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 merge_delete(ext_size, merge, peak_list, pvalue_list):
# peaks_gain = read_diffpeaks(path)
regions_plus = GenomicRegionSet('regions') #pot. mergeable
regions_minus = GenomicRegionSet('regions') #pot. mergeable
regions_unmergable = GenomicRegionSet('regions')
last_orientation = ""
for i, t in enumerate(peak_list):
chrom, start, end, c1, c2, strand, ratio = t[0], t[1], t[2], t[3], t[4], t[5], t[6]
r = GenomicRegion(chrom = chrom, initial = start, final = end, name = '', \
orientation = strand, data = str((c1, c2, pvalue_list[i], ratio)))
if end - start > ext_size:
if strand == '+':
if last_orientation == '+':
region_plus.add(r)
else:
regions_unmergable.add(r)
elif strand == '-':
if last_orientation == '-':
region_mins.add(r)
else:
regions_unmergable.add(r)
if merge:
regions_plus.extend(ext_size/2, ext_size/2)
regions_plus.merge()
regions_plus.extend(-ext_size/2, -ext_size/2)
merge_data(regions_plus)
regions_minus.extend(ext_size/2, ext_size/2)
regions_minus.merge()
regions_minus.extend(-ext_size/2, -ext_size/2)
merge_data(regions_minus)
results = GenomicRegionSet('regions')
for el in regions_plus:
results.add(el)
for el in regions_minus:
results.add(el)
for el in regions_unmergable:
results.add(el)
results.sort()
return results
def merge_delete(ext_size, merge, peak_list, pvalue_list):
# peaks_gain = read_diffpeaks(path)
regions_plus = GenomicRegionSet('regions') #pot. mergeable
regions_minus = GenomicRegionSet('regions') #pot. mergeable
regions_unmergable = GenomicRegionSet('regions')
last_orientation = ""
for i, t in enumerate(peak_list):
chrom, start, end, c1, c2, strand, ratio = t[0], t[1], t[2], t[3], t[
4], t[5], t[6]
r = GenomicRegion(chrom = chrom, initial = start, final = end, name = '', \
orientation = strand, data = str((c1, c2, pvalue_list[i], ratio)))
if end - start > ext_size:
if strand == '+':
if last_orientation == '+':
region_plus.add(r)
else:
regions_unmergable.add(r)
elif strand == '-':
if last_orientation == '-':
region_mins.add(r)
else:
regions_unmergable.add(r)
if merge:
regions_plus.extend(ext_size / 2, ext_size / 2)
regions_plus.merge()
regions_plus.extend(-ext_size / 2, -ext_size / 2)
merge_data(regions_plus)
regions_minus.extend(ext_size / 2, ext_size / 2)
regions_minus.merge()
regions_minus.extend(-ext_size / 2, -ext_size / 2)
merge_data(regions_minus)
results = GenomicRegionSet('regions')
for el in regions_plus:
results.add(el)
for el in regions_minus:
results.add(el)
for el in regions_unmergable:
results.add(el)
results.sort()
return results
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 get_bc_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.")
regions = GenomicRegionSet("Interested regions")
regions.read(args.input_files[1])
regions.merge()
reads_file = GenomicSignal()
bam = Samfile(args.input_files[0], "rb")
genome_data = GenomeData(args.organism)
fasta = Fastafile(genome_data.get_genome())
hmm_data = HmmData()
if args.bias_table:
bias_table_list = args.bias_table.split(",")
bias_table = BiasTable().load_table(table_file_name_F=bias_table_list[0],
table_file_name_R=bias_table_list[1])
else:
table_F = hmm_data.get_default_bias_table_F_ATAC()
table_R = hmm_data.get_default_bias_table_R_ATAC()
bias_table = BiasTable().load_table(table_file_name_F=table_F,
table_file_name_R=table_R)
if args.strand_specific:
fname_forward = os.path.join(args.output_location, "{}_forward.wig".format(args.output_prefix))
fname_reverse = os.path.join(args.output_location, "{}_reverse.wig".format(args.output_prefix))
f_forward = open(fname_forward, "a")
f_reverse = open(fname_reverse, "a")
for region in regions:
signal_f, signal_r = reads_file.get_bc_signal_by_fragment_length(
ref=region.chrom, start=region.initial, end=region.final, bam=bam, fasta=fasta, bias_table=bias_table,
forward_shift=args.forward_shift, reverse_shift=args.reverse_shift, min_length=None, max_length=None,
strand=True)
if args.norm:
signal_f = reads_file.boyle_norm(signal_f)
perc = scoreatpercentile(signal_f, 98)
std = np.std(signal_f)
signal_f = reads_file.hon_norm_atac(signal_f, perc, std)
signal_r = reads_file.boyle_norm(signal_r)
perc = scoreatpercentile(signal_r, 98)
std = np.std(signal_r)
signal_r = reads_file.hon_norm_atac(signal_r, perc, std)
f_forward.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_f)]) + "\n")
f_reverse.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_r)]) + "\n")
f_forward.close()
f_reverse.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, "{}_forward.bw".format(args.output_prefix))
os.system(" ".join(["wigToBigWig", fname_forward, chrom_sizes_file, bw_filename, "-verbose=0"]))
os.remove(fname_forward)
bw_filename = os.path.join(args.output_location, "{}_reverse.bw".format(args.output_prefix))
os.system(" ".join(["wigToBigWig", fname_reverse, chrom_sizes_file, bw_filename, "-verbose=0"]))
os.remove(fname_reverse)
else:
output_fname = os.path.join(args.output_location, "{}.wig".format(args.output_prefix))
with open(output_fname, "a") as output_f:
for region in regions:
signal = reads_file.get_bc_signal_by_fragment_length(ref=region.chrom, start=region.initial,
end=region.final,
bam=bam, fasta=fasta, bias_table=bias_table,
forward_shift=args.forward_shift,
reverse_shift=args.reverse_shift,
min_length=None, max_length=None, strand=False)
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 get_bc_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.")
regions = GenomicRegionSet("Interested regions")
regions.read(args.input_files[1])
regions.merge()
reads_file = GenomicSignal()
bam = Samfile(args.input_files[0], "rb")
genome_data = GenomeData(args.organism)
fasta = Fastafile(genome_data.get_genome())
hmm_data = HmmData()
if args.bias_table:
bias_table_list = args.bias_table.split(",")
bias_table = BiasTable().load_table(
table_file_name_F=bias_table_list[0],
table_file_name_R=bias_table_list[1])
else:
table_F = hmm_data.get_default_bias_table_F_ATAC()
table_R = hmm_data.get_default_bias_table_R_ATAC()
bias_table = BiasTable().load_table(table_file_name_F=table_F,
table_file_name_R=table_R)
if args.strand_specific:
fname_forward = os.path.join(
args.output_location, "{}_forward.wig".format(args.output_prefix))
fname_reverse = os.path.join(
args.output_location, "{}_reverse.wig".format(args.output_prefix))
f_forward = open(fname_forward, "a")
f_reverse = open(fname_reverse, "a")
for region in regions:
signal_f, signal_r = reads_file.get_bc_signal_by_fragment_length(
ref=region.chrom,
start=region.initial,
end=region.final,
bam=bam,
fasta=fasta,
bias_table=bias_table,
forward_shift=args.forward_shift,
reverse_shift=args.reverse_shift,
min_length=None,
max_length=None,
strand=True)
if args.norm:
signal_f = reads_file.boyle_norm(signal_f)
perc = scoreatpercentile(signal_f, 98)
std = np.std(signal_f)
signal_f = reads_file.hon_norm_atac(signal_f, perc, std)
signal_r = reads_file.boyle_norm(signal_r)
perc = scoreatpercentile(signal_r, 98)
std = np.std(signal_r)
signal_r = reads_file.hon_norm_atac(signal_r, perc, std)
f_forward.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_f)]) + "\n")
f_reverse.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_r)]) + "\n")
f_forward.close()
f_reverse.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,
"{}_forward.bw".format(args.output_prefix))
os.system(" ".join([
"wigToBigWig", fname_forward, chrom_sizes_file, bw_filename,
"-verbose=0"
]))
os.remove(fname_forward)
bw_filename = os.path.join(
args.output_location,
"{}_reverse.bw".format(args.output_prefix))
os.system(" ".join([
"wigToBigWig", fname_reverse, chrom_sizes_file, bw_filename,
"-verbose=0"
]))
os.remove(fname_reverse)
else:
output_fname = os.path.join(args.output_location,
"{}.wig".format(args.output_prefix))
with open(output_fname, "a") as output_f:
for region in regions:
signal = reads_file.get_bc_signal_by_fragment_length(
ref=region.chrom,
start=region.initial,
end=region.final,
bam=bam,
fasta=fasta,
bias_table=bias_table,
forward_shift=args.forward_shift,
reverse_shift=args.reverse_shift,
min_length=None,
max_length=None,
strand=False)
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 call_peaks(bam, csizes, pval, min_reads, binsize, cfile=None):
'''
Call peaks on bam file using pvalue and binomial model.
Returns GenomeRegionSet with peaks, and CoverageSet with signal.
'''
# make chromsizes region set
rs = get_chrom_sizes_as_genomicregionset(csizes)
print("calculating extension sizes...")
# calculate ext size
ext, _ = get_extension_size(bam, start=0, end=300, stepsize=5)
print("calculating coverage...")
# calc coverage
cov = CoverageSet('coverageset', rs)
cov.coverage_from_bam(bam_file=bam, extension_size=ext, paired_reads=True)
# calculate cov2 for output bw
cov2 = CoverageSet('coverageset2', rs)
cov2.coverage_from_bam(bam_file=bam,
extension_size=ext,
paired_reads=True,
binsize=binsize,
stepsize=binsize // 2)
if cfile is not None:
print(f"Using control file: {cfile}")
control = CoverageSet('contorl', rs)
control.coverage_from_bam(bam_file=cfile, extension_size=ext)
with np.errstate(divide='ignore', invalid='ignore'):
norm_igg(cov, control)
# recalc overall coverage
cov.overall_cov = reduce(lambda x, y: np.concatenate(
(x, y)), [cov.coverage[i] for i in range(len(cov.genomicRegions))])
# total coverage
s = np.sum(cov.overall_cov)
# probability of event, a read in a bin, (avg reads/bin )/libsize
p = np.mean(cov.overall_cov[cov.overall_cov > 0]) / s
# what is the max coverage
maxcov = np.max(cov.overall_cov)
# create dict with probability for each count value
mc = np.arange(0, maxcov + 1, dtype="object")
d = {count: binom_test((count, s - count), p=p) for count in mc}
# create GenomicRegionSet to hold peaks
res = GenomicRegionSet('identified_peaks')
print("calculating peaks...")
# iterate through bins in genome, store peaks
for i, c in enumerate(cov.overall_cov):
if filter_bins(c, d, min_reads):
chrom, s, e = cov.index2coordinates(i, rs)
res.add(GenomicRegion(chrom, s, e + 1, data=d[c]))
# merge ol peaks
res.merge()
# merge peaks within ext dist
rc = res.cluster(ext)
return rc, cov, cov2