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setup.py
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setup.py
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import helper
import statistics
import os
def sdf2gdf():
header = ['Locus', 'Total_Depth', 'Average_Depth_sample'] + ['Depth_for_' + x for x in args_.sample_list]
# peek into the first line
with open(args_.sdf) as f:
fields = next(f).strip().split('\t')
if len(fields) != len(header) - 1:
raise ValueError('Input SDF and sample list different in length')
with open(args_.output_prefix + '.gdf', 'w') as f1:
f1.write('\t'.join(header) + '\n')
with open(args_.sdf) as f2:
for line in f2:
fields = line.strip().split('\t')
chr = fields[0].replace('chr', '')
pos = fields[1]
locus = chr + ':' + pos
data = [int(x) for x in fields[2:]]
total = sum(data)
avg = round(statistics.mean(data), 2)
new_fields = [locus, total, avg] + data
f1.write('\t'.join([str(x) for x in new_fields]) + '\n')
def define():
with open(args_.table) as f:
s = 14 # first star allele index
header = next(f).strip().split('\t')
dat = [[] for x in header[s:]]
for line in f:
fields = line.strip().split('\t')
pos = fields[5]
var = fields[8]
wt = fields[9]
for i in range(s, len(fields)):
if fields[i] == '1':
dat[i - s].append('{}:{}>{}'.format(pos, wt, var))
with open(args_.output_prefix + '.stargazer-view.txt', 'w') as f:
for i in range(len(dat)):
f.write(header[s + i] + '\t' + ','.join(dat[i]) + '\n')
def slice():
sliced_vcf = helper.read_vcf_region(args_.vcf, args_.region)
helper.write_vcf(sliced_vcf, args_.output_prefix + '.vcf')
def check():
with open(args_.log, 'a') as f:
f.write(f'\n{args_.line_break}\n')
f.write('Step 1/2: Checking star_table.txt...\n\n')
star_data = []
snp_data = []
gene_list = []
with open(f'{args_.program_dir}/star_table.txt') as f:
star_header = next(f).strip().split('\t')
for line in f:
fields = line.strip().split('\t')
gene = fields[star_header.index('gene')]
if gene not in gene_list:
gene_list.append(gene)
star_data.append(fields)
with open(f'{args_.program_dir}/snp_table.txt') as f:
snp_header = next(f).strip().split('\t')
for line in f:
fields = line.strip().split('\t')
snp_data.append(fields)
snp_dict = {}
star_dict = {}
for gene in gene_list:
snp_dict[gene] = [x for x in snp_data if x[snp_header.index('gene')] == gene]
star_dict[gene] = [x for x in star_data if x[star_header.index('gene')] == gene]
def check_allele(x, gene, has_cadd30, type, has_lof):
snp_list = [x[snp_header.index('pos')] + ':' + x[snp_header.index('wt')] + '>' + x[snp_header.index('var')] for x in snp_dict[gene]]
cadd_dict = dict(zip(snp_list, [float(x[snp_header.index('cadd')]) for x in snp_dict[gene]]))
so_dict = dict(zip(snp_list, [x[snp_header.index('so')] for x in snp_dict[gene]]))
effect_dict = dict(zip(snp_list, [x[snp_header.index('effect')] for x in snp_dict[gene]]))
lof_dict = dict(zip(snp_list, [x[snp_header.index('lof')] for x in snp_dict[gene]]))
seen_list = []
if x == '.' or x == 'ref':
return
cadd30_seen = False
for snp in x.split(','):
pos = int(snp.split(':')[0])
wt = snp.split(':')[1].split('>')[0]
var = snp.split(':')[1].split('>')[1]
if snp not in snp_list:
raise ValueError(f'Unrecognized allele definition: {gene.upper()}{name} (#{number}) has {snp}')
if wt == var:
raise ValueError(f'Incorrect allele definition: {gene.upper()}{name} (#{number}) has {pos}-{wt}-{var}')
if seen_list and seen_list[-1] > pos:
raise ValueError(f'Allele definition is not coordinate sorted: {gene.upper()}{name} (#{number}) has {x}')
seen_list.append(pos)
if type == 'core':
if cadd_dict[snp] >= 30:
cadd30_seen = True
if has_cadd30 == 'no':
raise ValueError(f'Incorrect CADD information: {gene.upper()}{name} (#{number}) is marked as not having CADD30, but it has {snp} with CADD >= 30 ({cadd_dict[snp]})')
if has_lof == 'no' and lof_dict[snp] == 'yes':
raise ValueError(f'Incorrect LoF information: {gene.upper()}{name} (#{number}) is marked as not having LoF, but it has {snp} with LoF (Effect={effect_dict[snp]}; SO={so_dict[snp]})')
if type == 'core' and not cadd30_seen and has_cadd30 == 'yes':
raise ValueError(f'Incorrect CADD information: {gene.upper()}{name} (#{number}) is marked as having CADD30, but no such variant was found ({x}; {",".join({str(cadd_dict[y]) for y in x.split(",")})})')
for fields in star_data:
gene = fields[star_header.index('gene')]
name = fields[star_header.index('name')]
core = fields[star_header.index('core')]
tag = fields[star_header.index('tag')]
number = int(fields[star_header.index('number')])
has_cadd30 = fields[star_header.index('has_cadd30')]
has_lof = fields[star_header.index('has_lof')]
score = fields[star_header.index('score')]
if number == 1:
count = 0
count += 1
if number != count:
raise ValueError(f'Unmatched allele count: {gene.upper()}{name} (#{number}) should count {count}')
check_allele(core, gene, has_cadd30, 'core', has_lof)
check_allele(tag, gene, has_cadd30, 'tag', has_lof)
if has_cadd30 == 'yes' and score == 'unknown':
raise ValueError(f'Conflicting data: {gene.upper()}{name} (#{number}) is marked as having CADD30, but it has unknown score')
if has_lof == 'yes' and score == 'unknown':
raise ValueError(f'Conflicting data: {gene.upper()}{name} (#{number}) is marked as having LoF, but it has unknown score')
with open(args_.log, 'a') as f:
f.write(f'Status: Completed\n\n')
f.write('Allele counts: Pass\n')
f.write('Allele definition: Pass\n')
f.write('CADD30 information: Pass\n')
f.write('LoF information: Pass\n')
f.write(f'\n{args_.line_break}\n')
with open(args_.log, 'a') as f:
f.write('Step 2/2: Checking snp_table.txt...\n\n')
for fields in snp_data:
gene = fields[snp_header.index('gene')]
pos = fields[snp_header.index('pos')]
hg = fields[snp_header.index('hg')]
var = fields[snp_header.index('var')]
wt = fields[snp_header.index('wt')]
rev = fields[snp_header.index('rev')] == 'yes'
impact = fields[snp_header.index('impact')]
cadd = float(fields[snp_header.index('cadd')])
causal = fields[snp_header.index('causal')]
name = f'{gene.upper()}-{pos}-{hg}-{var}-{wt}'
number = int(fields[1])
current_pos = int(pos)
if number == 1:
previous_pos = current_pos
previous_var = var
count = 0
count += 1
if current_pos < previous_pos:
raise ValueError(f'Variants are not coordinate sorted: {name} (#{number}) is after {previous_pos}')
if current_pos == previous_pos and var < previous_var:
raise ValueError(f'Variants are not alphabetically sorted: {name} is after {previous_var}')
previous_pos = current_pos
previous_var = var
if number != count:
raise ValueError(f'Unmatched variant count: {name} (#{number}) should count {count}')
if rev and (hg != var or wt == hg or wt == var):
raise ValueError(f'Conflicting data: {name} is marked as revertant')
if cadd >= 30 and impact != 'high_impact':
raise ValueError(f'Conflicting data: {name} has CADD >= 30 and {impact}')
# check associated phenotype
phenotype = ''
for star in star_dict[gene]:
if ',' in star[star_header.index('core')]:
continue
if star[star_header.index('sv')] != '.':
continue
if f'{pos}:{wt}>{var}' in star[star_header.index('core')].split(','):
phenotype = star[star_header.index('phenotype')]
if not phenotype:
phenotype = '.'
if causal != phenotype:
raise ValueError(f'Incorrect phenotype information: {name} ({",".join(causal)}) should have {phenotype}')
if causal not in ['.', 'unknown_function', 'normal_function', 'IV/Normal'] and impact != 'high_impact':
raise ValueError(f'Incorrect impact information: {name} ({impact}; {causal}) should have high_impact')
with open(args_.log, 'a') as f:
f.write(f'Status: Completed\n\n')
f.write('Variant counts: Pass\n')
f.write('Coordinate sorted: Pass\n')
f.write('Alphabetically sorted: Pass\n')
f.write('Reverting variants: Pass\n')
f.write('Variant impact: Pass\n')
f.write('Variant phenotype: Pass\n')
f.write(f'\n{args_.line_break}')
def merge():
if not args_.vcf_dir:
raise ValueError('Required argument "--vcf_dir" not found')
vcfs = []
region = None
if args_.target_gene and args_.region:
raise ValueError('Arguments "--target_gene" and "--region" cannot be used together')
if args_.target_gene:
region = helper.get_region(args_.target_gene)
if args_.region:
region = args_.region
for r, d, f in os.walk(args_.vcf_dir):
for file in f:
if file.endswith('vcf'):
vcf_path = os.path.join(r, file)
if region:
vcf = helper.read_vcf_region(vcf_path, region)
else:
vcf = helper.read_vcf_simple(vcf_path)
vcfs.append(vcf)
merged_vcf = vcfs[0]
size = len(merged_vcf.data)
for vcf in vcfs[1:]:
sample_id = vcf.header[9]
merged_vcf.header.append(sample_id)
for i in range(size):
data = vcf.data[i][9]
merged_vcf.data[i].append(data)
helper.write_vcf(merged_vcf, f'{args_.output_prefix}.vcf')
SUBTOOLS = {'sdf2gdf': sdf2gdf, 'define': define, 'slice': slice, 'check': check, 'merge': merge}
DESCRIPTION = f'''tool description:
create various files necessary for running Stargazer
getting help:
stargazer.py setup -h
available subtools:
sdf2gdf
create gdf file from sdf file
define
define star alleles using variants from the SNP table
slice
create sliced VCF file
check
check the SNP and star allele tables
merge
create new VCF file by merging multiple VCF files
main usages:
run the sdf2gdf subtool
stargazer.py setup sdf2gdf -o OUTPUT_PREFIX --sample_list [SAMPLE [SAMPLE ...]] --sdf SDF
run the define subtool
stargazer.py setup define -o OUTPUT_PREFIX --table TABLE
run the slice subtool
stargazer.py setup slice -o OUTPUT_PREFIX --region REGION --VCF VCF
run the check subtool
stargazer.py setup check -o OUTPUT_PREFIX
run the merge subtool
stargazer.py setup merge -o OUTPUT_PREFIX --vcf_dir VCF_DIR [--region REGION] [-t TARGET_GENE]
'''
def run(args):
global args_
args_ = args
SUBTOOLS[args_.subtool]()