def main():
if args.input == stdin:
outprefix = "repeatmasker.out"
else:
if args.output:
outprefix = args.output
else:
outprefix = ('.').join(args.input.split('.')[:-1])
outprefix = metaoutput(outprefix, ".gff.gz")
outfile = metaopen(outprefix, "wt")
print("OUT to GFF converter...")
with metaopen(args.input, "r", buffering=args.buffering) as data:
count = 0
for l in data:
if count < 4:
count += 1
continue
line = l.strip().split()
strand = "+" if line[8] == "+" else "-"
class_family_info = line[10].split("/")
if len(class_family_info) == 1:
class_family_info.append(".")
additional_info = "Class=%s;Family=%s;Matching_repeat=%s;SW_score=%s;Perc_div=%s;Perc_del=%s;Pers_ins=%s"\
% (class_family_info[0], class_family_info[1],
line[9], line[0], line[1], line[2], line[3])
gff_line = "%s\tRepeatMasker\trepeat\t%s\t%s\t.\t%s\t.\t%s\n" % (
line[4], line[5], line[6], strand, additional_info)
outfile.write(gff_line)
def main():
outfile = metaopen(metaoutput(args.output, ".faa"), "wt")
csv = metaopen(args.tabular_file, 'rt').readlines()[1:]
data = Fasta_opener(args.input)
faa_dict = data.parse_sequences_without_join()
geneid_prev = None
count = 0
for ln in csv:
line = ln.strip().split(",")
geneid, length = line[5], int(line[9])
if geneid_prev is None:
geneid_prev = geneid
count, protein = length, line[8][1:-1]
continue
if geneid == geneid_prev:
if length >= count:
count, protein = length, line[8][1:-1]
else:
for k, v in faa_dict.items():
if protein in k:
result_sequence = k + v
outfile.write(result_sequence)
count, protein = 0, line[8][1:-1]
geneid_prev = geneid
def main():
if args.input == stdin:
outprefix = "windowmasker.asnb"
else:
if args.output:
outprefix = args.output
else:
outprefix = ('.').join(args.input.split('.')[:-1])
outprefix = metaoutput(outprefix, ".gff.gz")
outfile = metaopen(outprefix, "wt")
print("INTERVAL to GFF converter...")
with metaopen(args.input, "r", buffering=args.buffering) as data:
count = 0
for l in data:
if l.startswith(">"):
line = l.strip().split("|")
seq_name = line[1]
# info = "_".join(line[2].split())
else:
line = l.strip().split(" ")
start, stop = str(int(line[0]) + 1), line[2]
gff_line = [
seq_name, 'WindowMasker', 'repeat', start, stop, '.', '.',
'.', 'ID=' + str(count) + '\n'
] # INFO is just information from a FASTA file "+ ';INFO=' + info + '\n'"
count += 1
outfile.write('\t'.join(gff_line))
def main():
outfile = metaopen(metaoutput(args.output, ".csv"), "wt")
frame_coverages_amounts_dict = Counter()
line_counter = 0
frame_line_counter = 0
for ln in args.input:
line = ln.strip().split()
line_counter += 1
frame_line_counter += 1
frame_coverages_amounts_dict[int(line[1])] += 1
if frame_line_counter == args.frame_size:
start = line_counter - args.frame_size + 1
stop = line_counter
metrics = CoveragesMetrics(frame_coverages_amounts_dict)
coverage = metrics.median_value()
outfile.write("\t".join(
["MT", str(start), str(stop),
str(coverage)]) + "\n")
frame_coverages_amounts_dict.clear()
frame_line_counter = 0
if frame_coverages_amounts_dict:
start = line_counter - sum(frame_coverages_amounts_dict.values()) + 1
stop = line_counter
metrics = CoveragesMetrics(frame_coverages_amounts_dict)
coverage = metrics.median_value()
outfile.write(
"\t".join(["MT", str(start),
str(stop), str(coverage)]) + "\n")
def main():
outfile = metaopen(metaoutput(args.output, ".mask.bed.gz"), "wt")
for line in args.input:
scaffold_name, start, stop, coverage_value = args.input.readline(
).strip().split()
coverage_value = int(coverage_value)
if (coverage_value >
(2.5 * args.whole_median)) or coverage_value < (0.5 *
args.whole_median):
break
for i in args.input:
line = i.strip().split()
line[3] = int(line[3])
if (line[3] >
(2.5 * args.whole_median)) or (line[3] <
(0.5 * args.whole_median)):
if line[0] == scaffold_name:
if stop == line[1]:
stop = line[2]
continue
else:
outfile.write("\t".join([scaffold_name, start, stop]) +
"\n")
start = line[1]
stop = line[2]
else:
outfile.write("\t".join([scaffold_name, start, stop]) + "\n")
scaffold_name, start, stop = line[:3]
outfile.write("\t".join([scaffold_name, start, stop]) + "\n")
def main():
barcode_fd = metaopen(args.prefix + "_barcodes.txt.gz", 'wt')
forward_fd = metaopen(args.prefix + "_ema-bin-all_1.fastq.gz", 'wt')
reverse_fd = metaopen(args.prefix + "_ema-bin-all_2.fastq.gz", 'wt')
if args.input == '-':
data = stdin
elif args.compressed: #for file.gz
data = metaopen(args.input, "rt", args.buffering)
else:
data = metaopen(args.input, "r", args.buffering)
for line in data:
line = line.split()
barcode_fd.write(line[0] + '\n')
forward_fd.write(line[1] + '\n' + line[2] + '\n' + '+\n' + line[3] +
'\n')
reverse_fd.write(line[1] + '\n' + line[4] + '\n' + '+\n' + line[5] +
'\n')
barcode_fd.close()
forward_fd.close()
reverse_fd.close()
if args.input != '-':
data.close()
def main():
if args.input == stdin:
outprefix = "trf.dat"
else:
if args.output:
outprefix = args.output
else:
outprefix = ('.').join(args.input.split('.')[:-1])
outprefix = metaoutput(outprefix, ".gff.gz")
outfile = metaopen(outprefix, "wt")
print("DAT to GFF converter...")
with metaopen(args.input, "r", buffering=args.buffering) as data:
count = 0
for l in data:
line = l.strip().split(" ")
if l.startswith(
'Sequence:'
): # information about authors and parameters is not processed
seq_name = line[1]
elif l[0].isdigit():
[
start, stop, period, copies, consensus_size, perc_match,
perc_indels, align_score, perc_A, perc_C, perc_G, perc_T,
entropy, cons_seq, repeat_seq
] = line
gff_line = [
seq_name, 'TRF', 'repeat', start, stop, '.', '.', '.',
'ID=' + str(count) + ';period=' + period + ';copies=' +
copies + ';consensus_size=' + consensus_size +
';perc_match=' + perc_match + ';perc_indels=' +
perc_indels + ';align_score=' + align_score + 'perc_A' +
perc_A + 'perc_C' + perc_C + 'perc_G' + perc_G + 'perc_T' +
perc_T + ';entropy=' + entropy + ';cons_seq=' + cons_seq +
';repeat_seq=' + repeat_seq + '\n'
]
count += 1
outfile.write('\t'.join(gff_line))
def parse_sequences_without_join(self, buffering=None) -> dict:
print("parse_sequences started")
data = OrderedDict()
header = None
f = metaopen(self.path, 'rt', buffering)
for line in f:
if line.startswith('>'):
header = line
data[header] = []
else:
data[header].append(line)
f.close()
for name in data:
data[name] = ''.join(data[name])
return data
def parse_sequences(self, buffering=None) -> dict:
"""
Parses a text file of genome sequences into a dictionary.
Arguments:
buffering: buffer text value
"""
print("parse_sequences started")
data = OrderedDict()
self.lengths = {}
header = None
f = metaopen(self.path, 'rt', buffering)
for line in f:
if line.startswith('>'):
header = line[1:].split(' ')[0]
data[header] = []
else:
data[header].append(line.rstrip())
f.close()
for name in data:
data[name] = ''.join(data[name])
self.lengths[name] = len(data[name])
return data
if args.whole_genome_stats:
metrics.get_whole_genome_stats()
if args.scaffolds_stats:
metrics.get_scaffolds_stats()
if args.nonoverlapping_windows_stats:
metrics.get_nonoverlapping_windows_stats(args.frame_size)
if args.universal_windows_stats: # in development for mosdepth
metrics.get_universal_windows_stats(args.frame_size, args.frame_shift)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="script for calculating median, average, maximum and minimum coverage in genomecov.tab.gz or mosdepth.bed.gz. Report to files.csv")
group_required = parser.add_argument_group('Required options')
group_required.add_argument('-i', '--input', type=lambda s: metaopen(s, "rt"),
help="input file.bam.gz (don`t use for STDIN)", default=stdin)
group_additional = parser.add_argument_group('Additional options')
group_additional.add_argument('-o', '--output', metavar='PATH', type=str, default=False,
help='output file prefix without frame size')
group_additional.add_argument('-t', '--tool-name', type=str, default="mosdepth",
help="tool name parameter (you can use 'mosdepth' or 'genomecov')")
group_additional.add_argument('-f', '--frame-size', type=int,
help="<f> bp windows size (for windows statistics)", default=1000000)
group_additional.add_argument('--frame-shift', type=int,
help="window shift step (for windows statistics)", default=1000000)
group_additional.add_argument('-g', '--whole-genome-stats', action="store_true", default=False,
help="to calculate statistics for whole genome only")
group_additional.add_argument('-s', '--scaffolds-stats', action="store_true", default=False,
help="to calculate statistics for scaffolds only")
outfile.writelines(
coordinates_list_to_BED(args.scaffold_name,
coordinates_merge_by_median))
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description=
"Script for determining the coordinates of the PAR. Output of coordinates to BED file"
)
group_required = parser.add_argument_group('Required options')
group_required.add_argument(
'-i',
'--input',
type=lambda s: metaopen(s, "rt"),
help="input coverage_statistics_output.csv (don`t use for STDIN)",
default=stdin)
group_additional = parser.add_argument_group('Additional options')
group_additional.add_argument('-o',
'--output',
metavar='PATH',
type=str,
default=False,
help='output file prefix')
group_additional.add_argument('-f',
'--window-size',
type=int,
help="the window size used in your data",
default=10000)