def get_varcnt(invcf):
'''Computes number of exonic variants per gene
Args:
invcf(str): VARANT annotated VCF
Returns:
varcnt(dictionary): Returns exonic variant count per gene
'''
varcnt = {}
vcf = VCFParser(invcf)
for rec in vcf:
vcf.parseinfo(rec)
ant = vp.parse(rec.info)
prant = vp.prio_trans(ant)
cache = []
for altid, antinfo in prant.items():
if altid != 'intergenic':
genelist = antinfo.keys()
for gene in genelist:
txant = antinfo[gene]['TRANSCRIPT']
key = (rec.chrom, gene)
if 'CodingExonic' in txant.region.split('_')\
and txant.mutation != 'Syn' \
and rec.info['ESPAF'] < 5.0 and key not in cache: #TODO (to be replaced by ExAC?)
cache.append(key)
if key not in varcnt:
varcnt[key] = 1
else:
varcnt[key] += 1
return varcnt
def vtexonic(self, rec):
"""Check if variant is in the exonic region (varant annotated)"""
hpm = 'StopGain StopLoss StartLoss NonSyn FrameShiftInsert FrameShiftDelete NonFrameShiftInsert NonFrameShiftDelete'.split(
)
splc = ['SpliceDonor', 'SpliceAcceptor']
warn = ['CDS_NOT_MULTIPLE_OF_3']
vpop = vp.parse(rec.info)
for altnum, val in vpop.items():
for gene, gd in val.items():
if gd:
for t in gd['TRANSCRIPTS']:
if t.mutation in hpm:
return True
if t.splice in splc:
return True
if t.warning in warn:
return True
return False
def ranking_vcf(self):
'''
this function is obsolete and replaced by vcf2xls_varant()
'''
import gcn.lib.io.vcf as vcf
job_name = 'ranking_vcf'
msg = 'annotating Divine prediction score into filtered VCF ... [%s;%s]' % (
job_name, self.vcf)
lib_utils.msgout('notice', msg)
self.logger.info(msg)
ranked_vcf = '%s.ranked' % self.vcf
ostream = open(ranked_vcf, 'w')
v = vcf.VCFParser(self.vcf)
v.add_meta_info("DVN", "1", "Float",\
"Gene damage score predicted by Divine:%s"%self.command)
v.writeheader(ostream)
for rec in v:
v.parseinfo(rec)
vpop = vp.parse(rec.info)
max_dmg_sc = 0.
for altnum, val in vpop.items():
for gene, gd in val.items():
if gene in self.gene_dmg:
if self.gene_dmg[gene] > max_dmg_sc:
max_dmg_score = self.gene_dmg[gene]
rec.info.DVN = max_dmg_score
v.write(ostream, rec)
ostream.close()
v.stream.close()
os.rename(ranked_vcf, self.vcf)
msg = 'done. [%s]' % job_name
lib_utils.msgout('notice', msg)
self.logger.info(msg)
def _store_variants(self, beta_fits):
'''
collect essential info on each variant
'''
job_name = '_store_variants'
msg = 'collecting variant information and class label to determine genetic damage [%s;%s]...' % (
job_name, self.vcf)
lib_utils.msgout('notice', msg)
self.logger.info(msg)
mutation_info = []
v = vcf.VCFParser(self.vcf)
for rec in v:
v.parseinfo(rec)
varlist = normalize_variant(rec.chrom, rec.pos, rec.ref, rec.alt)
mut_type = varlist[0][-1]
if ':' in rec.id[0]:
mut_type = 'mnp'
# collect conservation prediction score (CADD and GERP++)
cadd_aa = './.'
px_cadd = None
if rec.info.CADD_raw:
# to get CADD_raw (average)
px_cadd, cadd_aa = vcf.get_CADD_scores(mut_type,
rec.info.CADD_aa,
rec.info.CADD_raw,
beta_fits)
# to get GERP++ score
px_gerp = None
if rec.info.GerpConserve:
px_gerp = vcf.get_GERP_scores(mut_type, cadd_aa,
rec.info.GerpRSScore, beta_fits)
# which score can be chosen
px = 0.5
if self.cadd > 0 and px_cadd is not None:
px = px_cadd
elif px_gerp is not None:
px = px_gerp
vpop = vp.parse(rec.info)
genes = []
# to get MAF in the order of ExAC, ESP, and 1K
if rec.info.EXACDB:
maf = float(rec.info.EXACAF[0])
elif rec.info.ESPDB:
maf = float(rec.info.ESPAF[0])
elif rec.info.KGDB:
maf = float(rec.info.KGAF[0])
else:
maf = 0.
# to compute a significance of MAF
maf_offset = 0.
if maf > 0:
maf_offset = (
1. - self.dm.beta1 * math.exp(1000. * maf)) / self.dm.beta2
if maf_offset < 0.:
maf_offset = 0.
# to get transcript length
for altnum, val in vpop.items():
# for each gene involved with the variant
for gene, gd in val.items():
protein_len = self.dm.avg_protein_len
if gd:
for t in gd['TRANSCRIPTS']:
if t.protein_len:
protein_len = float(t.protein_len)
break
# store a set of essential annotation to be used for genetic damage
if gene not in genes:
mutation_info.append([
gene, rec.info.INDEL, rec.info.CLASS_TAG,
protein_len, px, maf_offset
])
genes.append(gene)
# done reading filterd VCF file
v.stream.close()
msg = 'done. [%s]' % job_name
lib_utils.msgout('notice', msg)
self.logger.info(msg)
return mutation_info
def vtexonic(self, rec):
"""
Check if variant is in the exonic region (varant annotated)
TODO: apply a max distance of donor/acceptor distance; a cascade filter
"""
hpm = 'StopGain StopLoss StartLoss NonSyn FrameShiftInsert FrameShiftDelete NonFrameShiftInsert NonFrameShiftDelete'.split(
)
splc = ['SpliceDonor', 'SpliceAcceptor']
splc_coding = ['ESS', 'ESE']
warn = ['CDS_NOT_MULTIPLE_OF_3']
intronic = ['CodingIntronic', 'NonCodingIntronic']
exonic = ['CodingExonic', 'NonCodingExonic']
utr = ['UTR5', 'UTR3']
vpop = vp.parse(rec.info)
regions = []
for altnum, val in vpop.items():
for gene, gd in val.items():
if gd:
#to extract recessive/dominant
inherited = 'as_recessive'
for mim in gd['MIM_PHENS']:
if 'AUTOSOMAL_DOMINANT' in mim: #TODO:https://ghr.nlm.nih.gov/handbook/inheritance/inheritancepatterns
inherited = 'as_dominant'
for t in gd['TRANSCRIPTS']:
if self.regincl == 'all' or t.region in self.regincl:
region = [
t.trans_id, t.region, t.protein_len, inherited
]
if t.region in exonic: #CodingExonic,NonCodingExonic
if t.splice in splc_coding:
region_tag = 'splc_coding'
if t.mutation in hpm:
region_tag += ';hpm'
region.append(region_tag)
regions.append(region)
elif t.mutation in hpm:
region.append('hpm')
regions.append(region)
elif t.warning in warn:
region.append('warn')
regions.append(region)
elif self.dconf['splice_dist'] > 0:
dists = []
tcdna = t.cdna.split('_')
T = len(tcdna)
if t.region in intronic:
mObj = re.search(r'c\.(.+)[+-](\d+)',
tcdna[0])
if mObj:
dists.append(int(mObj.group(2)))
if T > 1:
mObj = re.search(
r'(.+)[+-](\d+)', tcdna[1])
if mObj:
dists.append(int(mObj.group(2)))
elif t.region in utr:
mObj = re.search(r'c\.[-\*](\d+)',
tcdna[0])
if mObj:
dists.append(int(mObj.group(1)))
if T > 1:
mObj = re.search(
r'[-\*](\d+)', tcdna[1])
if mObj:
dists.append(int(mObj.group(1)))
if dists:
if min(dists) <= self.dconf['splice_dist']:
region.append('splc_ext_intron')
regions.append(region)
if regions:
return regions
elif self.dconf['regulome']:
for altnum, val in vpop.items():
for gene, gd in val.items():
if gd:
inherited = 'as_recessive'
for mim in gd['MIM_PHENS']:
if 'AUTOSOMAL_DOMINANT' in mim: #TODO:https://ghr.nlm.nih.gov/handbook/inheritance/inheritancepatterns
inherited = 'as_dominant'
break
for t in gd['TRANSCRIPTS']:
if rec.info.RegulomeScore:
return [
t.trans_id, t.region, t.protein_len,
inherited, 'regulome'
]
else:
return [
t.trans_id, None, None, inherited, None
]
def in_gene(self, rec, genes):
vpop = vp.parse(rec.info)
for altnum, val in vpop.items():
for gene, gd in val.items():
if gene in genes:
return True
def get_gene_data(vcffile, pedigree, GQ_THRES):
"""Retrieves gene_transcript wise variants where there exits at least one
frameshift/stopgain mutation.
Args:
- vcffile(str): Input VCF file.
Note - VCF should be VARANT annotated.
- pedigree(list): [Father SampleID, Mother SampleID,
Child SampleID]. Expects the order in which
the SampleIDs are mentioned above.
- GQ_THRES(int): Threshold Genotype Quality
Returns:
- gene_data_phased(dictionary): Genotype Phased gene_transcript
wise variants where there is
at least one Frameshift/
Stopgain mutation.
- gene_data_unphased(dictionary): Genotype Unphased gene_transcript
wise variants where there is
at least one Frameshift/Stopgain
mutation in homozygous state.
"""
data1 = {}
data2 = {}
FILTER = ['PASS', 'VQSRTrancheSNP99.00to99.90']
v = vcf.VCFParser(vcffile)
for rec in v:
v.parseinfo(rec)
v.parsegenotypes(rec)
varfltr = rec['filter']
if len([True for flt in FILTER if flt in varfltr]) > 0:
genotypes = check_genotype(rec, pedigree, GQ_THRES)
if genotypes:
pg = phase(*genotypes)
if pg[1] == '|':
c1, c2 = int(pg[0]), int(pg[-1])
va = vp.parse(rec.info)
for idx, altid in enumerate([c1, c2]):
if altid != 0:
if altid in va:
gene = va[altid].keys()[0]
if len(va[altid][gene]) > 0:
for ta in va[altid][gene]['TRANSCRIPTS']:
if ta.region == 'CodingExonic':
trans_id = ta.trans_id
key = (rec.chrom, rec.pos, \
','.join(rec.id), rec.ref, \
rec.alt[altid - 1], altid)
gi = (gene, trans_id)
if gi not in data1:
data1[gi] = [{}, {}]
data1[gi][idx][key] = \
[ta.mutation,
pg,
genotypes[0],
genotypes[1]]
else:
data1[gi][idx][key] = \
[ta.mutation,
pg,
genotypes[0],
genotypes[1]]
else:
c1, c2 = int(pg[0]), int(pg[-1])
va = vp.parse(rec.info)
for altid in [c1, c2]:
if altid != 0:
if altid in va:
gene = va[altid].keys()[0]
if len(va[altid][gene]) > 0:
for ta in va[altid][gene]['TRANSCRIPTS']:
if ta.region == 'CodingExonic':
trans_id = ta.trans_id
key = (rec.chrom, rec.pos, \
','.join(rec.id), rec.ref, \
rec.alt[altid - 1], altid)
gi = (gene, trans_id)
if gi not in data2:
data2[gi] = [{}]
data2[gi][0][key] = \
[ta.mutation,
pg,
genotypes[0],
genotypes[1]]
else:
data2[gi][0][key] = \
[ta.mutation,
pg,
genotypes[0],
genotypes[1]]
gene_data_phased = {}
for k, v in data1.items():
for e in v:
if len(e) > 0:
if len(e.values()) > 1:
if len([True for mut in [x[0] for x in e.values()] \
if mut.startswith('FrameShift') \
or mut == 'StopGain']) > 0:
if k not in gene_data_phased:
gene_data_phased[k] = [e]
else:
gene_data_phased[k].append(e)
del data1
gene_data_unphased = {}
for k, v in data2.items():
for e in v:
if len(e) > 0:
if len(e.values()) > 1:
if len([True for y in [(x[0], x[1]) for x in e.values()] \
if (y[0].startswith('FrameShift') or \
y[0] == 'StopGain') and \
int(y[1][0]) == int(y[1][2])]) > 0:
if k not in gene_data_unphased:
gene_data_unphased[k] = [e]
else:
gene_data_unphased[k].append(e)
del data2
return gene_data_phased, gene_data_unphased
def _extract_mutation_info(self,beta_fits):
'''
objective: to extract (gene_qsymbol,mutation_type,variant_class_tag,transcript_length,insillico_prediction_score,MAF_significance_offset,zygosity) from annotated/filtered VCF file
to transfer genmod information to class_tag and also get rid of some redundancy in VCF info
'''
job_name = '_extract_mutation_info'
msg='collecting variant information and class label to determine genetic damage [%s;%s]...'%(job_name,self.vcf)
lib_utils.msgout('notice',msg);self.logger.info(msg)
mutation_info = []
if self.proband_id:
rewrite_vcf = True
v = vcf.VCFParser(self.vcf,sampleids=[self.proband_id])
pdom,pdom0 = self.gather_pdomain_scores(v)
v.stream.close()
vcf_tmp = self.vcf+'.tmp'
ostream = open(vcf_tmp, 'w')
rmInfo = ['Exonic','Annotation','Compounds']
v = vcf.VCFParser(self.vcf)
v.writeheader(ostream,to_del_info = rmInfo)
else:
rewrite_vcf = False
v = vcf.VCFParser(self.vcf)
pdom,pdom0 = self.gather_pdomain_scores(v)
v.stream.close()
v = vcf.VCFParser(self.vcf)
msg = 'Importing max transcript length for each gene ...'
lib_utils.msgout('notice', msg);
self.logger.info(msg)
refgene = Refgene()
cds_lens = refgene.get_max_cds_length()
tx_lens = {}
for gene, cds_len in cds_lens.iteritems():
tx_lens[gene] = int(cds_len/3.)
ridx = 0
for rec in v:
v.parseinfo(rec)
#to remove redundant gene symbols annotated by genmod but add transcript version
if rewrite_vcf:
for rkey in rmInfo:
v.delete_info(rec, rkey)
if rec.info.GeneticModels:
genmod_tag = lib_ped.parse_genmod_inherit_model(\
rec.info.GeneticModels[0].split(':')[1])
rec.info.CLASS_TAG += genmod_tag
v.write(ostream, rec)
varlist = normalize_variant(rec.chrom, rec.pos, rec.ref, rec.alt)
mut_type = varlist[0][-1]
if ':' in rec.id[0]:
mut_type = 'mnp'
# collect conservation prediction score (CADD and GERP++)
cadd_aa = './.'
px_cadd = None
if rec.info.CADD_raw:
# to get CADD_raw (average)
px_cadd, cadd_aa = vcf.get_CADD_scores(mut_type, rec.info.CADD_aa, rec.info.CADD_raw, beta_fits)
# to get GERP++ score
px_gerp = None
if rec.info.GerpConserve:
px_gerp = vcf.get_GERP_scores(mut_type, cadd_aa, rec.info.GerpRSScore, beta_fits)
# which score can be chosen
px = 0.5
if self.cadd>0 and px_cadd is not None:
px = px_cadd
elif px_gerp is not None:
px = px_gerp
vpop = vp.parse(rec.info)
genes = []
# to get MAF in the order of ExAC, ESP, and 1K
if rec.info.EXACDB:
maf = get_min_maf(rec.info.EXACAF[0])
elif rec.info.ESPDB:
maf = get_min_maf(rec.info.ESPAF[0])
elif rec.info.KGDB:
maf = get_min_maf(rec.info.KGAF[0])
else:
maf = 0.
# to compute a significance of MAF
maf_offset = self.dm.get_maf_xoffset(maf)
#pdom.iloc[ridx]==ridx
pdom_idx = pdom.index[pdom.ridx == ridx].tolist()
if pdom_idx:
patho_p = pdom.phat_lo[pdom_idx[0]]
patho_pden = pdom.patho_dens_p[pdom_idx[0]]
else:
# assign a default pathogenic domain value (15% quantile value)
patho_p = pdom0.phat_lo
patho_pden = pdom0.patho_dens_p
vartype = get_var_type(rec.ref,rec.alt)
# to get transcript length
for altnum, val in vpop.items():
# for each gene involved with the variant
for gene, gd in val.items():
protein_len = self.dm.avg_protein_len
if gene in tx_lens:
protein_len = tx_lens[gene]
# store a set of essential annotation to be used for genetic damage
if gene not in genes:
mutation_info.append([gene, vartype, rec.info.CLASS_TAG, protein_len, px, maf_offset, patho_p, patho_pden])
genes.append(gene)
ridx += 1
# done reading filterd VCF file
if rewrite_vcf:
v.stream.close()
ostream.close()
os.rename(vcf_tmp,self.vcf)
msg = 'done. [%s]'%job_name
lib_utils.msgout('notice',msg); self.logger.info(msg)
return mutation_info
def _load(invcf, thres_af, nmethod, data=None, sc=1):
if not data:
data = {}
vcfs = VCFParser(invcf)
samples = vcfs.samples
for rec in vcfs:
vcfs.parseinfo(rec)
vcfs.parsegenotypes(rec)
if not _is_HQVar(rec.filter): # Checks variant is PASS
continue
for sid in samples:
if sid not in data:
data[sid] = {}
gi = rec[sid]
gt, gq = gi.GT, gi.GQ
# Checks if genotype is not reference or GQ >= 30
if not _genotype_check(gt, gq):
continue
altid = int(gt.split('/')[1])
var = rec.chrom + ':' + str(rec.pos) + ':' + rec.ref +\
':' + rec.alt[altid - 1]
af, flag = isRare(altid, rec.info, thres_af)
if not flag: # Checks if variant is not Rare (AF < 5%) in ExAC
continue
if 'LCR' in rec.info:
continue
if 'CLNDBN' in rec.info:
dn = rec.info.CLNDBN[altid - 1]
sig_num = rec.info.CLNSIG[altid - 1]
if '|' in sig_num:
sig_num = [int(e) for e in sig_num.split('|') if e != '.']
if sig_num:
sig_num.sort()
sig_num = sig_num[-1]
cln_sig = CLNSIG_MAP[sig_num]
else:
cln_sig, dn = '', ''
elif sig_num != '.':
sig_num = int(sig_num)
cln_sig = CLNSIG_MAP[sig_num]
else:
dn, cln_sig = '', ''
else:
dn, cln_sig = '', ''
if 'LCR' in rec.info:
lcr = 'LCR'
else:
lcr = ''
if 'CADD_phred' in rec.info:
val = rec.info['CADD_phred'][altid - 1]
if val == '.':
cadd = ''
else:
cadd = float(val)
else:
cadd = ''
if len(rec.ref) == len(rec.alt[altid - 1]) and len(rec.ref) == 1:
ada_score, rf_score = get_dbscSNV_ant(rec.chrom, rec.pos,
rec.ref,
rec.alt[altid - 1])
if (ada_score and ada_score > 0.6) or (rf_score
and rf_score > 0.6):
scpred = 'Damaging'
else:
scpred = ''
else:
ada_score, rf_score, scpred = '', '', ''
sc_ant = [scpred, ada_score, rf_score]
#Parse annotation and prioritize transcript
pa = vp.prio_trans(vp.parse(rec.info))
# Ignore the intergenic variants
if altid not in pa:
continue
eqtl_flag = False
for gene, ant in pa[altid].items():
ta = ant['TRANSCRIPT']
key = ta.trans_id + '_' + ta.aa
snps3d_pred = ['', '', '', '']
# SC-1 variant present in Clinvar as Pathogenic or Likely Pathogenic
if sc == 1: # Search Criteria 1
if (cln_sig in ['Pathogenic', 'Likely pathogenic']):
if gene not in data[sid]:
data[sid][gene] = []
data[sid][gene].append(
(ta, af, cadd, gt, eqtl_flag, var, dn, cln_sig,
lcr, sc_ant, snps3d_pred))
# SC-2 variant is protein altering + SC-1
if sc == 2: # Search Criteria 2
if (_is_PASnv(ta) and _is_Damaging(altid, rec.info, ta,
snps3d_pred, nmethod)) or _is_NonSense(ta) \
or _is_Splicing(ta) or _is_PAIndel(ta) or \
scpred == 'Damaging' or (cln_sig in ['Pathogenic',
'Likely pathogenic']):
if gene not in data[sid]:
data[sid][gene] = []
data[sid][gene].append(
(ta, af, cadd, gt, eqtl_flag, var, dn, cln_sig,
lcr, sc_ant, snps3d_pred))
# SC-5 in intronic and UTR variants + SC-1 + SC-2
if sc == 3: # Search Criteria 3
if _is_Intronic(ta) or _is_UTR(ta) or _is_PASnv(ta) or \
_is_NonSense(ta) or _is_Splicing(ta) or _is_PAIndel(ta) \
or scpred == 'Damaging' or cln_sig in ['Pathogenic',
'Likely pathogenic']:
if gene not in data[sid]:
data[sid][gene] = []
data[sid][gene].append(
(ta, af, cadd, gt, eqtl_flag, var, dn, cln_sig,
lcr, sc_ant, snps3d_pred))
return data
