def main_load_library(args):
lib_prefix = args.prefix.split("/")[-1]
files = {
"gff": ".gff",
"ref": ".fasta",
"barcode": ".barcode.bed",
"version": ".version.json",
"proteins": ".proteins.csv",
"non_coding_bed": ".non_coding.bed"
}
if pp.nofolder(sys.base_prefix + "/share/covidprofiler"):
pp.run_cmd("mkdir %s " % (sys.base_prefix + "/share/covidprofiler/"))
pp.run_cmd("cp %s %s" % (args.msa, "%s/share/covidprofiler/%s.msa.fa" %
(sys.base_prefix, lib_prefix)))
pp.run_cmd("cp %s %s" %
(args.meta, "%s/share/covidprofiler/%s.msa.meta.csv" %
(sys.base_prefix, lib_prefix)))
for key in files:
new_file_location = sys.base_prefix + "/share/covidprofiler/" + lib_prefix + files[
key]
pp.run_cmd("cp %s %s" % (args.prefix + files[key], new_file_location))
pp.run_cmd("samtools faidx %s" % sys.base_prefix +
"/share/covidprofiler/" + lib_prefix + ".fasta")
pp.run_cmd("bwa index %s" % sys.base_prefix + "/share/covidprofiler/" +
lib_prefix + ".fasta")
if os.path.isfile("%s" % sys.base_prefix + "/share/covidprofiler/" +
lib_prefix + ".dict"):
pp.run_cmd("rm %s" % sys.base_prefix + "/share/covidprofiler/" +
lib_prefix + ".dict")
pp.log("Sucessfully imported library")
def main_load_library(args):
lib_prefix = args.prefix.split("/")[-1]
files = {
"gff": ".gff",
"ref": ".fasta",
"barcode": ".barcode.bed",
"version": ".version.json"
}
if pp.nofolder(sys.base_prefix + "/share/covidprofiler"):
pp.run_cmd("mkdir %s " % (sys.base_prefix + "/share/covidprofiler/"))
for key in files:
new_file_location = sys.base_prefix + "/share/covidprofiler/" + lib_prefix + files[
key]
pp.run_cmd("cp %s %s" % (args.prefix + files[key], new_file_location))
pp.run_cmd("samtools faidx %s" % sys.base_prefix +
"/share/covidprofiler/" + lib_prefix + ".fasta")
pp.run_cmd("bwa index %s" % sys.base_prefix + "/share/covidprofiler/" +
lib_prefix + ".fasta")
if os.path.isfile("%s" % sys.base_prefix + "/share/covidprofiler/" +
lib_prefix + ".dict"):
pp.run_cmd("rm %s" % sys.base_prefix + "/share/covidprofiler/" +
lib_prefix + ".dict")
pp.run_cmd("gatk CreateSequenceDictionary -R %s" % sys.base_prefix +
"/share/covidprofiler/" + lib_prefix + ".fasta")
pp.log("Sucessfully imported library")
def get_summary(json_results,
conf,
columns=None,
drug_order=None,
reporting_af=0.0):
if not columns:
columns = []
drugs = set()
for l in open(conf["bed"]):
arr = l.rstrip().split()
for d in arr[5].split(","):
drugs.add(d)
if drug_order:
drugs = drug_order
drug_table = []
results = {}
annotation = {}
for key in columns:
if key not in json_results["dr_variants"][0]:
pp.log(
"%s not found in variant annotation, is this a valid column in the database CSV file? Exiting!"
% key, True)
for x in json_results["dr_variants"]:
d = x["drug"]
if float(x["freq"]) < reporting_af: continue
if d not in results: results[d] = list()
results[d].append("%s %s (%.2f)" % (x["gene"], x["change"], x["freq"]))
if d not in annotation: annotation[d] = {key: [] for key in columns}
for key in columns:
annotation[d][key].append(x[key])
for d in drugs:
if d in results:
results[d] = ", ".join(results[d]) if len(results[d]) > 0 else ""
r = "R" if len(results[d]) > 0 else ""
for key in columns:
annotation[d][key] = ", ".join(
annotation[d][key]) if len(annotation[d][key]) > 0 else ""
else:
results[d] = ""
r = ""
dictline = {
"Drug": d.capitalize(),
"Genotypic Resistance": r,
"Mutations": results[d]
}
for key in columns:
dictline[key] = annotation[d][key] if d in annotation else ""
drug_table.append(dictline)
pipeline_tbl = [{
"Analysis": "Mapping",
"Program": json_results["pipeline"]["mapper"]
}, {
"Analysis": "Variant Calling",
"Program": json_results["pipeline"]["variant_caller"]
}]
new_json = json_results.copy()
new_json["drug_table"] = drug_table
new_json["pipline_table"] = pipeline_tbl
return new_json
def write_html(json_results,conf,outfile,columns = None,drug_order = None):
json_results = get_summary(json_results,conf,columns = columns, drug_order=drug_order)
html_strings = {}
html_strings["id"] = json_results["id"]
html_strings["date"] = time.ctime()
html_strings["strain"] = json_results["sublin"]
html_strings["drtype"] = json_results["drtype"]
html_strings["dr_report"] = dict_list2html(json_results["drug_table"],["Drug","Genotypic Resistance","Mutations"]+columns,{"Drug":"Drug<sup>1</sup>","Genotypic Resistance":"Resistance","Mutations":"Supporting Mutations (frequency)"})
html_strings["lineage_report"] = dict_list2html(json_results["lineage"],["lin","family","spoligotype","rd"],{"lin":"Lineage<sup>2</sup>","frac":"Estimated fraction","family":"Family","spoligotype":"Main Spoligotype","rd":"RDS"})
html_strings["other_var_report"] = dict_list2html(json_results["other_variants"],["gene","genome_pos","change","freq"],{"gene":"Gene","genome_pos":"Chromosome Position","change":"Mutation","freq":"Estimated fraction"})
html_strings["pipeline"] = dict_list2html(json_results["pipline_table"],["Analysis","Program"])
html_strings["version"] = json_results["tbprofiler_version"]
o = open(outfile,"w")
pp.log("Writing results to %s" % outfile)
o.write(load_html(html_strings))
o.close()
def main(args):
vcf = vcf_class(args.vcf)
# vcf.get_mean_genotype()
if args.genes:
vcf.get_genesum()
geno_file = vcf.prefix + ".geno"
genesum_file = vcf.prefix + ".genesum"
meta = {}
for s in vcf.samples:
meta[s] = {}
for row in csv.DictReader(open(args.csv)):
for pheno in row.keys():
if pheno == "id": continue
if row['id'] not in meta: continue
meta[row["id"]][pheno] = row[pheno]
phenos = [x.rstrip() for x in open(args.phenos).readlines()]
cmd_file = pp.get_random_file()
X = open(cmd_file, "w")
for pheno in phenos:
pheno_file = "%s.pheno" % pheno
if pheno not in row:
pp.log("%s not in CSV file" % pheno, True)
P = open(pheno_file, "w")
P.write("\n".join([
meta[s][pheno] if pheno in meta[s] else "NA" for s in vcf.samples
]))
P.close()
X.write(
"gemma -p %s -g %s -gk 1 -o %s -maf 0.00005 -miss 0.99 && gemma -lmm 1 -p %s -g %s -k output/%s.cXX.txt -o %s -maf 0.00005 -miss 0.99 && gemma -lmm 1 -p %s -g %s -k output/%s.cXX.txt -o %s.genesum -notsnp\n"
% (pheno_file, geno_file, pheno, pheno_file, geno_file, pheno,
pheno, pheno_file, genesum_file, pheno, pheno))
X.close()
if args.preprocess:
pp.log("Preprocessing finished\n", True)
else:
pp.run_cmd("cat %s | parallel -j %s" % (cmd_file, args.threads))
def calculate(args):
sample_file = args.samples
dst_file = args.dst
dst = load_dst(dst_file)
drug_loci = pp.load_bed(args.bed, [6], 4) # {'Rv0668': ('rifampicin')}
FAIL = open("samples_not_found.txt", "w")
samples = [x.rstrip() for x in open(sample_file).readlines()]
ext = ".results.json"
drugs = [d.lower() for d in dst[samples[0]].keys()]
results = {
d: {
"tp": [],
"tn": [],
"fp": [],
"fn": []
}
for d in drugs + ["flq", "mdr", "xdr", "sus"]
}
counts = {
d: {
"tp": 0,
"tn": 0,
"fp": 0,
"fn": 0
}
for d in drugs + ["flq", "mdr", "xdr", "sus"]
}
pre = args.dir if args.dir else ""
for s in tqdm(samples):
res_file = "%s/%s%s" % (pre, s, ext)
if pp.nofile(res_file):
pp.log("Warning: %s does not exist!" % res_file)
FAIL.write("%s\n" % s)
continue
res = json.load(open(res_file))
na_drugs = set()
for locus in drug_loci:
if res["missing_regions"][locus] > args.miss:
for tmp in drug_loci[locus][0].split(","):
na_drugs.add(tmp)
resistant_drugs = [d["drug"].lower() for d in res["dr_variants"]]
for d in drugs:
if d in na_drugs:
dst[s][d] = "NA"
for d in drugs:
if dst[s][d] == "0" and d not in resistant_drugs:
results[d]["tn"].append(s)
counts[d]["tn"] += 1
elif dst[s][d] == "0" and d in resistant_drugs:
results[d]["fp"].append(s)
counts[d]["fp"] += 1
elif dst[s][d] == "1" and d not in resistant_drugs:
results[d]["fn"].append(s)
counts[d]["fn"] += 1
elif dst[s][d] == "1" and d in resistant_drugs:
results[d]["tp"].append(s)
counts[d]["tp"] += 1
#### Fluoroquinolones ####
dst_flq = "0"
dst_flq_NA = True
for d in fluoroquinolones:
if d not in dst[s]: continue
if dst[s][d] != "NA": dst_flq_NA = False
if dst[s][d] == "1": dst_flq = "1"
dst_flq_list = [dst[s][d] for d in fluoroquinolones if d in dst[s]]
if "1" in dst_flq_list and "0" in dst_flq_list:
dst_flq = "NA"
if dst_flq_NA: dst_flq = "NA"
gst_flq = "0"
for d in fluoroquinolones:
if d in resistant_drugs: gst_flq = "1"
if dst_flq == "1" and gst_flq == "1":
results["flq"]["tp"].append(s)
counts["flq"]["tp"] += 1
if dst_flq == "0" and gst_flq == "1":
results["flq"]["fp"].append(s)
counts["flq"]["fp"] += 1
if dst_flq == "1" and gst_flq == "0":
results["flq"]["fn"].append(s)
counts["flq"]["fn"] += 1
if dst_flq == "0" and gst_flq == "0":
results["flq"]["tn"].append(s)
counts["flq"]["tn"] += 1
#### MDR & XDR ####
dst_mdr = "1" if dst[s]["rifampicin"] == "1" and dst[s][
"isoniazid"] == "1" else "0"
if dst[s]["rifampicin"] == "NA" or dst[s]["isoniazid"] == "NA":
dst_mdr = "NA"
flq = False
flq_NA = True
for d in fluoroquinolones:
if d not in dst[s]: continue
if dst[s][d] != "NA": flq_NA = False
if dst[s][d] == "1": flq = True
amg = False
amg_NA = True
for d in aminoglycosides:
if d not in dst[s]: continue
if dst[s][d] != "NA": amg_NA = False
if dst[s][d] == "1": amg = True
dst_xdr = "1" if dst_mdr == "1" and flq and amg else "0"
if flq_NA or amg_NA: dst_xdr = "NA"
if dst_mdr == "NA": dst_xdr = "NA"
#### Profiling results #####
gst_mdr = "1" if "rifampicin" in resistant_drugs and "isoniazid" in resistant_drugs else "0"
flq = False
for d in fluoroquinolones:
if d in resistant_drugs: flq = True
amg = False
for d in aminoglycosides:
if d in resistant_drugs: amg = True
gst_xdr = "1" if gst_mdr == "1" and flq and amg else "0"
if dst_mdr == "1" and gst_mdr == "1":
results["mdr"]["tp"].append(s)
counts["mdr"]["tp"] += 1
if dst_mdr == "0" and gst_mdr == "1":
results["mdr"]["fp"].append(s)
counts["mdr"]["fp"] += 1
if dst_mdr == "1" and gst_mdr == "0":
results["mdr"]["fn"].append(s)
counts["mdr"]["fn"] += 1
if dst_mdr == "0" and gst_mdr == "0":
results["mdr"]["tn"].append(s)
counts["mdr"]["tn"] += 1
if dst_xdr == "1" and gst_xdr == "1":
results["xdr"]["tp"].append(s)
counts["xdr"]["tp"] += 1
if dst_xdr == "0" and gst_xdr == "1":
results["xdr"]["fp"].append(s)
counts["xdr"]["fp"] += 1
if dst_xdr == "1" and gst_xdr == "0":
results["xdr"]["fn"].append(s)
counts["xdr"]["fn"] += 1
if dst_xdr == "0" and gst_xdr == "0":
results["xdr"]["tn"].append(s)
counts["xdr"]["tn"] += 1
### susceptibility
if "NA" not in [dst[s][d] for d in first_line]:
dst_sus = "1" if "1" not in [dst[s][d] for d in drugs] else "0"
gst_sus = "1" if all(
[x not in resistant_drugs for x in first_line]) else "0"
if dst_sus == "1" and gst_sus == "1":
results["sus"]["tp"].append(s)
counts["sus"]["tp"] += 1
if dst_sus == "0" and gst_sus == "1":
results["sus"]["fp"].append(s)
counts["sus"]["fp"] += 1
if dst_sus == "1" and gst_sus == "0":
results["sus"]["fn"].append(s)
counts["sus"]["fn"] += 1
if dst_sus == "0" and gst_sus == "0":
results["sus"]["tn"].append(s)
counts["sus"]["tn"] += 1
json.dump(results, open("results.json", "w"))
json.dump(counts, open("counts.json", "w"))
counts = json.load(open("counts.json"))
drugs = [x.rstrip().lower() for x in open(args.drugs).readlines()
] if args.drugs else list(counts.keys())
print("Drug\tNum\tSusceptible\tResistant\tSensitivity\tSpecificity")
for d in drugs:
if d not in counts: continue
if counts[d]["tp"] + counts[d]["fn"] == 0 or counts[d]["tn"] + counts[
d]["fp"] == 0:
continue
sensitivity = counts[d]["tp"] / (counts[d]["tp"] + counts[d]["fn"])
specificity = counts[d]["tn"] / (counts[d]["tn"] + counts[d]["fp"])
total = counts[d]["tp"] + counts[d]["fp"] + counts[d]["tn"] + counts[
d]["fn"]
suc = counts[d]["tn"] + counts[d]["fp"]
res = counts[d]["tp"] + counts[d]["fn"]
print("%s\t%s\t%s\t%s\t%s\t%s" %
(d.capitalize(), total, suc, res, sensitivity, specificity))
def main_profile(args):
#### Setup conf dictionary ###
if args.db == "tbdb" and not args.external_db and pp.nofile(
sys.base_prefix + "/share/tbprofiler/tbdb.fasta"):
pp.log(
"Can't find the tbdb file at %s. Please run 'tb-profiler update_tbdb' to load the default library or specify another using the '--external_db' flag"
% sys.base_prefix,
ext=True)
if args.external_db:
conf = get_conf_dict(args.external_db)
else:
conf = get_conf_dict(sys.base_prefix +
"/share/tbprofiler/%s" % args.db)
### Create folders for results if they don't exist ###
if pp.nofolder(args.dir):
os.mkdir(args.dir)
for x in ["bam", "vcf", "results"]:
if pp.nofolder(args.dir + "/" + x):
os.mkdir(args.dir + "/" + x)
### Set up platform dependant parameters ###
if args.platform == "nanopore":
args.mapper = "minimap2"
args.caller = "bcftools"
args.no_trim = True
run_delly = False
else:
if args.no_delly:
run_delly = False
else:
run_delly = True
### Setup prefix for files ###
files_prefix = args.dir + "/" + args.prefix
### Create bam file if fastq has been supplied ###
if args.bam == None:
if args.read1 and args.read2 and args.no_trim:
# Paired + no trimming
fastq_obj = pp.fastq(args.read1, args.read2)
elif args.read1 and args.read2 and not args.no_trim:
# Paired + trimming
untrimmed_fastq_obj = pp.fastq(args.read1, args.read2)
fastq_obj = untrimmed_fastq_obj.trim(files_prefix,
threads=args.threads)
elif args.read1 and not args.read2 and args.no_trim:
# Unpaired + trimming
fastq_obj = pp.fastq(args.read1, args.read2)
elif args.read1 and not args.read2 and not args.no_trim:
# Unpaired + trimming
untrimmed_fastq_obj = pp.fastq(args.read1)
fastq_obj = untrimmed_fastq_obj.trim(files_prefix,
threads=args.threads)
else:
exit("\nPlease provide a bam file or a fastq file(s)...Exiting!\n")
bam_obj = fastq_obj.map_to_ref(ref_file=conf["ref"],
prefix=files_prefix,
sample_name=args.prefix,
aligner=args.mapper,
platform=args.platform,
threads=args.threads)
bam_file = bam_obj.bam_file
else:
bam_file = args.bam
print(args.delly_bcf_file)
run_coverage = False if args.no_coverage else True
### Run profiling module from pathogen-profiler ###
results = pp.bam_profiler(
conf=conf,
bam_file=bam_file,
prefix=files_prefix,
platform=args.platform,
caller=args.caller,
threads=args.threads,
no_flagstat=args.no_flagstat,
run_delly=run_delly,
calling_params=args.calling_params,
coverage_fraction_threshold=args.coverage_fraction_threshold,
missing_cov_threshold=args.missing_cov_threshold,
delly_bcf_file=args.delly_bcf_file)
json.dump(results, open(args.prefix + ".tmp_results.json", "w"))
### Reformat the results to TB-Profiler style ###
results = tbp.reformat(results, conf, reporting_af=args.reporting_af)
results["id"] = args.prefix
results["tbprofiler_version"] = tbp._VERSION
results["pipeline"] = {
"mapper": args.mapper if not args.bam else "N/A",
"variant_caller": args.caller
}
json_output = args.dir + "/results/" + args.prefix + ".results.json"
tex_output = args.dir + "/results/" + args.prefix + ".results.tex"
text_output = args.dir + "/results/" + args.prefix + ".results.txt"
csv_output = args.dir + "/results/" + args.prefix + ".results.csv"
json.dump(results, open(json_output, "w"))
extra_columns = [x.lower() for x in args.add_columns.split(",")
] if args.add_columns else []
if args.pdf:
tbp.write_tex(results, conf, tex_output, extra_columns)
pp.run_cmd("pdflatex %s" % tex_output, verbose=1)
pp.rm_files([
tex_output, args.dir + "/" + args.prefix + ".results.aux",
args.dir + "/" + args.prefix + ".results.log"
])
if args.txt:
tbp.write_text(results,
conf,
text_output,
extra_columns,
reporting_af=args.reporting_af)
if args.csv:
tbp.write_csv(results, conf, csv_output, extra_columns)
### Move files to respective directories ###
if not args.bam:
pp.run_cmd("mv %(dir)s/%(prefix)s.bam* %(dir)s/bam/" % vars(args))
if not args.no_trim:
pp.run_cmd("rm -f %s" % " ".join(fastq_obj.files))
pp.run_cmd("mv -f %(dir)s/%(prefix)s*.vcf.gz* %(dir)s/vcf/" % vars(args))
if run_delly and results["delly"] == "success" and not args.delly_bcf_file:
pp.run_cmd("mv -f %(dir)s/%(prefix)s.delly.bcf* %(dir)s/vcf/" %
vars(args))
### Add meta data to results
if args.meta:
for row in csv.DictReader(open(args.meta)):
if row["id"] == results["id"]:
for col in row:
results["meta_" + col] = row[col]
pp.log("Profiling finished sucessfully!")
def main(args):
if args.drugs:
args.drugs = [x.lower() for x in args.drugs.split(",")]
conf = conf = get_conf_dict(sys.base_prefix +
"/share/tbprofiler/%s" % args.db)
json_db = json.load(open(conf["json_db"]))
drug2genes = defaultdict(set)
gene2drugs = defaultdict(set)
gene2lt = {}
lt2gene = {}
for l in open(conf["bed"]):
row = l.rstrip().split()
for d in row[5].split(","):
drug2genes[d].add(row[3])
gene2drugs[row[3]].add(d)
gene2lt[row[3]] = row[3]
gene2lt[row[4]] = row[3]
lt2gene[row[3]] = row[4]
mutations = []
for l in open(args.mutations):
row = l.strip().split()
mutations.append((gene2lt[row[0]], row[1]))
meta = {}
reader = csv.DictReader(open(args.meta))
drug_resistant_isolates = {
d: set()
for d in drug2genes if d in reader.fieldnames
}
for row in reader:
meta[row["id"]] = row
for drug in drug_resistant_isolates:
if row[drug] == "1":
drug_resistant_isolates[drug].add(row["id"])
pp.log(f"Analysing {len(drug_resistant_isolates)} drugs")
if args.samples:
samples = [
x.rstrip() for x in open(args.samples).readlines()
if x.rstrip() in meta
]
else:
samples = [
x.replace(".results.json", "") for x in os.listdir("results/")
if x[-13:] == ".results.json"
if x.replace(".results.json", "") in meta
]
variants = {x: set() for x in mutations}
hgvs2bcftools = {}
variant_drug_associations = defaultdict(set)
for s in tqdm(samples):
tmp = json.load(open(f"{args.dir}/{s}.results.json"))
for var in tmp["dr_variants"] + tmp["other_variants"]:
if (var["locus_tag"], var["change"]) in mutations:
hgvs2bcftools[var["change"]] = var["_internal_change"]
variants[(var["locus_tag"], var["change"])].add(s)
if "drug" in var:
variant_drug_associations[(var["locus_tag"],
var["change"])].add(var["drug"])
total_sample_n = len(samples)
pp.log(f"Found {total_sample_n} samples in meta list with result files")
pp.log("-" * 40)
pp.log(variant_drug_associations)
print(
"Gene,Mutation,Drug resistance association,Total frequency (percentage),Associated drugs,Drug resitant frequency (percentage)"
)
for gene, mut in variants:
total_freq = len(variants[(gene, mut)])
total_pct = total_freq / total_sample_n * 100
dr_associated = "Not associated"
if (gene, mut) in variant_drug_associations:
drugs = variant_drug_associations[(gene, mut)]
dr_associated = "Associated"
else:
drugs = gene2drugs[gene]
if args.drugs:
drugs = [d for d in drugs if d in args.drugs]
dr_freqs = []
dr_pcts = []
for drug in drugs:
dr_freq = len(variants[(gene, mut)].intersection(
drug_resistant_isolates[drug]))
dr_pct = dr_freq / len(drug_resistant_isolates[drug]) * 100
dr_freqs.append(dr_freq)
dr_pcts.append(dr_pct)
zipped_list = ["%s (%.2f)" % (x, y) for x, y in zip(dr_freqs, dr_pcts)]
print("%s,%s,%s,%s (%.2f),%s,%s" %
(lt2gene[gene], mut, dr_associated, total_freq, total_pct,
';'.join(drugs), ';'.join(zipped_list)))
def main(args):
if pp.nofolder(args.out_dir):
pp.run_cmd("mkdir %s" % args.out_dir)
conf = {
"ref": args.ref,
"gff": args.gff,
"bed": args.bed,
"ann": args.ann,
}
if args.conf:
conf = json.load(open(args.conf))
for x in ["ref", "gff", "bed", "ann"]:
if conf[x] == None:
pp.log("%s variable is not defined" % x, True)
bam_obj = pp.bam(args.bam,
args.prefix,
conf["ref"],
platform=args.platform)
bcf_obj = bam_obj.call_variants(
prefix=args.prefix + ".targets",
call_method=args.call_method,
gff_file=conf["gff"],
bed_file=conf["bed"],
mixed_as_missing=False if args.platform == "Illumina" else True,
threads=args.threads,
min_dp=args.min_depth,
af=args.af,
caller=args.caller)
csq = bcf_obj.load_csq(ann_file=conf["ann"])
variants = []
chr2gene_pos = {}
for l in open(conf["ann"]):
row = l.rstrip().split()
chr2gene_pos[int(row[1])] = int(row[3])
for var in list(csq.values())[0]:
var["_internal_change"] = var["change"]
var["change"] = pp.reformat_mutations(var["change"], var["type"],
var["gene_id"], chr2gene_pos)
variants.append(var)
if not args.no_delly:
delly_bcf = bam_obj.run_delly()
deletions = delly_bcf.overlap_bed(conf["bed"])
for deletion in deletions:
tmp_change = pp.reformat_mutations(
"%(chr)s_%(start)s_%(end)s" % deletion, var["type"],
var["gene_id"], chr2gene_pos)
tmp = {
"genome_pos": deletion["start"],
"gene_id": deletion["region"],
"chr": deletion["chr"],
"freq": 1,
"type": "large_deletion",
"change": tmp_change
}
variants.append(tmp)
json.dump(variants,
open("%s/%s.pp-results.json" % (args.out_dir, args.prefix), "w"))
for x in [
".targets.bcf", ".targets.csq.bcf", ".targets.csq.bcf.csi",
".targets.delly.bcf", ".targets.delly.bcf.csi",
".targets.del_pos.bed", ".targets.gvcf.gz", ".targets.gvcf.gz.csi",
".targets.missing.bcf"
]:
if args.no_delly and "delly" in x: continue
pp.run_cmd("rm %s%s" % (args.prefix, x))
