def run_from_args(args): vcf = Vcf() vcf_out=sys.stdout in_header = True header_lines = list() with su.InputStream(args.manta_vcf) as input_stream: for line in input_stream: if in_header: header_lines.append(line) if line[0:6] == '#CHROM': in_header=False vcf.add_header(header_lines) vcf.add_info('PRPOS', '1', 'String', 'Breakpoint probability dist') vcf.add_info('PREND', '1', 'String', 'Breakpoint probability dist') vcf.add_info('STRANDS', '.', 'String', 'Strand orientation of the adjacency in BEDPE format (DEL:+-, DUP:-+, INV:++/--') vcf.add_info('SU', '.', 'Integer', 'Number of pieces of evidence supporting the variant across all samples') vcf.add_info('PE', '.', 'Integer', 'Number of paired-end reads supporting the variant across all samples') vcf.add_info('SR', '.', 'Integer', 'Number of split reads supporting the variant across all samples') vcf.add_info('INSLEN_ORIG', '.', 'Integer', 'Original insertion length') vcf.add_info('CIPOS95', '2', 'Integer', 'Confidence interval (95%) around POS for imprecise variants') vcf.add_info('CIEND95', '2', 'Integer', 'Confidence interval (95%) around END for imprecise variants') vcf.add_info('SECONDARY', '0', 'Flag', 'Secondary breakend in a multi-line variant') vcf_out.write(vcf.get_header()+'\n') else: v = Variant(line.rstrip().split('\t'), vcf) convert_variant(v, args.max_ins) vcf_out.write(v.get_var_string()+"\n")
def run_from_args(args): deletion_size_map, max_size = load_deletion_sizes(args.threshold_file) with su.InputStream(args.input) as stream: variant_stream = VCFReader(stream) args.output.write(variant_stream.vcf_obj.get_header()) args.output.write('\n') return set_missing(variant_stream, deletion_size_map, args.output, max_size, args.sr_cutoff)
def run_from_args(args): # sys.stderr.write(args.vcf_in) if args.tSet is None: if args.method!="large_sample": sys.stderr.write("Training data required for naive Bayes or hybrid classifiers\n") parser.print_help() sys.exit(1) with su.InputStream(args.input) as stream: chrom_names = args.sex_chrom.strip().split(',') sex_chrom_names = set(chrom_names) for chrom in chrom_names: sex_chrom_names.add(chromosome_prefix(chrom)) sys.stderr.write('sex chromosome names are: {0}\n'.format(str(sex_chrom_names))) run_reclassifier( stream, args.vcf_out, args.gender, sex_chrom_names, args.ae_path, args.f_overlap, args.exclude, args.slope_threshold, args.rsquared_threshold, args.tSet, args.method, args.diag_outfile )
def run_from_args(args): # sys.stderr.write(args.vcf_in) if args.tSet is None: if args.method!="large_sample": sys.stderr.write("Training data required for naive Bayes or hybrid classifiers\n") parser.print_help() sys.exit(1) with su.InputStream(args.input) as stream: run_reclassifier(stream, args.vcf_out, args.gender, args.ae_path, args.f_overlap, args.exclude, args.slope_threshold, args.rsquared_threshold, args.tSet, args.method, args.diag_outfile)
def test_plain_iteration(self): test_directory = os.path.dirname(os.path.abspath(__file__)) test_data_dir = os.path.join(test_directory, 'test_data', 'utils') test_input = os.path.join(test_data_dir, 'file.txt') stream = su.InputStream(test_input) for line in stream: sys.stdout.write(line) stream.close() self.assertTrue(stream.handle.closed)
def test_context_manager(self): test_directory = os.path.dirname(os.path.abspath(__file__)) test_data_dir = os.path.join(test_directory, 'test_data', 'utils') test_input = os.path.join(test_data_dir, 'file.txt.gz') temporary_obj = None with su.InputStream(test_input) as stream: temporary_obj = stream for line in stream: sys.stdout.write(line) self.assertTrue(temporary_obj.closed)
def run_from_args(args): with su.InputStream(args.input, args.tempdir) as stream: bedpeToVcf(stream, args.output)
def test_init_hyphen(self): new_handle = su.InputStream('-') self.assertIs(new_handle.handle, sys.stdin)
def run_from_args(args): with su.InputStream(args.input) as stream: pruner = Pruner(args.max_distance, args.eval_param) pruner.cluster_bedpe(stream, args.output, args.is_sorted)
def run_from_args(args): with su.InputStream(args.vcf_in) as stream: run_gt_refine(stream, args.vcf_out, args.diag_outfile, args.gender)
def run_from_args(args): with su.InputStream(args.input) as stream: return vcfToBedpe(stream, args.output)
def run_from_args(args): with su.InputStream(args.bedpe) as stream: processBEDPE(stream, args.name, args.dist)
def run_from_args(args): with su.InputStream(args.input) as stream: return sname_filter(stream, args.filter_file, args.output, args.complement)
def run_from_args(args): with su.InputStream(args.bedpe) as stream: bedpeToVcf(stream, args.output)
def run_from_args(args): with su.InputStream(args.input_vcf) as stream: sv_readdepth(stream, args.sample, args.root, args.window, args.output_vcf, args.cnvnator, args.coordinates)
def run_from_args(args): with su.InputStream(args.input) as stream: processBEDPE(stream, args.name, args.dist, args.output)
def run_from_args(args): with su.InputStream(args.input_vcf, args.tempdir) as input_stream: updater = UpdateInfo(input_stream) updater.execute()
def run_from_args(args): with su.InputStream(args.input) as stream: run_pairwise_ld(stream, args.exclude, args.ld_outfile, args.winsz)