/rmsk.hg18.chr21.bed")
for al in bam:
chrom = bam.getrname(al.rname)
start = al.pos
end = al.aend
name = al.qname
for hit in rmsk.search(chrom, start, end):
print chrom, start, end, name,
print hit.chrom, hit.start, hit.end, hit.name
def main(args):
option = "r" if args.samformat else "rb"
samfile = Samfile(args.bamfile, "rb")
#Iterates over each read instead of each contig
outputs = defaultdict(list)
#import ipdb; ipdb.set_trace()
for aln in samfile.fetch(until_eof = True):
ref = samfile.getrname(aln.tid)
outputs[ref].append(aln)
for ref, alns in outputs.iteritems():
print_reads(alns, ref, samfile.header)
def main(args=None):
if args is None:
args = sys.argv[1:]
f = Samfile(args[0])
header = f.header
f.close()
reflen = header['SQ'][0]['LN']
BamIO.write(clip(BamIO.parse(args[0]), reflen), args[1], header=header)
return 0
def paired_end_sam_parsing(sam_list, cov, identity_threshold):
match = {}
to_process = []
if sam_list[0] is None:
print "The ene-to-end mapping of SE data produced an error."
else:
to_process.append(sam_list[0])
if sam_list[1] is None:
print "The local mapping mode of SE data produced an error."
else:
to_process.append(sam_list[1])
for paired_sam in to_process:
r1_match = {}
r2_match = {}
sam = Samfile(paired_sam)
for align in sam:
if align.tid != -1:
query_name, query_len, ref_name = align.qname, float(
align.rlen), sam.getrname(align.tid)
if align.cigar is not None:
align_len, query_aligned_len = cigar_parsing(align.cigar)
# print query_name, align_len, query_aligned_len
nm = -1
if (query_aligned_len / query_len) * 100 >= cov:
for coppia in align.tags:
if coppia[0] == "NM":
nm = float(coppia[1])
if align_len != 0 and nm >= 0:
paired_perc_id = ((align_len - nm) / align_len) * 100
if paired_perc_id >= 90:
if align.is_read1:
r1_match.setdefault(query_name, {})
r1_match[query_name].setdefault(ref_name, [])
r1_match[query_name][ref_name].append(
paired_perc_id)
if align.is_read2:
r2_match.setdefault(query_name, {})
r2_match[query_name].setdefault(ref_name, [])
r2_match[query_name][ref_name].append(
paired_perc_id)
sam.close()
for query in set(r1_match.keys()).intersection(set(r2_match.keys())):
for ref in set(r1_match[query].keys()).intersection(
r2_match[query].keys()):
average_perc_id = calcola_media(
[max(r1_match[query][ref]),
max(r2_match[query][ref])])
if average_perc_id >= identity_threshold:
match.setdefault(query, set())
match[query].add(ref)
return match
def __init__(self, fname, referenceFastaFname=None):
self.filename = fname = abspath(expanduser(fname))
self.peer = Samfile(fname, "rb", check_sq=False)
self._checkFileCompatibility()
self._loadReferenceInfo()
self._loadReadGroupInfo()
self._loadProgramInfo()
self.referenceFasta = None
if referenceFastaFname is not None:
if self.isUnmapped:
raise ValueError, "Unmapped BAM file--reference FASTA should not be given as argument to BamReader"
self._loadReferenceFasta(referenceFastaFname)
def main():
bam = Samfile("bedtools/tests/data/NA18152.bam", "rb")
rmsk = IntervalFile("bedtools/tests/data/rmsk.hg18.chr21.bed")
# Example 1:
# Method: IntervalFile.all_hits()
# Report _all_ of the rmsk features that overlap with the BAM alignment
for al in bam:
strand = "+"
if al.is_reverse: strand = "-"
i = Interval(bam.getrname(al.rname), al.pos, al.aend, strand)
for hit in rmsk.all_hits(i, same_strand=True, ovlp_pct=0.75):
print "\t".join(str(x) for x in [i, hit])
def compare_stats(impl, refimpl):
# no read filters
kwargs = {'chrom': 'Pf3D7_01_v3',
'start': 0,
'end': 2000,
'one_based': False}
expected = refimpl(Samfile('fixture/test.bam'), **kwargs)
actual = impl(Samfile('fixture/test.bam'), **kwargs)
compare_iterators(expected, actual)
# read filters
kwargs['min_mapq'] = 1
kwargs['no_dup'] = True
expected = refimpl(Samfile('fixture/test.bam'), **kwargs)
actual = impl(Samfile('fixture/test.bam'), **kwargs)
compare_iterators(expected, actual)
def main():
bam = Samfile("bedtools/tests/data/NA18152.bam", "rb")
rmsk = IntervalFile("bedtools/tests/data/rmsk.hg18.chr21.bed")
# Example 1:
# Method: IntervalFile.all_hits()
# Report _all_ of the rmsk features that overlap with the BAM alignment
for al in bam:
strand = "+"
if al.is_reverse: strand = "-"
i = Interval(bam.getrname(al.rname), al.pos, al.aend, strand)
for hit in rmsk.all_hits(i, same_strand=True, ovlp_pct=0.75):
print "\t".join(str(x) for x in [i,hit])
def expression_dict_from_bam(alias_dict, gene_dict, exp_file_name):
# Fetching expression
exp_dict = dict()
exp_file = Samfile(exp_file_name, "rb")
for k in gene_dict.keys():
geneVec = gene_dict[k]
gene = geneVec[3]
region = [geneVec[0], int(geneVec[1]), int(geneVec[2])]
exp = fetch_counts(exp_file, region)
exp_dict[gene] = float(exp) / (region[2] - region[1])
exp_file.close()
# Returning objects
return exp_dict
def annotate(context, bam_path, in_stream, sample, group, cutoff, extendby,
prefix, threshold):
"""Annotate intervals in a BED-file/stream.
\b
BAM_PATH: Path to BAM-file
IN_STREAM: Chanjo-style BED-file with interval definitions
"""
# connect to the BAM file
with Samfile(bam_path) as bam:
# user defined sample id or randomly generated
sample = (sample or get_sample_id(bam.header) or id_generator())
# step 1: metadata header
metadata = dict(sample_id=sample,
group_id=group,
cutoff=cutoff,
coverage_source=path(bam_path).abspath(),
extension=extendby)
click.echo("#%s" % json.dumps(metadata))
# step 2: annotate list of intervals with coverage and completeness
bed_lines = pipe(
annotate_bed_stream(bed_stream=in_stream,
bam_path=bam_path,
cutoff=cutoff,
extension=extendby,
contig_prefix=prefix,
bp_threshold=threshold),
map(serialize_interval(bed=True)) # stringify/bedify
)
# reduce/write the BED lines
for bed_line in bed_lines:
click.echo(bed_line)
def bam_uniq(args):
"""
* BAM file should be sorted in (tid, pos)
* (qname, pos, is_unmapped, is_read_2, cigar) is checked
* if multiple records exist, primary alignment is selected
* scores are not changed
"""
sam = Samfile(args.bam)
# setup output
if args.output.endswith('.bam'):
mode = 'wb'
else:
mode = 'wh'
out = pysam.Samfile(args.output, mode=mode, template=sam)
it = sam # TODO region
def get_key(rec):
return (rec.qname, rec.pos, rec.is_unmapped, rec.is_read2, rec.cigar)
def get_best_rec(recs):
for rec in recs:
if not rec.is_secondary and not rec.is_supplementary:
return rec
return rec # No primary alignments were found
for (tid,
pos), recs in groupby(it, lambda rec:
(rec.tid, rec.pos)): # assume position sorted
recs1 = sorted(recs, key=get_key) # manual sort by key is needed
for key, recs2 in groupby(recs1, get_key):
rec = get_best_rec(recs2)
out.write(rec)
def compare_stats_withref(impl, refimpl, bam_fn='fixture/test.bam',
fasta_fn='fixture/ref.fa'):
# no read filters
kwargs = {'chrom': 'Pf3D7_01_v3',
'start': 0,
'end': 2000,
'one_based': False}
expected = refimpl(Samfile(bam_fn), Fastafile(fasta_fn), **kwargs)
actual = impl(Samfile(bam_fn), Fastafile(fasta_fn), **kwargs)
compare_iterators(expected, actual)
# read filters
kwargs['min_mapq'] = 1
kwargs['no_dup'] = True
expected = refimpl(Samfile(bam_fn), Fastafile(fasta_fn), **kwargs)
actual = impl(Samfile(bam_fn), Fastafile(fasta_fn), **kwargs)
compare_iterators(expected, actual)
def filter(self, infile, countfile):
inbam = Samfile(infile, 'rb')
count_labels = [
'u', 'u-pf', 'u-pf-n',
'u-pf-n-mm%d' % self.max_mismatches,
'u-pf-n-mm%d-mito' % self.max_mismatches, 'mm', 'nm', 'qc-flagged',
'umi-duplicate', 'umi-duplicate-nuclear', 'nuclear-align',
'autosomal-align', 'paired-aligned', 'paired-nuclear-align',
'paired-autosomal-align', 'all-aligned', 'all-mapq-filter'
]
logging.debug(count_labels)
self.counts = dict([(label, 0) for label in count_labels])
self.chrcounts = defaultdict(int)
self.mapqcounts = defaultdict(int)
self.samflagcounts = defaultdict(int)
self.readlengthcounts = defaultdict(int)
for read in inbam:
self.process_read(read, inbam)
countout = open(countfile, 'a')
self.write_dict(countout, self.counts)
self.write_dict(countout, self.chrcounts)
self.write_dict(countout, self.mapqcounts)
self.write_dict(countout, self.samflagcounts)
self.write_dict(countout, self.readlengthcounts)
countout.close()
def test_read_evidence_variant_matching_gatk_mini_bundle_extract():
handle = Samfile(data_path("gatk_mini_bundle_extract.bam"))
loci = [
Locus.from_inclusive_coordinates("20", 10008951), # 0
Locus.from_inclusive_coordinates("20", 10009053), # 1
Locus.from_inclusive_coordinates("20", 10009053, 10009054), # 2
Locus.from_inclusive_coordinates("20", 10006822), # 3
Locus.from_inclusive_coordinates("20", 10006822, 10006823), # 4
]
evidence = PileupCollection.from_bam(handle, loci)
eq_(evidence.match_summary(Variant(loci[0], "A", "C")), [('A', 1),
('C', 4)])
eq_(
evidence.filter(drop_duplicates=True).match_summary(
Variant(loci[0], "A", "C")), [('A', 0), ('C', 3)])
eq_(evidence.match_summary(Variant(loci[1], "A", "C")), [('A', 3),
('C', 0)])
eq_(evidence.match_summary(Variant(loci[1], "A", "CC")), [('A', 3),
('CC', 0)])
eq_(evidence.match_summary(Variant(loci[1], "A", "")), [('A', 3), ('', 0)])
eq_(evidence.match_summary(Variant(loci[1], "A", "")), [('A', 3), ('', 0)])
eq_(evidence.match_summary(Variant(loci[2], "AT", "")), [('AT', 3),
('', 0)])
eq_(evidence.match_summary(Variant(loci[3], "A", "")), [('A', 2), ('', 6)])
eq_(evidence.match_summary(Variant(loci[4], "AC", "")), [('AC', 2),
('', 6)])
eq_(
evidence.match_summary(
Variant(loci[4], "AC", ""),
lambda e: e.read_attributes().mapping_quality.mean()),
[('AC', 60.0), ('', 65.0)])
def get_bc_signal(arguments):
(mpbs_region, reads_file, organism, window_size, forward_shift,
reverse_shift, bias_table) = arguments
bam = Samfile(reads_file, "rb")
genome_data = GenomeData(organism)
signal = np.zeros(window_size)
# Fetch bias corrected signal
for region in mpbs_region:
mid = (region.final + region.initial) // 2
p1 = mid - window_size // 2
p2 = mid + window_size // 2
if p1 <= 0:
continue
# Fetch raw signal
_signal = bias_correction(chrom=region.chrom,
start=p1,
end=p2,
bam=bam,
bias_table=bias_table,
genome_file_name=genome_data.get_genome(),
forward_shift=forward_shift,
reverse_shift=reverse_shift)
if len(_signal) != window_size:
continue
# smooth the signal
signal = np.add(signal, np.array(_signal))
return signal
def mc_path_call6(args):
with open(args.gref) as fp:
fasta = Fasta(fp)
contigs = {contig.name: contig.seq.upper() for contig in fasta.contigs}
with Samfile(args.bam) as sam:
smb = SamModelBuilder2(sam, regions=args.regions, min_second_bases=args.min_second_bases, contigs=contigs)
if not args.table:
hap_depths = {ref.name: args.hap_depth for ref in smb.model.refs}
ploidies = {ref.name: args.copy_number for ref in smb.model.refs}
else:
tab = pd.read_table(args.table)
hap_depths = dict(zip(tab.contig, tab.hap_depth))
ploidies = dict(zip(tab.contig, tab.copy_number))
show_model(smb.model, verbosity=args.verbose)
from .infer6 import InferModel
im = InferModel(smb, hap_depths=hap_depths, ploidies=ploidies)
#im.init_best_het()
#im.init()
#im.run_through_variants()
if args.no_phase:
im.run_genotyping()
else:
im.run_haplotyping()
start_var = None
#start_var = smb.model.refs[0].get_variant(1203)
#im.run_phase_variants(start_var=start_var)
im.show_variant_info(show_all_variant=True)
def __init__(self, file_name):
"""
Initializes GenomicSignal.
"""
self.file_name = file_name
self.sg_coefs = None
self.bam = Samfile(file_name, "rb")
def get_raw_tracks(args):
# Initializing Error Handler
err = ErrorHandler()
if len(args.input_files) != 2:
err.throw_error("ME_FEW_ARG", add_msg="You must specify reads and regions file.")
output_fname = os.path.join(args.output_location, "{}.wig".format(args.output_prefix))
bam = Samfile(args.input_files[0], "rb")
regions = GenomicRegionSet("Interested regions")
regions.read(args.input_files[1])
regions.merge()
reads_file = GenomicSignal()
with open(output_fname, "a") as output_f:
for region in regions:
# Raw counts
signal = [0.0] * (region.final - region.initial)
for read in bam.fetch(region.chrom, region.initial, region.final):
if not read.is_reverse:
cut_site = read.pos + args.forward_shift
if region.initial <= cut_site < region.final:
signal[cut_site - region.initial] += 1.0
else:
cut_site = read.aend + args.reverse_shift - 1
if region.initial <= cut_site < region.final:
signal[cut_site - region.initial] += 1.0
if args.norm:
signal = reads_file.boyle_norm(signal)
perc = scoreatpercentile(signal, 98)
std = np.std(signal)
signal = reads_file.hon_norm_atac(signal, perc, std)
output_f.write("fixedStep chrom=" + region.chrom + " start=" + str(region.initial + 1) + " step=1\n" +
"\n".join([str(e) for e in np.nan_to_num(signal)]) + "\n")
output_f.close()
if args.bigWig:
genome_data = GenomeData(args.organism)
chrom_sizes_file = genome_data.get_chromosome_sizes()
bw_filename = os.path.join(args.output_location, "{}.bw".format(args.output_prefix))
os.system(" ".join(["wigToBigWig", output_fname, chrom_sizes_file, bw_filename, "-verbose=0"]))
os.remove(output_fname)
def split_reads(reads, ref_reads, alt_reads):
read_results = Counter()
ref_bam = Samfile(ref_reads, 'wb', template=reads)
alt_bam = Samfile(alt_reads, 'wb', template=reads)
prev_phase = None
prev_read = None
prev_qname = None
test = 0
for read in reads.fetch(until_eof=True):
test += 1
chrom = read.reference_name
snps_on_chrom = snp_dict[chrom]
phase = get_phase(read, snps_on_chrom)
read_qname = read.qname
if read_qname == prev_qname:
read_results["tot_read"]+=1
phase_set = set([phase, prev_phase])
phase_set.discard(None)
if len(phase_set) == 1:
read_phase = phase_set.pop()
if read_phase == -1:
ref_bam.write(read)
ref_bam.write(prev_read)
read_results["ref_read"]+=1
elif read_phase == 1:
alt_bam.write(read)
alt_bam.write(prev_read)
read_results["alt_read"]+=1
elif read_phase == 0:
read_results['misphased_read']+=1
elif len(phase_set)==0:
read_results["no_snps_read"]+=1
else:
read_results['misphased_read']+=1
prev_read = read
prev_phase = phase
prev_qname = read_qname
return(read_results)
def subsample(fn, ns=None):
if ns is None:
fn, ns = fn
sample = []
count = 0
outdir_base = path.join(path.dirname(fn), "subset")
sf = Samfile(fn)
try:
i_weight = float(sf.mapped) / max(ns)
print "Read out ", i_weight
except ValueError:
i_weight = 0.0
for read in sf:
i_weight += 1
print "Counted ", i_weight
i_weight /= float(max(ns))
sf = Samfile(fn)
print fn, count, i_weight
for i, read in enumerate(sf):
key = random() ** i_weight
if len(sample) < max(ns):
heappush(sample, (key, read, i + count))
else:
heappushpop(sample, (key, read, i + count))
count += i
for n in ns:
if n == min(ns):
outdir = outdir_base + "_min"
else:
outdir = outdir_base + "{:04.1f}M".format(n / 1e6)
try:
makedirs(outdir)
except OSError:
pass
sampN = sorted(sample, reverse=True)[: int(n)]
print "Kept {: >12,} of {: >12,} reads".format(len(sampN), count)
print fn, "->", outdir
stdout.flush()
of = Samfile(path.join(outdir, "accepted_hits.bam"), mode="wb", template=sf)
sample.sort(key=lambda (key, read, pos): (read.tid, read.pos))
for key, read, pos in sampN:
of.write(read)
of.close()
sf.close()
return [count for key, read, count in sample]
def _bowtie2_filter(fnam, fastq_path, unmap_out, map_out):
"""
Divides reads in a map file in two categories: uniquely mapped, and not.
Writes them in two files
"""
try:
fhandler = Samfile(fnam)
except IOError:
raise Exception('ERROR: file "%s" not found' % fnam)
# getrname chromosome names
i = 0
crm_dict = {}
while True:
try:
crm_dict[i] = fhandler.getrname(i)
i += 1
except ValueError:
break
# iteration over reads
unmap_out = open(unmap_out, 'w')
map_out = open(map_out, 'w')
fastq_in = open(fastq_path , 'r')
for line in fhandler:
line_in = fastq_in.readline()
if line.is_unmapped or line.mapq < 4:
read = '%s\t%s\t%s\t%s\t%s\n' % (
line_in.split('\t', 1)[0].rstrip('\n')[1:],
line.seq, line.qual, '-', '-'
)
unmap_out.write(read)
else:
read = '%s\t%s\t%s\t%s\t%s:%s:%d:%d\n' % (
line.qname, line.seq, line.qual, '1',
crm_dict[line.tid],
'-' if line.is_reverse else '+', line.pos + 1, len(line.seq))
map_out.write(read)
for _ in range(3):
fastq_in.readline()
unmap_out.close()
map_out.close()
fastq_in.close()
def main(args):
option = "r" if args.samformat else "rb"
samfile = Samfile(args.bamfile, "rb")
ref_ids = [samfile.gettid(r) for r in samfile.references]
#Iterates over each read instead of each contig
reads_to_print = []
for aln in samfile.fetch(until_eof = True):
if pair_is_aligned(aln, ref_ids):
if args.read_pair == 1 and aln.is_read1:
reads_to_print.append(aln)
elif args.read_pair == 2 and aln.is_read2:
reads_to_print.append(aln)
elif args.read_pair == 0:
reads_to_print.append(aln)
if len(reads_to_print) >= 10000:
# Flush the reads collected
print_reads(reads_to_print)
reads_to_print = []
print_reads(reads_to_print)
def removeEdgeMismatches(self,bamFile,minDistance, minBaseQual):
startTime=Helper.getTime()
minDistance=int(minDistance)
counter=0;j=0
num_lines = len(self.variantDict)
Helper.info(" [%s] remove Missmatches from the first %s bp from read edges" % (startTime.strftime("%c"),str(minDistance)),self.logFile,self.textField)
bamFile = Samfile(bamFile, "rb")
for varKey in self.variantDict.keys():
variant = self.variantDict[varKey]
counter+=1
if counter%10000==0:
Helper.status('%s mm parsed ' % counter ,self.logFile, self.textField,"grey")
keepSNP=False
varPos=variant.position-1
iter = bamFile.pileup(variant.chromosome, variant.position-1, variant.position)
#walks up the region wich overlap this position
for x in iter:
if x.pos == varPos:
for pileupread in x.pileups: #walk through the single reads
if not pileupread.is_del and not pileupread.is_refskip:
distance=abs(pileupread.alignment.alen-pileupread.query_position) if pileupread.alignment.is_reverse else pileupread.query_position
if distance >= minDistance:
#check readBase and Base Quality
if pileupread.alignment.query_sequence[pileupread.query_position] == variant.alt and pileupread.alignment.query_qualities[pileupread.query_position]>=minBaseQual:
#if pileupread.alignment.query_sequence[pileupread.query_position] == variant.alt:
keepSNP=True
if keepSNP==False:
j+=1
del self.variantDict[varKey]
Helper.status('%s of %svariants were deleted' % (j,num_lines), self.logFile, self.textField,"black")
Helper.printTimeDiff(startTime, self.logFile, self.textField)
bamFile.close()
def __init__(self, fname, referenceFastaFname=None):
self.filename = fname = abspath(expanduser(fname))
self.peer = Samfile(fname, "rb", check_sq=False)
self._checkFileCompatibility()
self._loadReferenceInfo()
self._loadReadGroupInfo()
self._loadProgramInfo()
self.referenceFasta = None
if referenceFastaFname is not None:
if self.isUnmapped:
raise ValueError, "Unmapped BAM file--reference FASTA should not be given as argument to BamReader"
self._loadReferenceFasta(referenceFastaFname)
def main(args):
option = "r" if args.samformat else "rb"
samfile = Samfile(args.bamfile, option)
ref_ids = [samfile.gettid(r) for r in samfile.references]
#Iterates over each read instead of each contig
reads_to_print_1 = []
reads_to_print_2 = []
reads_to_print_u = []
for aln in samfile.fetch(until_eof = True):
if aln.tid in ref_ids: # This read is aligned
if aln.rnext in ref_ids: # The mate is also aligned
if aln.is_read1:
reads_to_print_1.append(aln)
reads_to_print_1 = flush_reads(reads_to_print_1, args.R1)
elif aln.is_read2:
reads_to_print_2.append(aln)
reads_to_print_2 = flush_reads(reads_to_print_2, args.R2)
else:
reads_to_print_u.append(aln)
reads_to_print_u = flush_reads(reads_to_print_u, args.u)
print_reads(reads_to_print_1, args.R1)
print_reads(reads_to_print_2, args.R2)
print_reads(reads_to_print_u, args.u)
def __init__(self, file_name):
"""
Initializes GenomicSignal.
"""
self.file_name = file_name
self.bam = None
self.bw = None
self.sg_coefs = None
self.is_bam = False
self.is_bw = False
if(self.file_name.split(".")[-1].upper() == "BAM"):
self.is_bam = True
self.bam = Samfile(file_name,"rb")
elif(self.file_name.split(".")[-1].upper() == "BW" or self.file_name.split(".")[-1].upper() == "BIGWIG"):
self.is_bw = True
self.bw = BigWigFile(file_name)
else: pass # TODO ERROR
def __init__(self, fname, referenceFastaFname=None):
self.filename = fname = abspath(expanduser(fname))
self.peer = Samfile(fname, "rb")
# Check for sortedness, index.
# There doesn't seem to be a "public" way to do this right
# now, but that's fine because we're going to have to rewrite
# it all anyway once the pysam rewrite lands.
if not self.peer._hasIndex:
raise ValueError, "Specified bam file lacks a bam index---required for this API"
self._loadReferenceInfo()
self._loadReadGroupInfo()
self._loadProgramInfo()
self.referenceFasta = None
if referenceFastaFname is not None:
self._loadReferenceFasta(referenceFastaFname)
biasTableFFile.close()
biasTableR = dict()
biasTableRFile = open(biasTableRFileName,"r")
for line in biasTableRFile:
ll = line.strip().split("\t")
biasTableR[ll[0]] = float(ll[1])
biasTableRFile.close()
#################################################################################################
# EVALUATING PROTECTION
#################################################################################################
# Initialization
protectDict = dict()
bam = Samfile(dnaseFileName,"rb")
# Iterating on MPBSs
for mpbs in mpbsList:
# Fetching MPBSs
grepFileName = outputFileName+"_grepmpbs.bed"
to_remove.append(grepFileName)
os.system("grep \"\t\""+mpbs+"\"\t\" "+mpbsFileNameBed+" | cut -f 1,2,3 | sort -k1,1 -k2,2n > "+grepFileName)
# Intersect with footprints
intFileName = outputFileName+"_fpint.bed"
to_remove.append(intFileName)
os.system("intersectBed -a "+grepFileName+" -b "+fpFileNameBed+" -wa -u > "+intFileName)
# Iterating on MPBSs
def parse_sam(f_names1, f_names2=None, out_file1=None, out_file2=None,
genome_seq=None, re_name=None, verbose=False, clean=True,
mapper=None, **kwargs):
"""
Parse sam/bam file using pysam tools.
Keep a summary of the results into 2 tab-separated files that will contain 6
columns: read ID, Chromosome, position, strand (either 0 or 1), mapped
sequence lebgth, position of the closest upstream RE site, position of
the closest downstream RE site
:param f_names1: a list of path to sam/bam files corresponding to the
mapping of read1, can also be just one file
:param f_names1: a list of path to sam/bam files corresponding to the
mapping of read2, can also be just one file
:param out_file1: path to outfile tab separated format containing mapped
read1 information
:param out_file1: path to outfile tab separated format containing mapped
read2 information
:param genome_seq: a dictionary generated by :func:`pyatdbit.parser.genome_parser.parse_fasta`.
containing the genomic sequence
:param re_name: name of the restriction enzyme used
:param None mapper: software used to map (supported are GEM and BOWTIE2).
Guessed from file by default.
"""
# not nice, dirty fix in order to allow this function to only parse
# one SAM file
if not out_file1:
raise Exception('ERROR: out_file1 should be given\n')
if not re_name:
raise Exception('ERROR: re_name should be given\n')
if not genome_seq:
raise Exception('ERROR: genome_seq should be given\n')
if (f_names2 and not out_file2) or (not f_names2 and out_file2):
raise Exception('ERROR: out_file2 AND f_names2 needed\n')
frag_chunk = kwargs.get('frag_chunk', 100000)
if verbose:
print 'Searching and mapping RE sites to the reference genome'
frags = map_re_sites(re_name, genome_seq, frag_chunk=frag_chunk,
verbose=verbose)
if isinstance(f_names1, str):
f_names1 = [f_names1]
if isinstance(f_names2, str):
f_names2 = [f_names2]
if f_names2:
fnames = f_names1, f_names2
outfiles = out_file1, out_file2
else:
fnames = (f_names1,)
outfiles = (out_file1, )
# max number of reads per intermediate files for sorting
max_size = 1000000
windows = {}
multis = {}
procs = []
for read in range(len(fnames)):
if verbose:
print 'Loading read' + str(read + 1)
windows[read] = {}
num = 0
# iteration over reads
nfile = 0
tmp_files = []
reads = []
for fnam in fnames[read]:
try:
fhandler = Samfile(fnam)
except IOError:
print 'WARNING: file "%s" not found' % fnam
continue
except ValueError:
raise Exception('ERROR: not a SAM/BAM file\n%s' % fnam)
# get the iteration number of the iterative mapping
try:
num = int(fnam.split('.')[-1].split(':')[0])
except:
num += 1
# set read counter
windows[read].setdefault(num, 0)
# guess mapper used
if not mapper:
mapper = fhandler.header['PG'][0]['ID']
if mapper.lower()=='gem':
condition = lambda x: x[1][0][0] != 'N'
elif mapper.lower() in ['bowtie', 'bowtie2']:
condition = lambda x: 'XS' in dict(x)
else:
warn('WARNING: unrecognized mapper used to generate file\n')
condition = lambda x: x[1][1] != 1
if verbose:
print 'loading SAM file from %s: %s' % (mapper, fnam)
# getrname chromosome names
i = 0
crm_dict = {}
while True:
try:
crm_dict[i] = fhandler.getrname(i)
i += 1
except ValueError:
break
# iteration over reads
sub_count = 0 # to empty read buffer
for r in fhandler:
if r.is_unmapped:
continue
if condition(r.tags):
continue
positive = not r.is_reverse
crm = crm_dict[r.tid]
len_seq = len(r.seq)
if positive:
pos = r.pos + 1
else:
pos = r.pos + len_seq
try:
frag_piece = frags[crm][pos / frag_chunk]
except KeyError:
# Chromosome not in hash
continue
idx = bisect(frag_piece, pos)
try:
next_re = frag_piece[idx]
except IndexError:
# case where part of the read is mapped outside chromosome
count = 0
while idx >= len(frag_piece) and count < len_seq:
pos -= 1
count += 1
frag_piece = frags[crm][pos / frag_chunk]
idx = bisect(frag_piece, pos)
if count >= len_seq:
raise Exception('Read mapped mostly outside ' +
'chromosome\n')
next_re = frag_piece[idx]
prev_re = frag_piece[idx - 1 if idx else 0]
name = r.qname
reads.append('%s\t%s\t%d\t%d\t%d\t%d\t%d\n' % (
name, crm, pos, positive, len_seq, prev_re, next_re))
windows[read][num] += 1
sub_count += 1
if sub_count >= max_size:
sub_count = 0
nfile += 1
write_reads_to_file(reads, outfiles[read], tmp_files, nfile)
nfile += 1
write_reads_to_file(reads, outfiles[read], tmp_files, nfile)
# we have now sorted temporary files
# we do merge sort for eah pair
if verbose:
stdout.write('Merge sort')
stdout.flush()
while len(tmp_files) > 1:
file1 = tmp_files.pop(0)
try:
file2 = tmp_files.pop(0)
except IndexError:
break
if verbose:
stdout.write('.')
stdout.flush()
nfile += 1
tmp_files.append(merge_sort(file1, file2, outfiles[read], nfile))
if verbose:
stdout.write('\n')
tmp_name = tmp_files[0]
if verbose:
print 'Getting Multiple contacts'
reads_fh = open(outfiles[read], 'w')
## Also pipe file header
# chromosome sizes (in order)
reads_fh.write('# Chromosome lengths (order matters):\n')
for crm in genome_seq:
reads_fh.write('# CRM %s\t%d\n' % (crm, len(genome_seq[crm])))
reads_fh.write('# Mapped\treads count by iteration\n')
for size in windows[read]:
reads_fh.write('# MAPPED %d %d\n' % (size, windows[read][size]))
## Multicontacts
tmp_reads_fh = open(tmp_name)
try:
read_line = tmp_reads_fh.next()
except StopIteration:
raise StopIteration('ERROR!\n Nothing parsed, check input files and'
' chromosome names (in genome.fasta and SAM/MAP'
' files).')
prev_head = read_line.split('\t', 1)[0]
prev_head = prev_head.split('~' , 1)[0]
prev_read = read_line
multis[read] = 0
for read_line in tmp_reads_fh:
head = read_line.split('\t', 1)[0]
head = head.split('~' , 1)[0]
if head == prev_head:
multis[read] += 1
prev_read = prev_read.strip() + '|||' + read_line
else:
reads_fh.write(prev_read)
prev_read = read_line
prev_head = head
reads_fh.write(prev_read)
reads_fh.close()
if clean:
os.system('rm -rf ' + tmp_name)
# wait for compression to finish
for p in procs:
p.communicate()
return windows, multis
class _BamReaderBase(ReaderBase):
"""
The BamReader class provides a high-level interface to PacBio BAM
files. If a PacBio BAM index (bam.pbi file) is present and the
user instantiates the BamReader using the reference FASTA as the
second argument, the BamReader will provide an interface
compatible with CmpH5Reader.
"""
def _loadReferenceInfo(self):
refRecords = self.peer.header["SQ"]
refNames = [r["SN"] for r in refRecords]
refLengths = [r["LN"] for r in refRecords]
refMD5s = [r["M5"] for r in refRecords]
refIds = map(self.peer.gettid, refNames)
nRefs = len(refRecords)
if nRefs > 0:
self._referenceInfoTable = np.rec.fromrecords(zip(
refIds,
refIds,
refNames,
refNames,
refLengths,
refMD5s,
np.zeros(nRefs, dtype=np.uint32),
np.zeros(nRefs, dtype=np.uint32)),
dtype=[('ID', '<i8'), ('RefInfoID', '<i8'),
('Name', 'O'), ('FullName', 'O'),
('Length', '<i8'), ('MD5', 'O'),
('StartRow', '<u4'), ('EndRow', '<u4')])
self._referenceDict = {}
self._referenceDict.update(zip(refIds, self._referenceInfoTable))
self._referenceDict.update(zip(refNames, self._referenceInfoTable))
else:
self._referenceInfoTable = None
self._referenceDict = None
def _loadReadGroupInfo(self):
rgs = self.peer.header["RG"]
readGroupTable_ = []
pulseFeaturesInAll_ = frozenset(PULSE_FEATURE_TAGS.keys())
for rg in rgs:
rgID = rgAsInt(rg["ID"])
rgName = rg["PU"]
ds = dict([pair.split("=") for pair in rg["DS"].split(";") if pair != ""])
# spec: we only consider first two components of basecaller version
# in "chem" lookup
basecallerVersion = ".".join(ds["BASECALLERVERSION"].split(".")[0:2])
triple = ds["BINDINGKIT"], ds["SEQUENCINGKIT"], basecallerVersion
rgChem = decodeTriple(*triple)
rgReadType = ds["READTYPE"]
# TODO(dalexander): need FRAMERATEHZ in RG::DS!
#rgFrameRate = ds["FRAMERATEHZ"]
rgFrameRate = 75.0
readGroupTable_.append((rgID, rgName, rgReadType, rgChem, rgFrameRate))
pulseFeaturesInAll_ = pulseFeaturesInAll_.intersection(ds.keys())
self._readGroupTable = np.rec.fromrecords(
readGroupTable_,
dtype=[("ID" , np.int32),
("MovieName" , "O"),
("ReadType" , "O"),
("SequencingChemistry", "O"),
("FrameRate", float)])
assert len(set(self._readGroupTable.ID)) == len(self._readGroupTable), \
"First 8 chars of read group IDs must be unique!"
self._readGroupDict = { rg.ID : rg
for rg in self._readGroupTable }
self._pulseFeaturesAvailable = pulseFeaturesInAll_
def _loadProgramInfo(self):
pgRecords = [ (pg["ID"], pg.get("VN", None), pg.get("CL", None))
for pg in self.peer.header.get("PG", []) ]
if len(pgRecords) > 0:
self._programTable = np.rec.fromrecords(
pgRecords,
dtype=[("ID" , "O"),
("Version", "O"),
("CommandLine", "O")])
else:
self._programTable = None
def _loadReferenceFasta(self, referenceFastaFname):
ft = FastaTable(referenceFastaFname)
# Verify that this FASTA is in agreement with the BAM's
# reference table---BAM should be a subset.
fastaIdsAndLens = set((c.id, len(c)) for c in ft)
bamIdsAndLens = set((c.Name, c.Length) for c in self.referenceInfoTable)
if not bamIdsAndLens.issubset(fastaIdsAndLens):
raise ReferenceMismatch, "FASTA file must contain superset of reference contigs in BAM"
self.referenceFasta = ft
def _checkFileCompatibility(self):
# Verify that this is a "pacbio" BAM file of version at least
# 3.0b3
try:
checkedVersion = self.version
except:
raise IncompatibleFile(
"This BAM file is incompatible with this API " +
"(only PacBio BAM files version >= 3.0b3 are supported)")
def __init__(self, fname, referenceFastaFname=None):
self.filename = fname = abspath(expanduser(fname))
self.peer = Samfile(fname, "rb", check_sq=False)
self._checkFileCompatibility()
self._loadReferenceInfo()
self._loadReadGroupInfo()
self._loadProgramInfo()
self.referenceFasta = None
if referenceFastaFname is not None:
if self.isUnmapped:
raise ValueError, "Unmapped BAM file--reference FASTA should not be given as argument to BamReader"
self._loadReferenceFasta(referenceFastaFname)
@property
def isIndexLoaded(self):
return self.index is not None
@property
def isReferenceLoaded(self):
return self.referenceFasta is not None
@property
def isUnmapped(self):
return not(self.isMapped)
@property
def isMapped(self):
return len(self.peer.header["SQ"]) > 0
@property
def alignmentIndex(self):
raise UnavailableFeature("BAM has no alignment index")
@property
def movieNames(self):
return set([mi.MovieName for mi in self.readGroupTable])
@property
def readGroupTable(self):
return self._readGroupTable
def readGroupInfo(self, readGroupId):
return self._readGroupDict[readGroupId]
@property
def sequencingChemistry(self):
"""
List of the sequencing chemistries by movie. Order is
unspecified.
"""
return list(self.readGroupTable.SequencingChemistry)
@property
def referenceInfoTable(self):
return self._referenceInfoTable
#TODO: standard? how about subread instead? why capitalize ccs?
# can we standardize this? is cDNA an additional possibility
@property
def readType(self):
"""
Either "standard", "CCS", "mixed", or "unknown", to represent the
type of PacBio reads aligned in this BAM file.
"""
readTypes = self.readGroupTable.ReadType
if all(readTypes == "SUBREAD"):
return "standard"
elif all(readTypes == "CCS"):
return "CCS"
elif all((readTypes == "CCS") | (readTypes == "SUBREAD")):
return "mixed"
else:
return "unknown"
@property
def version(self):
return self.peer.header["HD"]["pb"]
def versionAtLeast(self, minimalVersion):
raise Unimplemented()
def softwareVersion(self, programName):
raise Unimplemented()
@property
def isSorted(self):
return self.peer.header["HD"]["SO"] == "coordinate"
@property
def isBarcoded(self):
raise Unimplemented()
@property
def isEmpty(self):
return (len(self) == 0)
def referenceInfo(self, key):
return self._referenceDict[key]
def atOffset(self, offset):
self.peer.seek(offset)
return BamAlignment(self, next(self.peer))
def hasPulseFeature(self, featureName):
return featureName in self._pulseFeaturesAvailable
def pulseFeaturesAvailable(self):
return self._pulseFeaturesAvailable
@property
def barcode(self):
raise Unimplemented()
@property
def barcodeName(self):
raise Unimplemented()
@property
def barcodes(self):
raise Unimplemented()
@requiresBai
def __len__(self):
return self.peer.mapped + self.peer.unmapped
def close(self):
if hasattr(self, "file") and self.file is not None:
self.file.close()
self.file = None
def __enter__(self):
return self
def __exit__(self, exc_type, exc_value, traceback):
self.close()
def main(argv=None):
"""Main script."""
##########################
# COMMAND-LINE ARGUMENTS #
##########################
# Get myself
program_name = sys.argv[0]
if not argv:
argv = sys.argv[1:]
# Get the cluster type used to control arguments
cluster_type = cluster.get_cluster_environment()
parser = argparse.ArgumentParser(
description=__doc__, add_help=False,
epilog=EPILOG, formatter_class=run.CustomFormatter)
req = parser.add_argument_group('Required arguments')
req.add_argument('-m', '--mode',
help='Operation mode', choices=['single', 'multi'],
required=True, metavar='mode')
req.add_argument('-s', '--snps',
help='SNP BED file', required=True, metavar='<BED>')
req.add_argument('-r', '--reads',
help='Mapped reads file [sam or bam]',
required=True, metavar='<[S/B]AM>')
uni = parser.add_argument_group('Universal optional arguments')
uni.add_argument('-p', '--prefix',
help='Prefix for temp files and output', default='TEST',
metavar='')
uni.add_argument('-b', '--bam', action='store_true', dest='bam',
help='Mapped read file type is bam (auto-detected if *.bam)')
uni.add_argument('-n', '--noclean', action='store_true',
help='Do not delete intermediate files (for debuging)')
uni.add_argument('-R', '--random-seed', default=None, type=int,
help='Set the state of the randomizer (for testing)')
uni.add_argument('-h', '--help', action='help',
help='show this help message and exit')
mult = parser.add_argument_group('Multi(plex) mode arguments')
mult.add_argument('-j', '--jobs', type=int,
help='Divide into # of jobs', default=100, metavar='')
if cluster_type == 'slurm' or cluster_type == 'torque':
mult.add_argument('-w', '--walltime',
help='Walltime for each job', default='3:00:00',
metavar='')
mult.add_argument('-k', '--mem', dest='memory', metavar='',
help='Memory for each job', default='5000MB')
mult.add_argument('--queue',
help='Queue to submit jobs to', default='batch',
metavar='')
mult.add_argument('--cluster', choices=['torque', 'slurm', 'normal'],
help='Which cluster to use, normal uses threads ' +
'on this machine', default=cluster_type)
mult.add_argument('--threads', type=int, metavar='', default=cpu_count(),
help='Max number of threads to run at a time ' +
'(normal mode only).')
single = parser.add_argument_group('Single mode arguments')
single.add_argument('-f', '--suffix', default='', metavar='',
help='Suffix for multiplexing [set automatically]')
logging = parser.add_argument_group('Logging options')
logging.add_argument('-q', '--quiet', action='store_true',
help="Quiet mode, only prints warnings.")
logging.add_argument('-v', '--verbose', action='store_true',
help="Verbose mode, prints debug info too.")
logging.add_argument('--logfile',
help='Logfile to write messages too, default is ' +
'STDERR')
args = parser.parse_args()
if args.random_seed is not None:
random.seed(args.random_seed)
print("Seed: ", args.random_seed, random.getstate()[1][:10])
###########################################################################
# File Preparations #
###########################################################################
# Take care of logging
if args.logfile:
logme.LOGFILE = args.logfile
if args.quiet:
logme.MIN_LEVEL = 'warn'
elif args.verbose:
logme.MIN_LEVEL = 'debug'
# Initialize variables
prefix = args.prefix + '_'
# Make sure we can run ourselves
if not run.is_exe(program_name):
program_name = run.which(parser.prog)
# Set the cluster type if we are in multi mode
if args.mode == 'multi' and (cluster_type == 'slurm'
or cluster_type == 'torque'):
cluster.QUEUE = args.cluster
# Check if the read file is sam or bam
file_check = args.reads.split('.')
file_check[-1] = file_check[-1].lower()
sam_path, sam_file = os.path.split(args.reads)
if args.reads.endswith('bam') or args.bam:
mode = 'rb'
else:
mode = 'r'
##################
# MULTIPLEX MODE #
##################
# If we're running in multiplex mode
if args.mode == 'multi':
logme.log('Splitting sam file {} into {} files.'.format(sam_file,
args.jobs))
reads_files = split_samfile(os.path.join(sam_path, sam_file),
args.jobs, prefix)
logme.log('Splitting complete.')
# Create PBS scripts and submit jobs to the cluster
subnoclean = ' --noclean' if args.noclean else ''
logme.log('Submitting split files to cluster')
jobs = [] # Hold job info for later checking
for reads_file in reads_files:
suffix = reads_file[-4:]
command = ("python2 " + program_name + " --mode single --snps " +
args.snps + " --reads " + reads_file + " --suffix " +
suffix + " --prefix " + args.prefix + subnoclean +
' --bam')
if cluster_type == 'normal':
jobs.append(cluster.submit(command, name=prefix + suffix,
threads=args.threads))
else:
jobs.append(cluster.submit(command, name=prefix + suffix,
time=args.walltime, cores=1,
mem=args.memory,
partition=args.queue))
sleep(2) # Pause for two seconds to make sure job is submitted
# Now wait and check for all jobs to complete every so long
logme.log('Submission done, waiting for jobs to complete.')
# First wait for jobs in queue to complete
cluster.wait(jobs)
sleep(1)
# Next, check if any jobs failed
failed = []
for i in range(1, args.jobs+1):
suffix = str(i).zfill(4)
if not os.path.isfile(prefix + suffix + '_done'):
failed.append(prefix + suffix)
# If any jobs failed, terminate
if failed:
logme.log('Some jobs failed!', 'critical')
return -1
logme.log('Jobs completed.')
# Remove 'done' files in case we want to run again.
os.system('rm {prefix}*_done'.format(prefix=prefix))
# Once the jobs are done, concatenate all of the counts into one file.
# Initialize dictionaries
tot_pos_counts = {}
tot_neg_counts = {}
tot_tot_counts = {}
tot_sum_pos = {}
tot_sum_neg = {}
for i in range(1, args.jobs+1):
suffix = str(i).zfill(4)
in_counts = prefix + 'SNP_COUNTS_' + suffix
# Parse the line to add it to the total file
with run.open_zipped(in_counts, 'r') as in_counts:
for line in in_counts:
line = line.rstrip('\n')
line_t = line.split('\t')
if 'CHR' in line:
continue
pos = line_t[0] + '|' + line_t[1]
pos_split = line_t[2].split('|')
neg_split = line_t[3].split('|')
if pos in tot_pos_counts or pos in tot_neg_counts or pos in tot_tot_counts:
for j in range(len(pos_split)):
tot_pos_counts[pos][j] += int(pos_split[j])
tot_neg_counts[pos][j] += int(neg_split[j])
tot_sum_pos[pos] += int(line_t[4])
tot_sum_neg[pos] += int(line_t[5])
tot_tot_counts[pos] += int(line_t[6])
else:
tot_pos_counts[pos] = [0, 0, 0, 0]
tot_neg_counts[pos] = [0, 0, 0, 0]
tot_tot_counts[pos] = 0
tot_sum_pos[pos] = 0
tot_sum_neg[pos] = 0
for j in range(len(pos_split)):
tot_pos_counts[pos][j] += int(pos_split[j])
tot_neg_counts[pos][j] += int(neg_split[j])
tot_sum_pos[pos] += int(line_t[4])
tot_sum_neg[pos] += int(line_t[5])
tot_tot_counts[pos] += int(line_t[6])
# Write out the final concatenated file
with run.open_zipped(prefix + 'SNP_COUNTS.txt', 'w') as final_counts:
final_counts.write('CHR\tPOSITION\tPOS_A|C|G|T\tNEG_A|C|G|T\t' +
'SUM_POS_READS\tSUM_NEG_READS\tSUM_READS\n')
keys = sorted(tot_pos_counts.keys())
for key in keys:
pos = key.split('|')
pos_fix = [str(x) for x in tot_pos_counts[key]]
neg_fix = [str(x) for x in tot_neg_counts[key]]
pos_out = '|'.join(pos_fix)
neg_out = '|'.join(neg_fix)
final_counts.write(str(pos[0]) + '\t' + str(pos[1]) + '\t' +
pos_out + '\t' + neg_out + '\t' +
str(tot_sum_pos[key]) + '\t' +
str(tot_sum_neg[key]) + '\t' +
str(tot_tot_counts[key]) + '\n')
# Sort the file numerically
os.system('sort -k1,2 -n ' + prefix + 'SNP_COUNTS.txt ' + ' -o ' +
prefix + 'SNP_COUNTS.txt')
# Clean up intermediate files.
if args.noclean is False:
cluster.clean()
os.system('rm {prefix}*COUNTS_* {prefix}*split_sam_*'.format(
prefix=prefix))
###############
# SINGLE MODE #
###############
# If we're running in single mode (each job submitted by multiplex mode
# will be running in single mode)
elif args.mode == 'single':
# First read in the information on the SNPs that we're interested in.
snps = {} # Initialize a dictionary of SNP positions
with run.open_zipped(args.snps) as snp_file:
for line in snp_file:
line = line.rstrip('\n')
line_t = line.split('\t')
pos = chrom_to_num(line_t[0]) + '|' + str(line_t[2])
snps[pos] = line_t[3]
# This is the dictionary of potential SNPs for each read.
potsnp_dict = {}
# Now parse the SAM file to extract only reads overlapping SNPs.
in_sam = Samfile(args.reads, mode)
references = in_sam.references # Faster to make a copy of references.
# Trackers to count how many reads are lost at each step
indel_skip = 0
nosnp_skip = 0
count = 0
snp_count = 0
ryo_filter = 0
for line in in_sam:
count += 1
# Skip lines that overlap indels OR don't match Ns
cigarstring = line.cigarstring
if 'D' in cigarstring or 'I' in cigarstring:
indel_skip += 1
continue
# Split the tags to find the MD tag:
tags = line.tags
for tagname, tagval in tags:
if tagname == 'MD' and 'N' in tagval:
# Remember that, for now, we're not allowing reads that
# overlap insertions/deletions.
chrom = references[line.rname]
pos = line.pos
read = line.seq
# We're assuming
# correct mapping such that FIRST MATES on the NEGATIVE
# STRAND are NEGATIVE, while SECOND MATES on the NEGATIVE
# STRAND are POSITIVE.
if line.is_reverse:
orientation = '-'
else:
orientation = '+'
# Parse the CIGAR string
cigar_types, cigar_vals = split_CIGAR(cigarstring)
if cigar_types[0] == 'S':
MD_start = int(cigar_vals[0])
else:
MD_start = 0
# Get the genomic positions corresponding to each base-pair
# of the read
read_genomic_positions = CIGAR_to_Genomic_Positions(
cigar_types, cigar_vals, line.pos+1)
# Get the tag data
MD_split = re.findall('\d+|\D+', tagval)
genome_start = 0
# The snp_pos dictionary will store the 1-base position
# => allele
snp_pos = {}
for i in MD_split:
if re.match('\^', i):
pass
elif i.isalpha():
if i == 'N':
snp_pos[read_genomic_positions[genome_start]] = read[MD_start]
MD_start += 1
genome_start += 1
else:
MD_start += 1
genome_start += 1
else:
MD_start += int(i)
genome_start += int(i)
for i in snp_pos:
snp_count += 1
# RYO: START EDIT - Implemented Filter
posVal = line.reference_name + '|' + str(i)
if posVal not in snps:
nosnp_skip += 1
continue
# RYO: END EDIT - Implmented Filter
snp = '{chr}|{i}\t{snp_pos}\t{orientation}'.format(
chr=chrom, i=i, snp_pos=snp_pos[i],
orientation=orientation)
if line.qname in potsnp_dict:
if snp not in potsnp_dict[line.qname]:
# RYO EDIT HERE - added conditional so that
# pairs of reads are not considered twice if
# they both overlap the same snp.
potsnp_dict[line.qname].append(snp)
else:
ryo_filter += 1
else:
potsnp_dict[line.qname] = []
potsnp_dict[line.qname].append(snp)
in_sam.close()
# Log all of the skipped reads
logme.log('Total reads: {}'.format(count), 'debug')
logme.log('Reads skipped for indels: {}'.format(indel_skip), 'debug')
logme.log('Total SNPs checked: {}'.format(snp_count), 'debug')
logme.log('SNPs not in SNP list: {}'.format(nosnp_skip), 'debug')
logme.log('Ryo filter: {}'.format(ryo_filter), 'debug')
# Initialize the counting dictionaries
pos_counts = {}
neg_counts = {}
# Go through the potential SNP dictionary and choose one SNP at random
# for those overlapping multiple SNPs
if args.random_seed is not None: # Dictionaries are unordered, so must sort for consistent random seed output.
keys = sorted(list(potsnp_dict.keys()))
else: # Because sorting is slow, only do it if random seed is set, slowdown is about 0.1s per 1 million reads..
keys = list(potsnp_dict.keys())
for key in keys:
snp = random.choice(potsnp_dict[key]).split('\t')
if snp[0] in snps:
if snp[0] in pos_counts or snp[0] in neg_counts:
if snp[2] == '+':
if snp[1] == 'A':
pos_counts[snp[0]][0] += 1
if snp[1] == 'C':
pos_counts[snp[0]][1] += 1
if snp[1] == 'G':
pos_counts[snp[0]][2] += 1
if snp[1] == 'T':
pos_counts[snp[0]][3] += 1
elif snp[2] == '-':
if snp[1] == 'A':
neg_counts[snp[0]][0] += 1
if snp[1] == 'C':
neg_counts[snp[0]][1] += 1
if snp[1] == 'G':
neg_counts[snp[0]][2] += 1
if snp[1] == 'T':
neg_counts[snp[0]][3] += 1
else:
pos_counts[snp[0]] = [0, 0, 0, 0]
neg_counts[snp[0]] = [0, 0, 0, 0]
if snp[2] == '+':
if snp[1] == 'A':
pos_counts[snp[0]][0] += 1
if snp[1] == 'C':
pos_counts[snp[0]][1] += 1
if snp[1] == 'G':
pos_counts[snp[0]][2] += 1
if snp[1] == 'T':
pos_counts[snp[0]][3] += 1
elif snp[2] == '-':
if snp[1] == 'A':
neg_counts[snp[0]][0] += 1
if snp[1] == 'C':
neg_counts[snp[0]][1] += 1
if snp[1] == 'G':
neg_counts[snp[0]][2] += 1
if snp[1] == 'T':
neg_counts[snp[0]][3] += 1
# Open the output file and write the SNP counts to it
out_counts = prefix + 'SNP_COUNTS_' + args.suffix if args.suffix \
else prefix + 'SNP_COUNTS.txt'
with open(out_counts, 'w') as out_counts:
# Write header
out_counts.write('CHR\tPOSITION\tPOS_A|C|G|T\tNEG_A|C|G|T\t' +
'SUM_POS_READS\tSUM_NEG_READS\tSUM_READS\n')
# Sort SNP positions and write them
keys = sorted(pos_counts.keys())
for key in keys:
pos = key.split('|')
sum_pos = sum(pos_counts[key])
sum_neg = sum(neg_counts[key])
tot_sum = sum(pos_counts[key]) + sum(neg_counts[key])
pos_fix = [str(x) for x in pos_counts[key]]
neg_fix = [str(x) for x in neg_counts[key]]
positive = '|'.join(pos_fix)
negative = '|'.join(neg_fix)
out_counts.write(pos[0] + '\t' + pos[1] + '\t' + positive +
'\t' + negative + '\t' + str(sum_pos) + '\t' +
str(sum_neg) + '\t' + str(tot_sum) + '\n')
if args.suffix:
os.system('touch ' + prefix + args.suffix + '_done')
def get_raw_signal(arguments):
(mpbs_name, mpbs_file1, mpbs_file2, reads_file1, reads_file2, organism,
window_size, forward_shift, reverse_shift) = arguments
mpbs1 = GenomicRegionSet("Motif Predicted Binding Sites of Condition1")
mpbs1.read(mpbs_file1)
mpbs2 = GenomicRegionSet("Motif Predicted Binding Sites of Condition2")
mpbs2.read(mpbs_file2)
mpbs = mpbs1.combine(mpbs2, output=True)
mpbs.sort()
bam1 = Samfile(reads_file1, "rb")
bam2 = Samfile(reads_file2, "rb")
genome_data = GenomeData(organism)
fasta = Fastafile(genome_data.get_genome())
signal_1 = np.zeros(window_size)
signal_2 = np.zeros(window_size)
motif_len = None
pwm = dict([("A", [0.0] * window_size), ("C", [0.0] * window_size),
("G", [0.0] * window_size), ("T", [0.0] * window_size),
("N", [0.0] * window_size)])
mpbs_regions = mpbs.by_names([mpbs_name])
num_motif = len(mpbs_regions)
for region in mpbs_regions:
if motif_len is None:
motif_len = region.final - region.initial
mid = (region.final + region.initial) / 2
p1 = mid - window_size / 2
p2 = mid + window_size / 2
if p1 <= 0:
continue
# Fetch raw signal
for read in bam1.fetch(region.chrom, p1, p2):
# check if the read is unmapped, according to issue #112
if read.is_unmapped:
continue
if not read.is_reverse:
cut_site = read.pos + forward_shift
if p1 <= cut_site < p2:
signal_1[cut_site - p1] += 1.0
else:
cut_site = read.aend + reverse_shift - 1
if p1 <= cut_site < p2:
signal_1[cut_site - p1] += 1.0
for read in bam2.fetch(region.chrom, p1, p2):
# check if the read is unmapped, according to issue #112
if read.is_unmapped:
continue
if not read.is_reverse:
cut_site = read.pos + forward_shift
if p1 <= cut_site < p2:
signal_2[cut_site - p1] += 1.0
else:
cut_site = read.aend + reverse_shift - 1
if p1 <= cut_site < p2:
signal_2[cut_site - p1] += 1.0
update_pwm(pwm, fasta, region, p1, p2)
return signal_1, signal_2, motif_len, pwm, num_motif
def split_samfile(sam_file, splits, prefix='', path=''):
"""Take a sam file and split it splits number of times.
:path: Where to put the split files.
:prefix: A prefix for the outfile names.
:returns: A tuple of job files.
"""
# Determine how many reads will be in each split sam file.
num_lines = count_reads(sam_file)
num_reads = int(int(num_lines)/splits) + 1
# Get rid of starting path
sam_name = os.path.basename(sam_file)
# Subset the SAM file into X number of jobs
cnt = 0
currjob = 1
suffix = '.split_sam_' + str(currjob).zfill(4)
run_file = os.path.join(path, prefix + sam_name + suffix)
rmode = 'rb' if sam_name.split('.')[0] == 'bam' else 'r'
wmode = 'wb'
# Actually split the file
outfiles = [run_file]
with Samfile(sam_file, rmode) as in_sam:
sam_split = Samfile(run_file, wmode, template=in_sam)
for line in in_sam:
cnt += 1
if cnt < num_reads:
sam_split.write(line)
elif cnt == num_reads:
# Check if next line is mate-pair. If so, don't split.
line2 = next(in_sam)
currjob += 1
suffix = '.split_sam_' + str(currjob).zfill(4)
run_file = os.path.join(path, prefix + sam_name + suffix)
new_sam = Samfile(run_file, wmode, template=in_sam)
outfiles.append(run_file)
if line.qname == line2.qname:
sam_split.write(line)
sam_split.write(line2)
sam_split.close()
cnt = 0
else:
sam_split.write(line)
sam_split.close()
new_sam.write(line2)
cnt = 0
sam_split = new_sam
sam_split.close()
return tuple(outfiles)
def parse_sam(f_names1, f_names2, frags, out_file1, out_file2, genome_seq,
re_name, verbose=False, **kwargs):
"""
Parse sam/bam file using pysam tools.
Keep a summary of the results into 2 tab-separated files that will contain 6
columns: read ID, Chromosome, position, strand (either 0 or 1), mapped
sequence lebgth, position of the closest upstream RE site, position of
the closest downstream RE site
:param f_names1: a list of path to sam/bam files corresponding to the
mapping of read1, can also be just one file
:param f_names1: a list of path to sam/bam files corresponding to the
mapping of read2, can also be just one file
:param frags: a dictionary generated by :func:`pyatdbit.mapping.restriction_enzymes.map_re_sites`.
"""
frags = map_re_sites(re_name, genome_seq, verbose=True)
frag_chunk = kwargs.get('frag_chunk', 100000)
fnames = f_names1, f_names2
outfiles = out_file1, out_file2
for read in range(2):
if verbose:
print 'Loading read' + str(read + 1)
reads = []
for fnam in fnames[read]:
if verbose:
print 'loading file:', fnam
try:
fhandler = Samfile(fnam)
except IOError:
continue
i = 0
crm_dict = {}
while True:
try:
crm_dict[i] = fhandler.getrname(i).replace('chr', '')
i += 1
except ValueError:
break
for r in fhandler:
if r.is_unmapped:
continue
if r.tags[1][1] != 1:
continue
positive = not r.is_reverse
crm = crm_dict[r.tid]
len_seq = len(r.seq)
pos = r.pos + (0 if positive else len_seq)
try:
frag_piece = frags[crm][pos / frag_chunk]
except KeyError:
# Chromosome not in hash
continue
idx = bisect(frag_piece, pos)
prev_re = frag_piece[idx - 1]
next_re = frag_piece[idx]
name = r.qname
reads.append('%s\t%s\t%d\t%d\t%d\t%d\t%d\n' % (
name, crm, pos, positive, len_seq, prev_re, next_re))
reads_fh = open(outfiles[read], 'w')
reads_fh.write(''.join(sorted(reads)))
reads_fh.close()
del(reads)
from progressbar import ProgressBar
import pandas as pd
try:
import matplotlib.pyplot as mpl
mpl.figure()
mpl.plot([1,2])
mpl.close()
has_mpl = True
except:
has_mpl = False
from pysam import Samfile
if __name__ == "__main__":
window_size = int(10e3)
mel = Samfile('analysis/on_mel/mel_gdna_bowtie2_dedup.bam')
sim = Samfile('analysis/on_mel/sim_gdna_bowtie2_dedup.bam')
posns = []
mel_covs = []
sim_covs = []
prog = 0
pbar = ProgressBar(max_value=sum(mel.lengths) + 1)
for r, l in zip(mel.references, mel.lengths):
for start in range(0, l, window_size):
posns.append('{}_{}'.format(r, start))
mel_covs.append(sum(sum(i) for i in
mel.count_coverage(r, start, start+window_size)))
sim_covs.append(sum(sum(i) for i in
sim.count_coverage(r, start, start+window_size)))
prog += l
def ace(args):
"""
%prog ace bamfile fastafile
convert bam format to ace format. This often allows the remapping to be
assessed as a denovo assembly format. bam file needs to be indexed. also
creates a .mates file to be used in amos/bambus, and .astat file to mark
whether the contig is unique or repetitive based on A-statistics in Celera
assembler.
"""
p = OptionParser(ace.__doc__)
p.add_option("--splitdir", dest="splitdir", default="outRoot",
help="split the ace per contig to dir [default: %default]")
p.add_option("--unpaired", dest="unpaired", default=False,
help="remove read pairs on the same contig [default: %default]")
p.add_option("--minreadno", dest="minreadno", default=3, type="int",
help="minimum read numbers per contig [default: %default]")
p.add_option("--minctgsize", dest="minctgsize", default=100, type="int",
help="minimum contig size per contig [default: %default]")
p.add_option("--astat", default=False, action="store_true",
help="create .astat to list repetitiveness [default: %default]")
p.add_option("--readids", default=False, action="store_true",
help="create file of mapped and unmapped ids [default: %default]")
from pysam import Samfile
opts, args = p.parse_args(args)
if len(args) != 2:
sys.exit(not p.print_help())
bamfile, fastafile = args
astat = opts.astat
readids = opts.readids
f = Fasta(fastafile)
prefix = bamfile.split(".")[0]
acefile = prefix + ".ace"
readsfile = prefix + ".reads"
astatfile = prefix + ".astat"
logging.debug("Load {0}".format(bamfile))
s = Samfile(bamfile, "rb")
ncontigs = s.nreferences
genomesize = sum(x for a, x in f.itersizes())
logging.debug("Total {0} contigs with size {1} base".format(ncontigs,
genomesize))
qual = "20" # default qual
totalreads = sum(s.count(x) for x in s.references)
logging.debug("Total {0} reads mapped".format(totalreads))
fw = open(acefile, "w")
if astat:
astatfw = open(astatfile, "w")
if readids:
readsfw = open(readsfile, "w")
print >> fw, "AS {0} {1}".format(ncontigs, totalreads)
print >> fw
for i, contig in enumerate(s.references):
cseq = f[contig]
nbases = len(cseq)
mapped_reads = [x for x in s.fetch(contig) if not x.is_unmapped]
nreads = len(mapped_reads)
nsegments = 0
print >> fw, "CO {0} {1} {2} {3} U".format(contig, nbases, nreads,
nsegments)
print >> fw, fill(str(cseq.seq))
print >> fw
if astat:
astat = Astat(nbases, nreads, genomesize, totalreads)
print >> astatfw, "{0}\t{1:.1f}".format(contig, astat)
text = fill([qual] * nbases, delimiter=" ", width=30)
print >> fw, "BQ\n{0}".format(text)
print >> fw
rnames = []
for a in mapped_reads:
readname = a.qname
rname = readname
if readids:
print >> readsfw, readname
rnames.append(rname)
strand = "C" if a.is_reverse else "U"
paddedstart = a.pos + 1 # 0-based to 1-based
af = "AF {0} {1} {2}".format(rname, strand, paddedstart)
print >> fw, af
print >> fw
for a, rname in zip(mapped_reads, rnames):
aseq, npadded = cigar_to_seq(a)
if aseq is None:
continue
ninfos = 0
ntags = 0
alen = len(aseq)
rd = "RD {0} {1} {2} {3}\n{4}".format(rname, alen, ninfos, ntags,
fill(aseq))
qs = "QA 1 {0} 1 {0}".format(alen)
print >> fw, rd
print >> fw
print >> fw, qs
print >> fw
class _BamReaderBase(object):
"""
The BamReader class provides a high-level interface to PacBio BAM
files. If a PacBio BAM index (bam.pbi file) is present and the
user instantiates the BamReader using the reference FASTA as the
second argument, the BamReader will provide an interface
compatible with CmpH5Reader.
"""
def _loadReferenceInfo(self):
refRecords = self.peer.header["SQ"]
refNames = [r["SN"] for r in refRecords]
refLengths = [r["LN"] for r in refRecords]
refMD5s = [r["M5"] for r in refRecords]
refIds = map(self.peer.gettid, refNames)
nRefs = len(refRecords)
self._referenceInfoTable = np.rec.fromrecords(zip(
refIds,
refIds,
refNames,
refNames,
refLengths,
refMD5s,
np.zeros(nRefs, dtype=np.uint32),
np.zeros(nRefs, dtype=np.uint32)),
dtype=[('ID', '<i8'), ('RefInfoID', '<i8'),
('Name', 'O'), ('FullName', 'O'),
('Length', '<i8'), ('MD5', 'O'),
('StartRow', '<u4'), ('EndRow', '<u4')])
self._referenceDict = {}
self._referenceDict.update(zip(refIds, self._referenceInfoTable))
self._referenceDict.update(zip(refNames, self._referenceInfoTable))
def _loadReadGroupInfo(self):
rgs = self.peer.header["RG"]
readGroupTable_ = []
pulseFeaturesInAll_ = frozenset(PULSE_FEATURE_TAGS.keys())
for rg in rgs:
# Regarding RG ID: BLASR currently outputs a hex digest of
# 10 nibbles, instead of the 8 which would fit into a
# 32-bit word. So we truncate here for the purposes of
# cross-referencing within this API and the PacBioBamIndex
# API. We do check for a collision below.
rgID = int(rg["ID"][:8], 16)
rgName = rg["PU"]
ds = dict([pair.split("=") for pair in rg["DS"].split(";") if pair != ""])
triple = ds["BINDINGKIT"], ds["SEQUENCINGKIT"], ds["SOFTWAREVERSION"]
rgChem = decodeTriple(*triple)
rgReadType = ds["READTYPE"]
readGroupTable_.append((rgID, rgName, rgReadType, rgChem))
pulseFeaturesInAll_ = pulseFeaturesInAll_.intersection(ds.keys())
self._readGroupTable = np.rec.fromrecords(
readGroupTable_,
dtype=[("ID" , np.uint32),
("MovieName" , "O"),
("ReadType" , "O"),
("SequencingChemistry", "O")])
assert len(set(self._readGroupTable.ID)) == len(self._readGroupTable), \
"First 8 chars of read group IDs must be unique!"
self._readGroupDict = { rg.ID : rg
for rg in self._readGroupTable }
self._pulseFeaturesAvailable = pulseFeaturesInAll_
def _loadProgramInfo(self):
# TODO: guarantee that these fields are nonoptional in our bams --- check with Marcus
# TODO: are we interesting in the PP info?
self._programTable = np.rec.fromrecords(
[ (pg["ID"], pg.get("VN", None), pg.get("CL", None))
for pg in self.peer.header["PG"] ],
dtype=[("ID" , "O"),
("Version", "O"),
("CommandLine", "O")])
def _loadReferenceFasta(self, referenceFastaFname):
ft = FastaTable(referenceFastaFname)
# Verify that this FASTA is in agreement with the BAM's
# reference table---BAM should be a subset.
fastaIdsAndLens = set((c.id, c.length) for c in ft)
bamIdsAndLens = set((c.Name, c.Length) for c in self.referenceInfoTable)
if not bamIdsAndLens.issubset(fastaIdsAndLens):
raise ReferenceMismatch, "FASTA file must contain superset of reference contigs in BAM"
self.referenceFasta = ft
def __init__(self, fname, referenceFastaFname=None):
self.filename = fname = abspath(expanduser(fname))
self.peer = Samfile(fname, "rb")
# Check for sortedness, index.
# There doesn't seem to be a "public" way to do this right
# now, but that's fine because we're going to have to rewrite
# it all anyway once the pysam rewrite lands.
if not self.peer._hasIndex:
raise ValueError, "Specified bam file lacks a bam index---required for this API"
self._loadReferenceInfo()
self._loadReadGroupInfo()
self._loadProgramInfo()
self.referenceFasta = None
if referenceFastaFname is not None:
self._loadReferenceFasta(referenceFastaFname)
@property
def isIndexLoaded(self):
return self.index is not None
@property
def isReferenceLoaded(self):
return self.referenceFasta is not None
def attach(self, fofnFilename):
self.basH5Collection = BasH5Collection(fofnFilename)
@property
def moviesAttached(self):
return (self.basH5Collection is not None)
@property
def alignmentIndex(self):
raise UnavailableFeature("BAM has no alignment index")
#TODO: change concept to readGroupTable in cmp.h5
@property
def movieInfoTable(self):
raise Unimplemented()
# TODO: change to read group accessor, this is semantically wrong now
def movieInfo(self, movieId):
raise Unimplemented()
@property
def movieNames(self):
return set([mi.MovieName for mi in self.readGroupTable])
@property
def readGroupTable(self):
return self._readGroupTable
def readGroup(self, readGroupId):
return self._readGroupDict[readGroupId]
@property
def sequencingChemistry(self):
"""
List of the sequencing chemistries by movie. Order is
unspecified.
"""
return list(self.readGroupTable.SequencingChemistry)
#TODO: elide "Info" innames?
@property
def referenceInfoTable(self):
return self._referenceInfoTable
#TODO: standard? how about subread instead? why capitalize ccs?
# can we standardize this? is cDNA an additional possibility
@property
def readType(self):
"""
Either "standard", "CCS", "mixed", or "unknown", to represent the
type of PacBio reads aligned in this BAM file.
"""
readTypes = self.readGroupTable.ReadType
if all(readTypes == "SUBREAD"):
return "standard"
elif all(readTypes == "CCS"):
return "CCS"
elif all((readTypes == "CCS") | (readTypes == "SUBREAD")):
return "mixed"
else:
return "unknown"
#TODO: Marcus needs to put something in the spec for this
@property
def version(self):
raise Unimplemented()
#TODO: Marcus needs to put something in the spec for this
def versionAtLeast(self, minimalVersion):
raise Unimplemented()
def softwareVersion(self, programName):
raise Unimplemented()
@property
def isSorted(self):
return True
@property
def isBarcoded(self):
raise Unimplemented()
@property
def isEmpty(self):
return (len(self) == 0)
# TODO: make this private in cmp.h5 reader
def alignmentGroup(self, alnGroupId):
raise UnavailableFeature("BAM has no HDF5 groups")
def referenceInfo(self, key):
return self._referenceDict[key]
def atOffset(self, offset):
self.peer.seek(offset)
return BamAlignment(self, next(self.peer))
def hasPulseFeature(self, featureName):
return featureName in self._pulseFeaturesAvailable
def pulseFeaturesAvailable(self):
return self._pulseFeaturesAvailable
@property
def barcode(self):
raise Unimplemented()
@property
def barcodeName(self):
raise Unimplemented()
@property
def barcodes(self):
raise Unimplemented()
def __repr__(self):
return "<%s for %s>" % (type(self).__name__, self.filename)
def __len__(self):
return self.peer.mapped
def close(self):
if hasattr(self, "file") and self.file is not None:
self.file.close()
self.file = None
def __enter__(self):
return self
def __exit__(self, exc_type, exc_value, traceback):
self.close()
