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 main():
cArgs = args()
root = os.environ["RGTDATA"] if "RGTDATA" in os.environ else "/rgtdata"
if not os.path.exists(root + "/{assembly}/genome_{assembly}.fa".format(assembly = cArgs.assembly)):
print(
"WARNING: genomic data is not present for {assembly}. We will attempt to download it.".format(assembly = cArgs.assembly),
file = sys.stderr
)
print(
"If you are running many jobs, they might run faster if you mount the appropriate data at {root}/{assembly}.".format(root = root, assembly = cArgs.assembly),
file = sys.stderr
)
result = os.system("python3 /reg-gen/data/setupGenomicData.py --{assembly}".format(assembly = cArgs.assembly))
if result != 0:
print("FATAL: Unable to load genome data for {assembly}.".format(assembly = cArgs.assembly), file = sys.stderr)
return 1
if cArgs.occurrence_threshold is None:
signal = footprint(cArgs.bam, cArgs.bed, cArgs.assembly, cArgs.ext_size, cArgs.dnase, cArgs.bias_type)
else:
g = GenomeData(organism = cArgs.assembly)
with FilteredRegions(cArgs.bed, cArgs.occurrence_threshold, g.get_genome(), g.get_chromosome_sizes()) as b:
signal = footprint(cArgs.bam, b.name, cArgs.assembly, cArgs.ext_size, cArgs.dnase, cArgs.bias_type)
if cArgs.aggregate or cArgs.plot_output is not None:
signal = aggregate(signal, (lambda x: "all") if cArgs.occurrence_threshold is None else lambda x: x["name"], cArgs.ext_size)
if cArgs.plot_output is not None:
plot(signal["all"]["forward"], signal["all"]["reverse"], cArgs.font, cArgs.plot_output)
if cArgs.output_file is None:
if not cArgs.output_as_tsv or not cArgs.aggregate:
print(json.dumps(signal))
else:
for k, v in signal.items():
if k != "all":
print("%s\t%s\t%s" % (k, ','.join([ str(x) for x in v["forward"] ]), ','.join([ str(x) for x in v["reverse"] ])))
else:
with open(cArgs.output_file, 'w') as o:
if not cArgs.output_as_tsv or not cArgs.aggregate:
o.write(json.dumps(signal) + '\n')
else:
for k, v in signal.items():
if k != "all":
o.write("%s\t%s\t%s\n" % (k, ','.join([ str(x) for x in v["forward"] ]), ','.join([ str(x) for x in v["reverse"] ])))
return 0
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)
import pandas as pd
from pysam import Samfile
from rgt.GenomicRegionSet import GenomicRegionSet
from rgt.Util import GenomeData
tf_file = sys.argv[1]
bam_file = sys.argv[2]
output_file = sys.argv[3]
gr_tfs = GenomicRegionSet(name="TFs")
gr_tfs.read(filename=tf_file)
gr_genes = gr_tfs.gene_association(organism="hg38")
# Fetching chromosome sizes
genome_data = GenomeData("hg38")
chrom_sizes_file_name = genome_data.get_chromosome_sizes()
chrom_sizes_file = open(chrom_sizes_file_name, "r")
chrom_sizes_dict = dict()
for chrom_sizes_entry_line in chrom_sizes_file:
chrom_sizes_entry_vec = chrom_sizes_entry_line.strip().split("\t")
chrom_sizes_dict[chrom_sizes_entry_vec[0]] = int(chrom_sizes_entry_vec[1])
chrom_sizes_file.close()
bam = Samfile(bam_file, "rb")
tf_list = list()
gene_list = list()
tc_list = list()
for i, r in enumerate(gr_tfs):
tf = r.name.split(".")[-1]
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)
