def main(args):
fm.filecheck(args.query)
fm.filecheck(args.subject)
ref_gene_seq = list(fm.fasta(args.query).fa_dict.values())[0]
start_anchor = ref_gene_seq[:args.anchor_size]
end_anchor = ref_gene_seq[-args.anchor_size:]
tmp_in = fm.get_random_file()
tmp_out = fm.get_random_file()
with open(tmp_in, "w") as O:
O.write(">tmp\n%s" % start_anchor)
fm.run_cmd("blastn -task blastn -query %s -subject %s -outfmt 15 > %s" %
(tmp_in, args.subject, tmp_out),
verbose=0)
start_hits = parse_blast(tmp_out, args.anchor_size * 0.9)
with open(tmp_in, "w") as O:
O.write(">tmp\n%s" % end_anchor)
fm.run_cmd("blastn -task blastn -query %s -subject %s -outfmt 15 > %s" %
(tmp_in, args.subject, tmp_out),
verbose=0)
end_hits = parse_blast(tmp_out, args.anchor_size * 0.9)
fm.rm_files([tmp_in, tmp_out])
result_type = ""
if args.strict_one_hit and (len(start_hits) > 1 or len(end_hits) > 1):
result_type = "NA"
else:
if start_hits[0]["subject_seq"] == end_hits[0]["subject_seq"]:
result_type = "OK"
start_hit = start_hits[0]
end_hit = end_hits[0]
else:
result_type = "Fragmented"
with open("%s.result.txt" % args.prefix, "w") as O:
O.write("%s\t%s\n" % (args.prefix, result_type))
if result_type != "OK":
quit()
print(start_hit, end_hit)
subject_seqs = fm.fasta(args.subject).fa_dict
if start_hit["subject_strand"] == "Plus" and end_hit[
"subject_strand"] == "Plus":
hit_seq = subject_seqs[
start_hit["subject_seq"]][start_hit["subject_start"] -
1:end_hit["subject_end"]]
elif start_hit["subject_strand"] == "Minus" and end_hit[
"subject_strand"] == "Minus":
hit_seq = revcom(subject_seqs[start_hit["subject_seq"]]
[end_hit["subject_end"] -
1:start_hit["subject_start"]])
# import pdb; pdb.set_trace()
with open("%s.extracted_seq.fa" % args.prefix, "w") as O:
O.write(">%s\n%s\n" % (args.prefix, hit_seq))
def main(args):
if args.prefix:
individual_bams = ["%s/%s%s" % (args.dir,run,args.suffix) for run in args.prefix.split("_")]
new_id = args.new_id if args.new_id else args.prefix
elif args.bams:
individual_bams = args.bams.split(",")
new_id = args.new_id if args.new_id else "_".join([bam.split("/")[-1].replace(args.suffix,"") for bam in individual_bams])
elif (not args.prefix and not args.bams) or (args.prefix and args.bams):
sys.stderr.write("Need wither '--bams' or '--prefix'... Exiting!\n")
quit()
if len(individual_bams)==1:
sys.stderr.write("Need more than one bam... Exiting!\n")
quit()
for bam in individual_bams:
fm.filecheck(bam)
new_bamfile = "%s/%s%s" % (args.dir,new_id,args.suffix)
tmp_bamfile = fm.get_random_file()
tmp_file = fm.get_random_file()
with open(tmp_file,"w") as O:
for l in fm.cmd_out("samtools view -H %s" % individual_bams[0]):
row = l.strip().split("\t")
if row[0]=="@RG":
continue
row[1] = "ID:%s" % new_id
row[2] = "SM:%s" % new_id
O.write("%s\n" % "\t".join(row))
fm.run_cmd("samtools merge -@ %s - %s | samtools reheader -i %s - | samtools addreplacerg -@ %s - -r 'ID:%s\\tSM:%s\\tPL:Illumina' -o %s" % (
args.threads," ".join(individual_bams), tmp_file, args.threads,new_id, new_id, new_bamfile)
)
fm.run_cmd("samtools index %s" % new_bamfile)
fm.rm_files([tmp_file,tmp_bamfile])
def main(args):
check_programs(["taxonkit", "seqtk"])
if not os.path.isdir(
"%s/.taxonkit/" % os.path.expanduser("~")) or not os.path.isfile(
"%s/.taxonkit/nodes.dmp" % os.path.expanduser("~")):
download_files()
nodes = set()
sys.stderr.write("Loading taxonomy\n")
cmd = "taxonkit list --ids %s" % (args.extract
if args.extract else args.exclude)
for l in fm.cmd_out(cmd):
if l == "": continue
row = l.strip().split()
nodes.add(row[0])
sys.stderr.write("Extracting read names\n")
args.tmp_file = str(uuid4())
total_reads = 0
kept_reads = 0
with open(args.tmp_file, "w") as O:
if args.exclude:
for l in tqdm(open(fm.filecheck(args.kraken2_output))):
total_reads += 1
row = l.strip().split()
if row[2] not in nodes:
O.write("%s\n" % row[1])
kept_reads += 1
else:
for l in tqdm(open(fm.filecheck(args.kraken2_output))):
total_reads += 1
row = l.strip().split()
if row[2] in nodes:
O.write("%s\n" % row[1])
kept_reads += 1
sys.stderr.write("Writing filtered fastq files\n")
fm.filecheck(args.R1)
args.R1_filt = args.R1.replace(".fastq.gz", "").replace(
".fq.gz", "").replace(".fastq", "") + ".kraken2_filt.fastq.gz"
fm.run_cmd("seqtk subseq %(R1)s %(tmp_file)s | gzip -c > %(R1_filt)s" %
vars(args))
if args.R2:
fm.filecheck(args.R2)
args.R2_filt = args.R2.replace(".fastq.gz", "").replace(
".fq.gz", "").replace(".fastq", "") + ".kraken2_filt.fastq.gz"
fm.run_cmd("seqtk subseq %(R2)s %(tmp_file)s | gzip -c > %(R2_filt)s" %
vars(args))
fm.rm_files([args.tmp_file])
sys.stderr.write("\nKept %s/%s reads\n" % (kept_reads, total_reads))
def main(args):
nodes = defaultdict(set)
sys.stderr.write("Loading taxonomy\n")
for l in tqdm(open(fm.filecheck(args.tax_dump))):
row = l.strip().split()
nodes[row[2]].add(row[0])
def flatten(d):
v = [[i] if not isinstance(i, list) else flatten(i) for i in d]
return [i for b in v for i in b]
def get_tax(t):
if len(nodes[t]) == 0:
return [t]
return [t] + flatten([get_tax(sub_t) for sub_t in nodes[t]])
sys.stderr.write("Extracting read names\n")
args.tmp_file = str(uuid4())
with open(args.tmp_file, "w") as O:
if args.exclude:
tax_tree = set(
flatten([get_tax(x) for x in args.exclude.split(",")]))
for l in tqdm(open(fm.filecheck(args.kraken2_output))):
row = l.strip().split()
if row[2] not in tax_tree:
O.write("%s\n" % row[1])
else:
tax_tree = set(
flatten([get_tax(x) for x in args.extract.split(",")]))
for l in tqdm(open(fm.filecheck(args.kraken2_output))):
row = l.strip().split()
if row[2] in tax_tree:
O.write("%s\n" % row[1])
sys.stderr.write("Writing filtered fastq files\n")
fm.filecheck(args.R1)
args.R1_filt = args.R1.replace(".fastq.gz", "").replace(
".fq.gz", "").replace(".fastq", "") + ".kraken2_filt.fastq.gz"
fm.run_cmd("seqtk subseq %(R1)s %(tmp_file)s | gzip -c > %(R1_filt)s" %
vars(args))
if args.R2:
fm.filecheck(args.R2)
args.R2_filt = args.R2.replace(".fastq.gz", "").replace(
".fq.gz", "").replace(".fastq", "") + ".kraken2_filt.fastq.gz"
fm.run_cmd("seqtk subseq %(R2)s %(tmp_file)s | gzip -c > %(R2_filt)s" %
vars(args))
fm.rm_files([args.tmp_file])
def main(args):
samples = []
for l in open(args.samples):
samples = [x.rstrip() for x in open(args.samples).readlines()]
for s in samples:
fm.filecheck("per_sample/%s%s" % (s, args.alignment_extension))
if fm.nofolder("%(dir)s/kraken" % vars(args)):
fm.run_cmd("%(dir)s/kraken" % vars(args))
args.cmd_file = fm.get_random_file()
with open(args.cmd_file, "w") as O:
for s in samples:
args.sample = s
if fm.nofile("%(dir)s/per_sample/%(sample)s.median_dp.txt" %
vars(args)):
O.write(
"printf %s\"\\t\"$(bedtools genomecov -d -ibam %s/per_sample/%s%s | datamash median 3)\"\\n\" > %s/per_sample/%s.median_dp.txt\n"
% (s, args.dir, s, args.alignment_extension, args.dir, s))
if fm.nofile("%(dir)s/kraken/%(sample)s.done" % vars(args)):
O.write(
"kraken2 --db /run/user/506/standard --gzip-compressed --paired %(dir)s/fastq/%(sample)s_1.fastq.gz %(dir)s/fastq/%(sample)s_2.fastq.gz --report %(dir)s/kraken/%(sample)s.report.txt --out %(dir)s/kraken/%(sample)s.out.txt --threads 10 --memory-mapping && touch %(dir)s/kraken/%(sample)s.done\n"
% vars(args))
fm.run_cmd("cat %(cmd_file)s | parallel -j %(io_heavy_threads)s" %
vars(args),
verbose=2)
fm.rm_files([args.cmd_file])
sample_metrics = []
for s in samples:
res = {"sample": s}
args.sample = s
for i, l in enumerate(
open("%(dir)s/per_sample/%(sample)s.bqsr.bamstats" %
vars(args))):
row = l.rstrip().split()
if i in [2, 3]:
res[row[3]] = int(row[0])
elif i == 4:
res[row[3]] = int(row[0])
res["mapped_percent"] = float(row[4].replace("(", "").replace(
"%", ""))
else:
pass
kraken_results = {}
for l in open("%(dir)s/kraken/%(sample)s.report.txt" % vars(args)):
row = l.strip().split()
if row[3] not in kraken_results:
kraken_results[row[3]] = (float(row[0]), " ".join(row[5:]))
if float(row[0]) > kraken_results[row[3]][0]:
kraken_results[row[3]] = (float(row[0]), " ".join(row[5:]))
res["kraken_genus"] = "%s (%.2f)" % (kraken_results["G"][1],
kraken_results["G"][0])
res["kraken_genus1"] = "%s (%.2f)" % (kraken_results["G1"][1],
kraken_results["G1"][0])
res["kraken_species"] = "%s (%.2f)" % (kraken_results["S"][1],
kraken_results["S"][0])
tbprofiler_result = json.load(
open("%(dir)s/tbprofiler/results/%(sample)s.results.json" %
vars(args)))
res["lineage"] = tbprofiler_result["main_lin"]
res["sub-lineage"] = tbprofiler_result["sublin"]
res["drtype"] = tbprofiler_result["drtype"]
tmp_drugs = defaultdict(list)
for var in tbprofiler_result["dr_variants"]:
for d in var["drugs"]:
tmp_drugs[d["drug"]].append(
"%s_%s (%.2f)" % (var["gene"], var["change"], var["freq"]))
for d in drugs:
res[d] = ", ".join(tmp_drugs[d])
sample_metrics.append(res)
with open(args.out + ".sample_info.csv", "w") as O:
writer = csv.DictWriter(O, fieldnames=list(sample_metrics[0]))
writer.writeheader()
writer.writerows(sample_metrics)
vcf = fm.vcf_class(args.vcf)
if fm.nofile(args.vcf + ".stats.txt"):
fm.run_cmd(
"bcftools norm -m - -f %(ref)s %(vcf)s | bcftools stats -v -s - > %(vcf)s.stats.txt"
% (vars(args)))
vcf_stats = vcf.load_stats()
results = {
"number of samples": vcf_stats["number of samples"],
"number of records": vcf_stats["number of records"],
"number of SNPs": vcf_stats["number of SNPs"],
"number of indels": vcf_stats["number of indels"],
}
snp_results = []
if fm.nofile(args.vcf + ".csq_info.txt"):
fm.run_cmd(
"bcftools view -V indels %(vcf)s | bcftools norm -m - -f %(ref)s | bcftools csq -f %(ref)s -g %(gff)s | correct_tb_csq.py | bcftools +fill-tags | bcftools query -f '%%POS\\t%%REF\\t%%ALT\\t%%AF\\t%%AC\\t%%BCSQ\\n' > %(vcf)s.csq_info.txt"
% vars(args))
fm.run_cmd(
"bcftools view -v indels %(vcf)s | bcftools norm -m - -f %(ref)s | bcftools csq -f %(ref)s -g %(gff)s | correct_tb_csq.py | bcftools +fill-tags | bcftools query -f '%%POS\\t%%REF\\t%%ALT\\t%%AF\\t%%AC\\t%%BCSQ\\n' >> %(vcf)s.csq_info.txt"
% vars(args))
variant_info = vcf.get_variant_data(args.ref, args.gff)
with open(args.out + ".variant_info.csv", "w") as O:
writer = csv.DictWriter(O, fieldnames=list(variant_info[0]))
writer.writeheader()
writer.writerows(variant_info)