def _map_line_to_json(fields):
vid = fields[0].split(":")
chrom = re.search(r'[1-9]+', vid[0]).group()
if chrom == '23':
chrom = chrom.replace('23', 'X')
HGVS = "chr%s:%s" % (chrom, vid[1])
# load as json data
if HGVS is None:
return
one_snp_json = {
"_id": HGVS,
"emv": {
"gene": fields[2],
"variant_id": fields[3],
"exon": fields[4],
"egl_variant": fields[5],
"egl_protein": fields[6],
"egl_classification": fields[7],
"egl_classification_date": fields[8],
"hgvs": fields[9].split(" | "),
"clinvar_rcv": fields[10],
}
}
return unlist(dict_sweep(value_convert(one_snp_json), vals=[""]))
def _map_line_to_json(fields):
assert len(fields) == VALID_CO_NUMBER
HGVS = fields[1]
if HGVS is None:
return
one_snp_json = {
"_id": HGVS,
'drugbank':
{
'drug': fields[2],
'interacting_gene_or_enzyme': fields[3],
'snp_rs_id': fields[0],
'allele_name': fields[4],
'defining_change': fields[5],
'adverse_reaction': fields[6],
'references': fields[7]
}
}
return dict_sweep(one_snp_json, ['Not Available'])
def _map_line_to_json(item):
chrom = item.CHROM
chromStart = item.POS
ref = item.REF
info = item.INFO
hpo_count=item.INFO['HPO_CT']
for alt in item.ALT:
alt = str(alt)
(HGVS, var_type) = get_hgvs_from_vcf(chrom, chromStart, ref, alt, mutant_type=True)
if HGVS is None:
return
one_snp_json = {
"_id": HGVS,
"geno2mp": {
"hpo_count": hpo_count,
}
}
obj = (dict_sweep(unlist(value_convert(one_snp_json)), [None]))
yield obj
def _map_line_to_json(item):
chrom = item.CHROM
chromStart = item.POS
ref = item.REF
info = item.INFO
hpo_count = item.INFO['HPO_CT']
for alt in item.ALT:
alt = str(alt)
(HGVS, var_type) = get_hgvs_from_vcf(chrom,
chromStart,
ref,
alt,
mutant_type=True)
if HGVS is None:
return
one_snp_json = {
"_id": HGVS,
"geno2mp": {
"hpo_count": hpo_count,
}
}
obj = (dict_sweep(unlist(value_convert(one_snp_json)), [None]))
yield obj
def _map_line_to_json(fields):
assert len(fields) == VALID_COLUMN_NO
rsid = fields[8]
# load as json data
if rsid is None:
return
url = "http://myvariant.info/v1/query?q=dbsnp.rsid:" + rsid + "&fields=_id"
r = requests.get(url)
for hits in r.json()["hits"]:
HGVS = hits["_id"]
one_snp_json = {
"_id": HGVS,
"grasp": {
"hg19": {"chr": fields[5], "pos": fields[6]},
"hupfield": fields[1],
"last_curation_date": fields[2],
"creation_date": fields[3],
"srsid": fields[4],
"publication": {
"journal": fields[16],
"title": fields[17],
"pmid": fields[7],
"snpid": fields[8],
"location_within_paper": fields[9],
"p_value": fields[10],
"phenotype": fields[11],
"paper_phenotype_description": fields[12],
"paper_phenotype_categories": fields[13],
"date_pub": fields[14],
},
"includes_male_female_only_analyses": fields[18],
"exclusively_male_female": fields[19],
"initial_sample_description": fields[20],
"replication_sample_description": fields[21],
"platform_snps_passing_qc": fields[22],
"gwas_ancestry_description": fields[23],
"discovery": {
"total_samples": fields[25],
"european": fields[26],
"african": fields[27],
"east_asian": fields[28],
"indian_south_asian": fields[29],
"hispanic": fields[30],
"native": fields[31],
"micronesian": fields[32],
"arab_me": fields[33],
"mixed": fields[34],
"unspecified": fields[35],
"filipino": fields[36],
"indonesian": fields[37],
},
"replication": {
"total_samples": fields[38],
"european": fields[39],
"african": fields[40],
"east_asian": fields[41],
"indian_south_asian": fields[42],
"hispanic": fields[43],
"native": fields[44],
"micronesian": fields[45],
"arab_me": fields[46],
"mixed": fields[47],
"unspecified": fields[48],
"filipino": fields[49],
"indonesian": fields[50],
},
"in_gene": fields[51],
"nearest_gene": fields[52],
"in_lincrna": fields[53],
"in_mirna": fields[54],
"in_mirna_bs": fields[55],
"oreg_anno": fields[61],
"conserv_pred_tfbs": fields[62],
"human_enhancer": fields[63],
"rna_edit": fields[64],
"polyphen2": fields[65],
"sift": fields[66],
"ls_snp": fields[67],
"uniprot": fields[68],
"eqtl_meth_metab_study": fields[69],
},
}
return list_split(dict_sweep(unlist(value_convert(one_snp_json)), [""]), ",")
def _map_line_to_json(cp):
clinical_siginificance = cp.ReferenceClinVarAssertion.\
ClinicalSignificance.Description
rcv_accession = cp.ReferenceClinVarAssertion.ClinVarAccession.Acc
review_status = cp.ReferenceClinVarAssertion.ClinicalSignificance.\
ReviewStatus
last_evaluated = cp.ReferenceClinVarAssertion.ClinicalSignificance.\
DateLastEvaluated
CLINVAR_ID = cp.ReferenceClinVarAssertion.MeasureSet.ID
number_submitters = len(cp.ClinVarAssertion)
# some items in clinvar_xml doesn't have origin information
try:
origin = cp.ReferenceClinVarAssertion.ObservedIn[0].Sample.Origin
except:
origin = None
# MeasureSet.Measure return a list, there might be multiple
# Measure under one MeasureSet
for Measure in cp.ReferenceClinVarAssertion.MeasureSet.Measure:
variation_type = Measure.Type
# exclude any item of which types belong to
# 'Variation', 'protein only' or 'Microsatellite'
if variation_type == 'Variation' or variation_type\
== 'protein only' or variation_type == 'Microsatellite':
continue
allele_id = Measure.ID
chrom = None
chromStart = None
chromEnd = None
ref = None
alt = None
if Measure.SequenceLocation:
for SequenceLocation in Measure.SequenceLocation:
# In this version, only accept information concerning GRCh37
if 'GRCh37' in SequenceLocation.Assembly:
chrom = SequenceLocation.Chr
chromStart = SequenceLocation.start
chromEnd = SequenceLocation.stop
ref = SequenceLocation.referenceAllele
alt = SequenceLocation.alternateAllele
if Measure.MeasureRelationship:
try:
symbol = Measure.MeasureRelationship[0].\
Symbol[0].get_ElementValue().valueOf_
except:
symbol = None
gene_id = Measure.MeasureRelationship[0].XRef[0].ID
else:
symbol = None
gene_id = None
if Measure.Name:
name = Measure.Name[0].ElementValue.valueOf_
else:
name = None
if len(Measure.CytogeneticLocation) == 1:
cytogenic = Measure.CytogeneticLocation[0]
else:
cytogenic = Measure.CytogeneticLocation
hgvs_coding = None
hgvs_genome = None
HGVS = {'genomic': [], 'coding': [], 'non-coding': [], 'protein': []}
coding_hgvs_only = None
hgvs_id = None
# hgvs_not_validated = None
if Measure.AttributeSet:
# 'copy number loss' or 'gain' have format different\
# from other types, should be dealt with seperately
if (variation_type == 'copy number loss') or \
(variation_type == 'copy number gain'):
for AttributeSet in Measure.AttributeSet:
if 'HGVS, genomic, top level' in AttributeSet.\
Attribute.Type:
if AttributeSet.Attribute.integerValue == 37:
hgvs_genome = AttributeSet.Attribute.get_valueOf_()
if 'genomic' in AttributeSet.Attribute.Type:
HGVS['genomic'].append(
AttributeSet.Attribute.get_valueOf_())
elif 'non-coding' in AttributeSet.Attribute.Type:
HGVS['non-coding'].append(
AttributeSet.Attribute.get_valueOf_())
elif 'coding' in AttributeSet.Attribute.Type:
HGVS['coding'].append(
AttributeSet.Attribute.get_valueOf_())
elif 'protein' in AttributeSet.Attribute.Type:
HGVS['protein'].append(
AttributeSet.Attribute.get_valueOf_())
else:
for AttributeSet in Measure.AttributeSet:
if 'genomic' in AttributeSet.Attribute.Type:
HGVS['genomic'].append(
AttributeSet.Attribute.get_valueOf_())
elif 'non-coding' in AttributeSet.Attribute.Type:
HGVS['non-coding'].append(
AttributeSet.Attribute.get_valueOf_())
elif 'coding' in AttributeSet.Attribute.Type:
HGVS['coding'].append(
AttributeSet.Attribute.get_valueOf_())
elif 'protein' in AttributeSet.Attribute.Type:
HGVS['protein'].append(
AttributeSet.Attribute.get_valueOf_())
if AttributeSet.Attribute.Type == 'HGVS, coding, RefSeq':
hgvs_coding = AttributeSet.Attribute.get_valueOf_()
elif AttributeSet.Attribute.Type == \
'HGVS, genomic, top level, previous':
hgvs_genome = AttributeSet.Attribute.get_valueOf_()
break
if chrom and chromStart and chromEnd:
if variation_type == 'single nucleotide variant':
hgvs_id = "chr%s:g.%s%s>%s" % (chrom, chromStart, ref, alt)
# items whose type belong to 'Indel, Insertion, \
# Duplication' might not hava explicit alt information, \
# so we will parse from hgvs_genome
elif variation_type == 'Indel':
if hgvs_genome:
indel_position = hgvs_genome.find('del')
indel_alt = hgvs_genome[indel_position + 3:]
hgvs_id = "chr%s:g.%s_%sdel%s" % \
(chrom, chromStart, chromEnd, indel_alt)
elif variation_type == 'Deletion':
hgvs_id = "chr%s:g.%s_%sdel" % \
(chrom, chromStart, chromEnd)
elif variation_type == 'Insertion':
if hgvs_genome:
ins_position = hgvs_genome.find('ins')
if 'ins' in hgvs_genome:
ins_ref = hgvs_genome[ins_position + 3:]
hgvs_id = "chr%s:g.%s_%sins%s" % \
(chrom, chromStart, chromEnd, ins_ref)
elif variation_type == 'Duplication':
if hgvs_genome:
dup_position = hgvs_genome.find('dup')
if 'dup' in hgvs_genome:
dup_ref = hgvs_genome[dup_position + 3:]
hgvs_id = "chr%s:g.%s_%sdup%s" % \
(chrom, chromStart, chromEnd, dup_ref)
elif variation_type == 'copy number loss' or\
variation_type == 'copy number gain':
if hgvs_genome:
hgvs_id = "chr" + hgvs_genome.split('.')[1] +\
hgvs_genome.split('.')[2]
elif hgvs_coding:
hgvs_id = hgvs_coding
coding_hgvs_only = True
else:
print "couldn't find any id", rcv_accession
return
else:
print 'no measure.attribute', rcv_accession
return
other_ids = ''
rsid = None
# loop through XRef to find rsid as well as other ids
if Measure.XRef:
for XRef in Measure.XRef:
if XRef.Type == 'rs':
rsid = 'rs' + str(XRef.ID)
other_ids = other_ids + XRef.DB + ':' + XRef.ID + ';'
# make sure the hgvs_id is not none
if hgvs_id:
one_snp_json = {
"_id": hgvs_id,
"clinvar": {
"allele_id": allele_id,
"chrom": chrom,
"hg19": {
"start": chromStart,
"end": chromEnd
},
"type": variation_type,
"name": name,
"gene": {
"id": gene_id,
"symbol": symbol
},
"clinical_significance": clinical_siginificance,
"rsid": rsid,
"rcv_accession": rcv_accession,
"origin": origin,
"cytogenic": cytogenic,
"review_status": review_status,
"hgvs": HGVS,
"number_submitters": number_submitters,
"last_evaluated": str(last_evaluated),
"other_ids": other_ids,
"clinvar_id": CLINVAR_ID,
"coding_hgvs_only": coding_hgvs_only,
"ref": ref,
"alt": alt
}
}
obj = (dict_sweep(unlist(value_convert(one_snp_json)), [None]))
yield obj
def _map_line_to_json(item):
chrom = item.CHROM
chromStart = item.POS
ref = item.REF
info = item.INFO
try:
baseqranksum = info['BaseQRankSum']
except:
baseqranksum = None
try:
clippingranksum = info['ClippingRankSum']
except:
clippingranksum = None
try:
mqranksum = info['MQRankSum']
except:
mqranksum = None
try:
readposranksum = info['ReadPosRankSum']
except:
readposranksum = None
try:
qd = info['QD']
except:
qd = None
try:
inbreedingcoeff = info['InbreedingCoeff']
except:
inbreedingcoeff = None
for i in range(0, len(item.ALT)):
item.ALT[i] = str(item.ALT[i])
for alt in item.ALT:
alt = str(alt)
(HGVS, var_type) = get_hgvs_from_vcf(chrom, chromStart, ref, alt, mutant_type=True)
if HGVS is None:
return
one_snp_json = {
"_id": HGVS,
"exac": {
"chrom": chrom,
"pos": chromStart,
"ref": ref,
"alt": alt,
"alleles": item.ALT,
"type": var_type,
"ac": {
"ac": info['AC'],
"ac_afr": info['AC_AFR'],
"ac_amr": info['AC_AMR'],
"ac_adj": info['AC_Adj'],
"ac_eas": info['AC_EAS'],
"ac_fin": info['AC_FIN'],
"ac_het": info['AC_Het'],
"ac_hom": info['AC_Hom'],
"ac_nfe": info['AC_NFE'],
"ac_oth": info['AC_OTH'],
"ac_sas": info['AC_SAS']
},
"af": info['AF'],
"an": {
"an": info['AN'],
"an_afr": info['AN_AFR'],
"an_amr": info['AN_AMR'],
"an_adj": info['AN_Adj'],
"an_eas": info['AN_EAS'],
"an_fin": info['AN_FIN'],
"an_nfe": info['AN_NFE'],
"an_oth": info['AN_OTH'],
"an_sas": info['AN_SAS']
},
"baseqranksum": baseqranksum,
"clippingranksum": clippingranksum,
"fs": info['FS'],
"het": {
"het_afr": info['Het_AFR'],
"het_amr": info['Het_AMR'],
"het_eas": info['Het_EAS'],
"het_fin": info['Het_FIN'],
"het_nfe": info['Het_NFE'],
"het_oth": info['Het_OTH'],
"het_sas": info['Het_SAS']
},
"hom": {
"hom_afr": info['Hom_AFR'],
"hom_amr": info['Hom_AMR'],
"hom_eas": info['Hom_EAS'],
"hom_fin": info['Hom_FIN'],
"hom_nfe": info['Hom_NFE'],
"hom_oth": info['Hom_OTH'],
"hom_sas": info['Hom_SAS']
},
"inbreedingcoeff": inbreedingcoeff,
"mq": {
"mq": info['MQ'],
"mq0": info['MQ0'],
"mqranksum": mqranksum
},
"ncc": info['NCC'],
"qd": qd,
"readposranksum": readposranksum,
"vqslod": info['VQSLOD'],
"culprit": info['culprit']
}
}
obj = (dict_sweep(unlist(value_convert(one_snp_json)), [None]))
yield obj
def _map_line_to_json(item):
chrom = item.CHROM
chromStart = item.POS
ref = item.REF
info = item.INFO
try:
baseqranksum = info['BaseQRankSum']
except:
baseqranksum = None
try:
clippingranksum = info['ClippingRankSum']
except:
clippingranksum = None
try:
mqranksum = info['MQRankSum']
except:
mqranksum = None
try:
readposranksum = info['ReadPosRankSum']
except:
readposranksum = None
try:
qd = info['QD']
except:
qd = None
try:
inbreedingcoeff = info['InbreedingCoeff']
except:
inbreedingcoeff = None
for i in range(0, len(item.ALT)):
item.ALT[i] = str(item.ALT[i])
for alt in item.ALT:
alt = str(alt)
(HGVS, var_type) = get_hgvs_from_vcf(chrom,
chromStart,
ref,
alt,
mutant_type=True)
if HGVS is None:
return
one_snp_json = {
"_id": HGVS,
"exac": {
"chrom": chrom,
"pos": chromStart,
"ref": ref,
"alt": alt,
"alleles": item.ALT,
"type": var_type,
"ac": {
"ac": info['AC'],
"ac_afr": info['AC_AFR'],
"ac_amr": info['AC_AMR'],
"ac_adj": info['AC_Adj'],
"ac_eas": info['AC_EAS'],
"ac_fin": info['AC_FIN'],
"ac_het": info['AC_Het'],
"ac_hom": info['AC_Hom'],
"ac_nfe": info['AC_NFE'],
"ac_oth": info['AC_OTH'],
"ac_sas": info['AC_SAS'],
"ac_female": info['AC_FEMALE'],
"ac_male": info['AC_MALE']
},
"af": info['AF'],
"an": {
"an": info['AN'],
"an_afr": info['AN_AFR'],
"an_amr": info['AN_AMR'],
"an_adj": info['AN_Adj'],
"an_eas": info['AN_EAS'],
"an_fin": info['AN_FIN'],
"an_nfe": info['AN_NFE'],
"an_oth": info['AN_OTH'],
"an_sas": info['AN_SAS'],
"an_female": info['AN_FEMALE'],
"an_male": info['AN_MALE']
},
"baseqranksum": baseqranksum,
"clippingranksum": clippingranksum,
"fs": info['FS'],
"het": {
"het_afr": info['Het_AFR'],
"het_amr": info['Het_AMR'],
"het_eas": info['Het_EAS'],
"het_fin": info['Het_FIN'],
"het_nfe": info['Het_NFE'],
"het_oth": info['Het_OTH'],
"het_sas": info['Het_SAS']
},
"hom": {
"hom_afr": info['Hom_AFR'],
"hom_amr": info['Hom_AMR'],
"hom_eas": info['Hom_EAS'],
"hom_fin": info['Hom_FIN'],
"hom_nfe": info['Hom_NFE'],
"hom_oth": info['Hom_OTH'],
"hom_sas": info['Hom_SAS']
},
"inbreedingcoeff": inbreedingcoeff,
"mq": {
"mq": info['MQ'],
"mq0": info['MQ0'],
"mqranksum": mqranksum
},
"ncc": info['NCC'],
"qd": qd,
"readposranksum": readposranksum,
"vqslod": info['VQSLOD'],
"culprit": info['culprit']
}
}
obj = (dict_sweep(unlist(value_convert(one_snp_json)), [None]))
yield obj
def _map_line_to_json(fields, version):
chrInfo = fields[0].split(":") # grch37
chrom = chrInfo[0]
chromStart = int(chrInfo[1])
ma_fin_percent = fields[7].split("/")
if fields[3]:
mutation = fields[3].split(">")
ref = mutation[0]
alt = mutation[1]
hg19 = get_pos_start_end(chrom, chromStart, ref, alt)
hg38 = get_pos_start_end(chrom, int(fields[30].split(":")[1]), ref,
alt)
if version == 'hg19':
HGVS = get_hgvs_from_vcf(chrom, chromStart, ref, alt)
elif version == 'hg38':
HGVS = get_hgvs_from_vcf(chrom, hg38[0], ref, alt)
# load as json data
if HGVS is None:
return
one_snp_json = {
"_id": HGVS,
"evs": {
"chrom": chrom,
"hg19": {
"start": hg19[0],
"end": hg19[1]
},
"hg38": {
"start": hg38[0],
"end": hg38[1]
},
"rsid": fields[1],
"dbsnp_version": get_dbsnp(fields[2]),
"ref": ref,
"alt": alt,
"allele_count": {
"european_american": count_dict(fields[4]),
"african_american": count_dict(fields[5]),
"all": count_dict(fields[6])
},
"ma_fin_percent": {
"european_american": ma_fin_percent[0],
"african_american": ma_fin_percent[1],
"all": ma_fin_percent[2]
},
"genotype_count": {
"european_american": count_dict(fields[8]),
"african_american": count_dict(fields[9]),
"all_genotype": count_dict(fields[10])
},
"avg_sample_read": fields[11],
"gene": {
"symbol": fields[12],
"accession": fields[13]
},
"function_gvs": fields[14],
"hgvs": {
"coding": fields[16],
"protein": fields[15]
},
"coding_dna_size": fields[17],
"conservation": {
"phast_cons": fields[18],
"gerp": fields[19]
},
"grantham_score": fields[20],
"polyphen2": {
"class": polyphen(fields[21])[0],
"score": polyphen(fields[21])[1]
},
"ref_base_ncbi": fields[22],
"chimp_allele": fields[23],
"clinical_info": fields[24],
"filter_status": fields[25],
"on_illumina_human_exome_chip": fields[26],
"gwas_pubmed_info": fields[27],
"estimated_age_kyrs": {
"ea": fields[28],
"aa": fields[29]
}
}
}
return dict_sweep(value_convert(one_snp_json),
vals=["NA", "none", "unknown"])
def _map_line_to_json(fields):
assert len(fields) == VALID_COLUMN_NO
rsid = fields[8]
# load as json data
if rsid is None:
return
url = 'http://myvariant.info/v1/query?q=dbsnp.rsid:'\
+ rsid + '&fields=_id'
r = requests.get(url)
for hits in r.json()['hits']:
HGVS = hits['_id']
one_snp_json = {
"_id": HGVS,
"grasp":
{
'hg19':
{
'chr': fields[5],
'pos': fields[6]
},
'hupfield': fields[1],
'last_curation_date': fields[2],
'creation_date': fields[3],
'srsid': fields[4],
'publication':
{
'journal': fields[16],
'title': fields[17],
'pmid': fields[7],
'snpid': fields[8],
'location_within_paper': fields[9],
'p_value': fields[10],
'phenotype': fields[11],
'paper_phenotype_description': fields[12],
'paper_phenotype_categories': fields[13],
'date_pub': fields[14]
},
'includes_male_female_only_analyses': fields[18],
'exclusively_male_female': fields[19],
'initial_sample_description': fields[20],
'replication_sample_description': fields[21],
'platform_snps_passing_qc': fields[22],
'gwas_ancestry_description': fields[23],
'discovery':
{
'total_samples': fields[25],
'european': fields[26],
'african': fields[27],
'east_asian': fields[28],
'indian_south_asian': fields[29],
'hispanic': fields[30],
'native': fields[31],
'micronesian': fields[32],
'arab_me': fields[33],
'mixed': fields[34],
'unspecified': fields[35],
'filipino': fields[36],
'indonesian': fields[37]
},
'replication':
{
'total_samples': fields[38],
'european': fields[39],
'african': fields[40],
'east_asian': fields[41],
'indian_south_asian': fields[42],
'hispanic': fields[43],
'native': fields[44],
'micronesian': fields[45],
'arab_me': fields[46],
'mixed': fields[47],
'unspecified': fields[48],
'filipino': fields[49],
'indonesian': fields[50]
},
'in_gene': fields[51],
'nearest_gene': fields[52],
'in_lincrna': fields[53],
'in_mirna': fields[54],
'in_mirna_bs': fields[55],
'oreg_anno': fields[61],
'conserv_pred_tfbs': fields[62],
'human_enhancer': fields[63],
'rna_edit': fields[64],
'polyphen2': fields[65],
'sift': fields[66],
'ls_snp': fields[67],
'uniprot': fields[68],
'eqtl_meth_metab_study': fields[69]
}
}
return list_split(dict_sweep(unlist(value_convert(one_snp_json)), [""]), ",")
def _map_line_to_json(cp, hg19):
try:
clinical_significance = cp.ReferenceClinVarAssertion.\
ClinicalSignificance.Description
except:
clinical_significance = None
rcv_accession = cp.ReferenceClinVarAssertion.ClinVarAccession.Acc
try:
review_status = cp.ReferenceClinVarAssertion.ClinicalSignificance.\
ReviewStatus
except:
review_status = None
try:
last_evaluated = cp.ReferenceClinVarAssertion.ClinicalSignificance.\
DateLastEvaluated
except:
last_evaluated = None
variant_id = cp.ReferenceClinVarAssertion.MeasureSet.ID
number_submitters = len(cp.ClinVarAssertion)
# some items in clinvar_xml doesn't have origin information
try:
origin = cp.ReferenceClinVarAssertion.ObservedIn[0].Sample.Origin
except:
origin = None
trait = cp.ReferenceClinVarAssertion.TraitSet.Trait[0]
synonyms = []
conditions_name = ''
for name in trait.Name:
if name.ElementValue.Type == 'Alternate':
synonyms.append(name.ElementValue.get_valueOf_())
if name.ElementValue.Type == 'Preferred':
conditions_name += name.ElementValue.get_valueOf_()
identifiers = {}
for item in trait.XRef:
if item.DB == 'Human Phenotype Ontology':
key = 'Human_Phenotype_Ontology'
else:
key = item.DB
identifiers[key.lower()] = item.ID
for symbol in trait.Symbol:
if symbol.ElementValue.Type == 'Preferred':
conditions_name += ' (' + symbol.ElementValue.get_valueOf_() + ')'
age_of_onset = ''
for _set in trait.AttributeSet:
if _set.Attribute.Type == 'age of onset':
age_of_onset = _set.Attribute.get_valueOf_()
# MeasureSet.Measure return a list, there might be multiple
# Measure under one MeasureSet
for Measure in cp.ReferenceClinVarAssertion.MeasureSet.Measure:
variation_type = Measure.Type
# exclude any item of which types belong to
# 'Variation', 'protein only' or 'Microsatellite'
if variation_type == 'Variation' or variation_type\
== 'protein only' or variation_type == 'Microsatellite':
continue
allele_id = Measure.ID
chrom = None
chromStart_19 = None
chromEnd_19 = None
chromStart_38 = None
chromEnd_38 = None
ref = None
alt = None
if Measure.SequenceLocation:
for SequenceLocation in Measure.SequenceLocation:
# In this version, only accept information concerning GRCh37
if 'GRCh37' in SequenceLocation.Assembly:
chrom = SequenceLocation.Chr
chromStart_19 = SequenceLocation.start
chromEnd_19 = SequenceLocation.stop
ref = SequenceLocation.referenceAllele
alt = SequenceLocation.alternateAllele
if 'GRCh38' in SequenceLocation.Assembly:
chromStart_38 = SequenceLocation.start
chromEnd_38 = SequenceLocation.stop
if not ref:
ref = SequenceLocation.referenceAllele
if not alt:
alt = SequenceLocation.alternateAllele
if Measure.MeasureRelationship:
try:
symbol = Measure.MeasureRelationship[0].\
Symbol[0].get_ElementValue().valueOf_
except:
symbol = None
gene_id = Measure.MeasureRelationship[0].XRef[0].ID
else:
symbol = None
gene_id = None
if Measure.Name:
name = Measure.Name[0].ElementValue.valueOf_
else:
name = None
if len(Measure.CytogeneticLocation) == 1:
cytogenic = Measure.CytogeneticLocation[0]
else:
cytogenic = Measure.CytogeneticLocation
hgvs_coding = None
hgvs_genome = None
HGVS = {'genomic': [], 'coding': [], 'non-coding': [], 'protein': []}
coding_hgvs_only = None
hgvs_id = None
if hg19:
chromStart = chromStart_19
chromEnd = chromEnd_19
else:
chromStart = chromStart_38
chromEnd = chromEnd_38
# hgvs_not_validated = None
if Measure.AttributeSet:
# 'copy number loss' or 'gain' have format different\
# from other types, should be dealt with seperately
if (variation_type == 'copy number loss') or \
(variation_type == 'copy number gain'):
for AttributeSet in Measure.AttributeSet:
if 'HGVS, genomic, top level' in AttributeSet.\
Attribute.Type:
if AttributeSet.Attribute.integerValue == 37:
hgvs_genome = AttributeSet.Attribute.get_valueOf_()
if 'genomic' in AttributeSet.Attribute.Type:
HGVS['genomic'].append(AttributeSet.Attribute.
get_valueOf_())
elif 'non-coding' in AttributeSet.Attribute.Type:
HGVS['non-coding'].append(AttributeSet.Attribute.
get_valueOf_())
elif 'coding' in AttributeSet.Attribute.Type:
HGVS['coding'].append(AttributeSet.Attribute.
get_valueOf_())
elif 'protein' in AttributeSet.Attribute.Type:
HGVS['protein'].append(AttributeSet.
Attribute.get_valueOf_())
else:
for AttributeSet in Measure.AttributeSet:
if 'genomic' in AttributeSet.Attribute.Type:
HGVS['genomic'].append(AttributeSet.
Attribute.get_valueOf_())
elif 'non-coding' in AttributeSet.Attribute.Type:
HGVS['non-coding'].append(AttributeSet.
Attribute.get_valueOf_())
elif 'coding' in AttributeSet.Attribute.Type:
HGVS['coding'].append(AttributeSet.Attribute.
get_valueOf_())
elif 'protein' in AttributeSet.Attribute.Type:
HGVS['protein'].append(AttributeSet.
Attribute.get_valueOf_())
if AttributeSet.Attribute.Type == 'HGVS, coding, RefSeq':
hgvs_coding = AttributeSet.Attribute.get_valueOf_()
elif AttributeSet.Attribute.Type == \
'HGVS, genomic, top level, previous':
hgvs_genome = AttributeSet.Attribute.get_valueOf_()
break
if chrom and chromStart and chromEnd:
if variation_type == 'single nucleotide variant':
hgvs_id = "chr%s:g.%s%s>%s" % (chrom, chromStart, ref, alt)
# items whose type belong to 'Indel, Insertion, \
# Duplication' might not hava explicit alt information, \
# so we will parse from hgvs_genome
elif variation_type == 'Indel':
if hgvs_genome:
indel_position = hgvs_genome.find('del')
indel_alt = hgvs_genome[indel_position+3:]
hgvs_id = "chr%s:g.%s_%sdel%s" % \
(chrom, chromStart, chromEnd, indel_alt)
elif variation_type == 'Deletion':
hgvs_id = "chr%s:g.%s_%sdel" % \
(chrom, chromStart, chromEnd)
elif variation_type == 'Insertion':
if hgvs_genome:
ins_position = hgvs_genome.find('ins')
if 'ins' in hgvs_genome:
ins_ref = hgvs_genome[ins_position+3:]
hgvs_id = "chr%s:g.%s_%sins%s" % \
(chrom, chromStart, chromEnd, ins_ref)
elif variation_type == 'Duplication':
if hgvs_genome:
dup_position = hgvs_genome.find('dup')
if 'dup' in hgvs_genome:
dup_ref = hgvs_genome[dup_position+3:]
hgvs_id = "chr%s:g.%s_%sdup%s" % \
(chrom, chromStart, chromEnd, dup_ref)
elif variation_type == 'copy number loss' or\
variation_type == 'copy number gain':
if hgvs_genome and chrom:
hgvs_id = "chr" + chrom + ":" + hgvs_genome.split('.')[2]
elif hgvs_coding:
hgvs_id = hgvs_coding
coding_hgvs_only = True
else:
print "couldn't find any id", rcv_accession
return
else:
print 'no measure.attribute', rcv_accession
return
for key in HGVS:
HGVS[key].sort()
rsid = None
cosmic = None
dbvar = None
uniprot = None
omim = None
# loop through XRef to find rsid as well as other ids
if Measure.XRef:
for XRef in Measure.XRef:
if XRef.Type == 'rs':
rsid = 'rs' + str(XRef.ID)
elif XRef.DB == 'COSMIC':
cosmic = XRef.ID
elif XRef.DB == 'OMIM':
omim = XRef.ID
elif XRef.DB == 'UniProtKB/Swiss-Prot':
uniprot = XRef.ID
elif XRef.DB == 'dbVar':
dbvar = XRef.ID
# make sure the hgvs_id is not none
if hgvs_id:
one_snp_json = {
"_id": hgvs_id,
"clinvar":
{
"allele_id": allele_id,
"variant_id": variant_id,
"chrom": chrom,
"omim": omim,
"cosmic": cosmic,
"uniprot": uniprot,
"dbvar": dbvar,
"hg19":
{
"start": chromStart_19,
"end": chromEnd_19
},
"hg38":
{
"start": chromStart_38,
"end": chromEnd_38
},
"type": variation_type,
"gene":
{
"id": gene_id,
"symbol": symbol
},
"rcv":
{
"accession": rcv_accession,
"clinical_significance": clinical_significance,
"number_submitters": number_submitters,
"review_status": review_status,
"last_evaluated": str(last_evaluated),
"preferred_name": name,
"origin": origin,
"conditions":
{
"name": conditions_name,
"synonyms": synonyms,
"identifiers": identifiers,
"age_of_onset": age_of_onset
}
},
"rsid": rsid,
"cytogenic": cytogenic,
"hgvs": HGVS,
"coding_hgvs_only": coding_hgvs_only,
"ref": ref,
"alt": alt
}
}
obj = (dict_sweep(unlist(value_convert(one_snp_json,
['chrom', 'omim', 'id', 'orphanet', 'gene',
'rettbase_(cdkl5)', 'cosmic', 'dbrbc'])), [None, '', 'None']))
yield obj
def _map_line_to_json(fields, version='hg19'):
# specific variable treatment
chrom = fields[0]
if chrom == 'M':
chrom = 'MT'
# fields[7] in version 2, represent hg18_pos
if fields[10] == ".":
hg18_end = "."
else:
hg18_end = int(fields[10])
chromStart = int(fields[8])
chromEnd = int(fields[8])
chromStart_38 = int(fields[1])
ref = fields[2].upper()
alt = fields[3].upper()
HGVS_19 = "chr%s:g.%d%s>%s" % (chrom, chromStart, ref, alt)
HGVS_38 = "chr%s:g.%d%s>%s" % (chrom, chromStart_38, ref, alt)
if version == 'hg19':
HGVS = HGVS_19
elif version == 'hg38':
HGVS = HGVS_38
if fields[69] == ".":
siphy = "."
else:
freq = fields[69].split(":")
siphy = {'a': freq[0], 'c': freq[1], 'g': freq[2], 't': freq[3]}
acc = fields[26].rstrip().rstrip(';').split(";")
pos = fields[28].rstrip().rstrip(';').split(";")
uniprot = map(dict, map(lambda t: zip(('acc', 'pos'), t), zip(acc, pos)))
# load as json data
one_snp_json = {
"_id": HGVS,
"dbnsfp": {
"rsid": fields[6],
"chrom": chrom,
"hg19": {
"start": fields[8],
"end": chromEnd
},
"hg18": {
"start": fields[10],
"end": hg18_end
},
"hg38": {
"start": fields[1],
"end": fields[1]
},
"ref": ref,
"alt": alt,
"aa": {
"ref": fields[4],
"alt": fields[5],
"pos": fields[22],
"refcodon": fields[13],
"codonpos": fields[14],
},
"genename": fields[11],
"uniprot": uniprot,
"interpro_domain": fields[111],
"cds_strand": fields[12],
"ancestral_allele": fields[16],
"ensembl": {
"geneid": fields[19],
"transcriptid": fields[20]
},
"sift": {
"score": fields[23],
"converted_rankscore": fields[24],
"pred": fields[25]
},
"polyphen2": {
"hdiv": {
"score": fields[29],
"rankscore": fields[30],
"pred": fields[31]
},
"hvar": {
"score": fields[32],
"rankscore": fields[33],
"pred": fields[34]
}
},
"lrt": {
"score": fields[35],
"converted_rankscore": fields[36],
"pred": fields[37],
"omega": fields[38]
},
"mutationtaster": {
"score": fields[39],
"converted_rankscore": fields[40],
"pred": fields[41],
"model": fields[42],
"AAE": fields[43]
},
"mutationassessor": {
"score": fields[46],
"rankscore": fields[47],
"pred": fields[48]
},
"fathmm": {
"score": fields[49],
"rankscore": fields[50],
"pred": fields[51]
},
"provean": {
"score": fields[52],
"rankscore": fields[53],
"pred": fields[54]
},
"metasvm": {
"score": fields[55],
"rankscore": fields[56],
"pred": fields[57]
},
"lr": {
"score": fields[58],
"rankscore": fields[59],
"pred": fields[60]
},
"reliability_index": fields[61],
"gerp++": {
"nr": fields[62],
"rs": fields[63],
"rs_rankscore": fields[64]
},
"phylop_7way": {
"vertebrate": fields[65],
"vertebrate_rankscore": fields[66]
},
"phastcons_7way": {
"vertebrate": fields[67],
"vertebrate_rankscore": fields[68]
},
"siphy_29way": {
"pi": siphy,
"logodds": fields[70],
"logodds_rankscore": fields[71]
},
"1000gp1": {
"ac": fields[72],
"af": fields[73],
"afr_ac": fields[74],
"afr_af": fields[75],
"eur_ac": fields[76],
"eur_af": fields[77],
"amr_ac": fields[78],
"amr_af": fields[79],
"eas_ac": fields[80],
"eas_af": fields[81],
"sas_ac": fields[82],
"sas_af": fields[83]
},
"twinsuk": {
"ac": fields[84],
"af": fields[85]
},
"alspac": {
"ac": fields[86],
"af": fields[87]
},
"esp6500": {
"aa_ac": fields[88],
"aa_af": fields[89],
"ea_ac": fields[90],
"ea_af": fields[91]
},
"exac": {
"ac": fields[92],
"af": fields[93],
"adj_ac": fields[94],
"adj_af": fields[95],
"afr_ac": fields[96],
"afr_af": fields[97],
"amr_ac": fields[98],
"amr_af": fields[99],
"eas_ac": fields[100],
"eas_af": fields[101],
"fin_ac": fields[102],
"fin_af": fields[103],
"nfe_ac": fields[104],
"nfe_af": fields[105],
"sas_ac": fields[106],
"sas_af": fields[107]
},
"clinvar": {
"rs": fields[108],
"clinsig": fields[109],
"trait": fields[110]
}
}
}
one_snp_json = list_split(dict_sweep(unlist(value_convert(one_snp_json)), vals=["."]), ";")
one_snp_json["dbnsfp"]["chrom"] = str(one_snp_json["dbnsfp"]["chrom"])
return one_snp_json
def _map_line_to_json(fields):
assert len(fields) == VALID_COLUMN_NO
chrom = fields[0]
chromStart = fields[1]
ref = fields[2]
alt = fields[4]
HGVS = get_hgvs_from_vcf(chrom, chromStart, ref, alt)
# load as json data
if HGVS is None:
return
one_snp_json = {
"_id": HGVS,
"cadd": {
'chrom': fields[0],
'pos': fields[1],
'ref': fields[2],
'anc': fields[3],
'alt': fields[4],
'type': fields[5],
'length': fields[6],
'istv': fields[7],
'isderived': fields[8],
'annotype': fields[9],
'consequence': fields[10],
'consscore': fields[11],
'consdetail': fields[12],
'gc': fields[13],
'cpg': fields[14],
'mapability': {
'20bp': fields[15],
'35bp': fields[16]
},
'scoresegdup': fields[17],
'phast_cons': {
'primate': fields[18],
'mammalian': fields[19],
'vertebrate': fields[20]
},
'phylop': {
'primate': fields[21],
'mammalian': fields[22],
'vertebrate': fields[23]
},
'gerp': {
'n': fields[24],
's': fields[25],
'rs': fields[26],
'rs_pval': fields[27]
},
'bstatistic': fields[28],
'mutindex': fields[29],
'dna': {
'helt': fields[30],
'mgw': fields[31],
'prot': fields[32],
'roll': fields[33]
},
'mirsvr': {
'score': fields[34],
'e': fields[35],
'aln': fields[36]
},
'targetscans': fields[37],
'fitcons': fields[38],
'chmm': {
'tssa': fields[39],
'tssaflnk': fields[40],
'txflnk': fields[41],
'tx': fields[42],
'txwk': fields[43],
'enh': fields[44],
# 'enh': fields[45],
'znfrpts': fields[46],
'het': fields[47],
'tssbiv': fields[48],
'bivflnk': fields[49],
'enhbiv': fields[50],
'reprpc': fields[51],
'reprpcwk': fields[52],
'quies': fields[53],
},
'encode': {
'exp': fields[54],
'h3k27ac': fields[55],
'h3k4me1': fields[56],
'h3k4me3': fields[57],
'nucleo': fields[58],
'occ': fields[59],
'p_val': {
'comb': fields[60],
'dnas': fields[61],
'faire': fields[62],
'polii': fields[63],
'ctcf': fields[64],
'mycp': fields[65]
},
'sig': {
'dnase': fields[66],
'faire': fields[67],
'polii': fields[68],
'ctcf': fields[69],
'myc': fields[70]
},
},
'segway': fields[71],
'motif': {
'toverlap': fields[72],
'dist': fields[73],
'ecount': fields[74],
'ename': fields[75],
'ehipos': fields[76],
'escorechng': fields[77]
},
'tf': {
'bs': fields[78],
'bs_peaks': fields[79],
'bs_peaks_max': fields[80]
},
'isknownvariant': fields[81],
'esp': {
'af': fields[82],
'afr': fields[83],
'eur': fields[84]
},
'1000g': {
'af': fields[85],
'asn': fields[86],
'amr': fields[87],
'afr': fields[88],
'eur': fields[89]
},
'min_dist_tss': fields[90],
'min_dist_tse': fields[91],
'gene': {
'gene_id': fields[92],
'feature_id': fields[93],
'ccds_id': fields[94],
'genename': fields[95],
'cds': {
'cdna_pos': fields[96],
'rel_cdna_pos': fields[97],
'cds_pos': fields[98],
'rel_cds_pos': fields[99]
},
'prot': {
'protpos': fields[100],
'rel_prot_pos': fields[101],
'domain': fields[102]
}
},
'dst2splice': fields[103],
'dst2spltype': fields[104],
'exon': fields[105],
'intron': fields[106],
'oaa': fields[107], # ref aa
'naa': fields[108], # alt aa
'grantham': fields[109],
'polyphen': {
'cat': fields[110],
'val': fields[111]
},
'sift': {
'cat': fields[112],
'val': fields[113]
},
'rawscore': fields[114], # raw CADD score
'phred': fields[115] # log-percentile of raw CADD score
}
}
return dict_sweep(unlist(value_convert(one_snp_json)), ["NA"])
def _map_line_to_json(fields, version):
# specific variable treatment
chrom = fields[0]
if chrom == 'M':
chrom = 'MT'
# fields[7] in version 2, represent hg18_pos
if fields[10] == ".":
hg18_end = "."
else:
hg18_end = int(fields[10])
# in case of no hg19 position provided, remove the item
if fields[8] == '.':
return None
else:
chromStart = int(fields[8])
chromEnd = int(fields[8])
chromStart_38 = int(fields[1])
ref = fields[2].upper()
alt = fields[3].upper()
HGVS_19 = "chr%s:g.%d%s>%s" % (chrom, chromStart, ref, alt)
HGVS_38 = "chr%s:g.%d%s>%s" % (chrom, chromStart_38, ref, alt)
if version == 'hg19':
HGVS = HGVS_19
elif version == 'hg38':
HGVS = HGVS_38
if fields[105] == ".":
siphy = "."
else:
freq = fields[105].split(":")
siphy = {'a': freq[0], 'c': freq[1], 'g': freq[2], 't': freq[3]}
gtex_gene = fields[181].split('|')
gtex_tissue = fields[182].split('|')
gtex = map(dict, map(lambda t: zip(('gene', 'tissue'), t), zip(gtex_gene, gtex_tissue)))
acc = fields[26].rstrip().rstrip(';').split(";")
pos = fields[28].rstrip().rstrip(';').split(";")
uniprot = map(dict, map(lambda t: zip(('acc', 'pos'), t), zip(acc, pos)))
provean_score = fields[52].split(';')
sift_score = fields[23].split(';')
hdiv_score = fields[29].split(';')
hvar_score = fields[32].split(';')
lrt_score = fields[35].split(';')
dann_score = fields[69].split(';')
mutationtaster_score = fields[39].split(';')
mutationassessor_score = fields[46].split(';')
vest3_score = fields[57].split(';')
metasvm_score = fields[59].split(';')
fathmm_score = fields[49].split(';')
lr_score = fields[62].split(';')
fathmm_coding_score = fields[71].split(';')
integrated_fitcons_score = fields[82].split(';')
gm12878_fitcons_score = fields[85].split(';')
h1_hesc_fitcons_score = fields[88].split(';')
huvec_fitcons_score = fields[91].split(';')
if len(provean_score) > 1:
for i in range(len(provean_score)):
if provean_score[i] == '.':
provean_score[i] = None
if len(sift_score) > 1:
for i in range(len(sift_score)):
if sift_score[i] == '.':
sift_score[i] = None
if len(hdiv_score) > 1:
for i in range(len(hdiv_score)):
if hdiv_score[i] == '.':
hdiv_score[i] = None
if len(hvar_score) > 1:
for i in range(len(hvar_score)):
if hvar_score[i] == '.':
hvar_score[i] = None
if len(lrt_score) > 1:
for i in range(len(lrt_score)):
if lrt_score[i] == '.':
lrt_score[i] = None
if len(mutationtaster_score) > 1:
for i in range(len(mutationtaster_score)):
if mutationtaster_score[i] == '.':
mutationtaster_score[i] = None
if len(mutationassessor_score) > 1:
for i in range(len(mutationassessor_score)):
if mutationassessor_score[i] == '.':
mutationassessor_score[i] = None
if len(metasvm_score) > 1:
for i in range(len(metasvm_score)):
if metasvm_score[i] == '.':
metasvm_score[i] = None
if len(vest3_score) > 1:
for i in range(len(vest3_score)):
if vest3_score[i] == '.':
vest3_score[i] = None
if len(fathmm_score) > 1:
for i in range(len(fathmm_score)):
if fathmm_score[i] == '.':
fathmm_score[i] = None
if len(lr_score) > 1:
for i in range(len(lr_score)):
if lr_score[i] == '.':
lr_score[i] = None
if len(fathmm_coding_score) > 1:
for i in range(len(fathmm_coding_score)):
if fathmm_coding_score[i] == '.':
fathmm_coding_score[i] = None
if len(dann_score) > 1:
for i in range(len(dann_score)):
if dann_score[i] == '.':
dann_score[i] = None
if len(integrated_fitcons_score) > 1:
for i in range(len(integrated_fitcons_score)):
if integrated_fitcons_score[i] == '.':
integrated_fitcons_score[i] = None
if len(gm12878_fitcons_score) > 1:
for i in range(len(gm12878_fitcons_score)):
if gm12878_fitcons_score[i] == '.':
gm12878_fitcons_score[i] = None
if len(h1_hesc_fitcons_score) > 1:
for i in range(len(h1_hesc_fitcons_score)):
if h1_hesc_fitcons_score[i] == '.':
h1_hesc_fitcons_score[i] = None
if len(huvec_fitcons_score) > 1:
for i in range(len(huvec_fitcons_score)):
if huvec_fitcons_score[i] == '.':
huvec_fitcons_score[i] = None
# load as json data
one_snp_json = {
"_id": HGVS,
"dbnsfp": {
"rsid": fields[6],
#"rsid_dbSNP144": fields[6],
"chrom": chrom,
"hg19": {
"start": chromStart,
"end": chromEnd
},
"hg18": {
"start": fields[10],
"end": hg18_end
},
"hg38": {
"start": fields[1],
"end": fields[1]
},
"ref": ref,
"alt": alt,
"aa": {
"ref": fields[4],
"alt": fields[5],
"pos": fields[22],
"refcodon": fields[13],
"codonpos": fields[14],
"codon_degeneracy": fields[15]
},
"genename": fields[11],
"uniprot": uniprot,
"interpro_domain": fields[180],
"cds_strand": fields[12],
"ancestral_allele": fields[16],
#"altaineandertal": fields[17],
#"denisova": fields[18]
"ensembl": {
"geneid": fields[19],
"transcriptid": fields[20],
"proteinid": fields[21]
},
"sift": {
"score": sift_score,
"converted_rankscore": fields[24],
"pred": fields[25]
},
"polyphen2": {
"hdiv": {
"score": hdiv_score,
"rankscore": fields[30],
"pred": fields[31]
},
"hvar": {
"score": hvar_score,
"rankscore": fields[33],
"pred": fields[34]
}
},
"lrt": {
"score": lrt_score,
"converted_rankscore": fields[36],
"pred": fields[37],
"omega": fields[38]
},
"mutationtaster": {
"score": mutationtaster_score,
"converted_rankscore": fields[40],
"pred": fields[41],
"model": fields[42],
"AAE": fields[43]
},
"mutationassessor": {
"score": mutationassessor_score,
"rankscore": fields[47],
"pred": fields[48]
},
"fathmm": {
"score": fathmm_score,
"rankscore": fields[50],
"pred": fields[51]
},
"provean": {
"score": provean_score,
"rankscore": fields[53],
"pred": fields[54]
},
"vest3": {
"score": vest3_score,
"rankscore": fields[57],
"transcriptid": fields[55],
"transcriptvar": fields[56]
},
"fathmm-mkl": {
"coding_score": fathmm_coding_score,
"coding_rankscore": fields[72],
"coding_pred": fields[73],
"coding_group": fields[74]
},
"eigen": {
"raw": fields[75],
"phred": fields[76],
"raw_rankscore": fields[77]
},
"eigen-pc": {
"raw": fields[78],
"raw_rankscore": fields[79]
},
"genocanyon": {
"score": fields[80],
"rankscore": fields[81]
},
"metasvm": {
"score": metasvm_score,
"rankscore": fields[60],
"pred": fields[61]
},
"metalr": {
"score": lr_score,
"rankscore": fields[63],
"pred": fields[64]
},
"reliability_index": fields[65],
"dann": {
"score": dann_score,
"rankscore": fields[70]
},
"gerp++": {
"nr": fields[94],
"rs": fields[95],
"rs_rankscore": fields[96]
},
"integrated": {
"fitcons_score": integrated_fitcons_score,
"fitcons_rankscore": fields[83],
"confidence_value": fields[84]
},
"gm12878": {
"fitcons_score": gm12878_fitcons_score,
"fitcons_rankscore": fields[86],
"confidence_value": fields[87]
},
"h1-hesc": {
"fitcons_score": h1_hesc_fitcons_score,
"fitcons_rankscore": fields[89],
"confidence_value": fields[90]
},
"huvec": {
"fitcons_score": huvec_fitcons_score,
"fitcons_rankscore": fields[92],
"confidence_value": fields[93]
},
"phylo": {
"p100way": {
"vertebrate": fields[97],
"vertebrate_rankscore": fields[98]
},
"p20way": {
"mammalian": fields[99],
"mammalian_rankscore": fields[100]
}
},
"phastcons": {
"100way": {
"vertebrate": fields[101],
"vertebrate_rankscore": fields[102]
},
"20way": {
"mammalian": fields[103],
"mammalian_rankscore": fields[104]
}
},
"siphy_29way": {
"pi": siphy,
"logodds": fields[106],
"logodds_rankscore": fields[107]
},
"1000gp3": {
"ac": fields[108],
"af": fields[109],
"afr_ac": fields[110],
"afr_af": fields[111],
"eur_ac": fields[112],
"eur_af": fields[113],
"amr_ac": fields[114],
"amr_af": fields[115],
"eas_ac": fields[116],
"eas_af": fields[117],
"sas_ac": fields[118],
"sas_af": fields[119]
},
"twinsuk": {
"ac": fields[120],
"af": fields[121]
},
"alspac": {
"ac": fields[122],
"af": fields[123]
},
"esp6500": {
"aa_ac": fields[124],
"aa_af": fields[125],
"ea_ac": fields[126],
"ea_af": fields[127]
},
"exac": {
"ac": fields[128],
"af": fields[129],
"adj_ac": fields[130],
"adj_af": fields[131],
"afr_ac": fields[132],
"afr_af": fields[133],
"amr_ac": fields[134],
"amr_af": fields[135],
"eas_ac": fields[136],
"eas_af": fields[137],
"fin_ac": fields[138],
"fin_af": fields[139],
"nfe_ac": fields[140],
"nfe_af": fields[141],
"sas_ac": fields[142],
"sas_af": fields[143]
},
"exac_nontcga": {
"ac": fields[144],
"af": fields[145],
"adj_ac": fields[146],
"adj_af": fields[147],
"afr_ac": fields[148],
"afr_af": fields[149],
"amr_ac": fields[150],
"amr_af": fields[151],
"eas_ac": fields[152],
"eas_af": fields[153],
"fin_ac": fields[154],
"fin_af": fields[155],
"nfe_ac": fields[156],
"nfe_af": fields[157],
"sas_ac": fields[158],
"sas_af": fields[159]
},
"exac_nonpsych": {
"ac": fields[160],
"af": fields[161],
"adj_ac": fields[162],
"adj_af": fields[163],
"afr_ac": fields[164],
"afr_af": fields[165],
"amr_ac": fields[166],
"amr_af": fields[167],
"eas_ac": fields[168],
"eas_af": fields[169],
"fin_ac": fields[170],
"fin_af": fields[171],
"nfe_ac": fields[172],
"nfe_af": fields[173]
},
"clinvar": {
"rs": fields[176],
"clinsig": fields[177],
"trait": fields[178],
"golden_stars": fields[179]
},
"gtex": gtex
}
}
one_snp_json = list_split(dict_sweep(unlist(value_convert(one_snp_json)), vals=["."]), ";")
one_snp_json["dbnsfp"]["chrom"] = str(one_snp_json["dbnsfp"]["chrom"])
return one_snp_json
def _map_line_to_json(fields):
# specific variable treatment
chrom = fields[0]
if fields[7] == ".":
hg18_end = "."
else:
hg18_end = int(fields[7])+1
chromStart = int(fields[1])
chromEnd = int(fields[1]) + 1
allele1 = fields[2]
allele2 = fields[3]
HGVS = "chr%s:g.%d%s>%s" % (chrom, chromStart, allele1, allele2)
if fields[74] == ".":
siphy = "."
else:
freq = fields[74].split(":")
siphy = {'a': freq[0], 'c': freq[1], 'g': freq[2], 't': freq[3]}
acc = fields[11].rstrip().rstrip(';').split(";")
pos = fields[13].rstrip().rstrip(';').split(";")
uniprot = map(dict, map(lambda t: zip(('acc', 'pos'), t), zip(acc, pos)))
# load as json data
one_snp_json = {
"_id": HGVS,
"dbnsfp":
{
"chrom": chrom,
"hg19":
{
"start": fields[1],
"end": chromEnd
},
"hg18":
{
"start": fields[7],
"end": hg18_end
},
"hg38":
{
"chrom": fields[8],
"pos": fields[9]
},
"allele1": allele1,
"allele2": allele2,
"aa":
{
"ref": fields[4],
"alt": fields[5],
"pos": fields[23],
"refcodon": fields[16],
"codonpos": fields[18],
"aapos_sift": fields[24],
"aapos_fathmm": fields[25]
},
"genename": fields[10],
"uniprot": uniprot,
"interpro_domain": fields[14],
"cds_strand": fields[15],
"slr_test_statistic": fields[17],
"fold-degenerate": fields[19],
"ancestral_allele": fields[20],
"ensembl":
{
"geneid": fields[21],
"transcriptid": fields[22]
},
"sift":
{
"score": fields[26],
"converted_rankscore": fields[27],
"pred": fields[28]
},
"polyphen2":
{
"hdiv":
{
"score": fields[29],
"rankscore": fields[30],
"pred": fields[31]
},
"hvar":
{
"score": fields[32],
"rankscore": fields[33],
"pred": fields[34]
}
},
"lrt":
{
"score": fields[35],
"converted_rankscore": fields[36],
"pred": fields[37]
},
"mutationtaster":
{
"score": fields[38],
"converted_rankscore": fields[39],
"pred": fields[40]
},
"mutationassessor":
{
"score": fields[41],
"rankscore": fields[42],
"pred": fields[43]
},
"fathmm":
{
"score": fields[44],
"rankscore": fields[45],
"pred": fields[46]
},
"radialsvm":
{
"score": fields[47],
"rankscore": fields[48],
"pred": fields[49]
},
"lr":
{
"score": fields[50],
"rankscore": fields[51],
"pred": fields[52]
},
"reliability_index": fields[53],
"vest3":
{
"score": fields[54],
"rankscore": fields[55]
},
"cadd":
{
"raw": fields[56],
"raw_rankscore": fields[57],
"phred": fields[58]
},
"gerp++":
{
"nr": fields[59],
"rs": fields[60],
"rs_rankscore": fields[61]
},
"phylop":
{
"46way":
{
"primate": fields[62],
"primate_rankscore": fields[63],
"placental": fields[64],
"placental_rankscore": fields[65],
},
"100way":
{
"vertebrate": fields[66],
"vertebrate_rankscore": fields[67]
}
},
"phastcons":
{
"46way":
{
"primate": fields[68],
"primate_rankscore": fields[69],
"placental": fields[70],
"placental_rankscore": fields[71],
},
"100way":
{
"vertebrate": fields[72],
"vertebrate_rankscore": fields[73]
}
},
"siphy_29way":
{
"pi": siphy,
"logodds": fields[75],
"logodds_rankscore": fields[76]
},
"lrt_omega": fields[77],
"unisnp_ids": fields[78],
"1000gp1":
{
"ac": fields[79],
"af": fields[80],
"afr_ac": fields[81],
"afr_af": fields[82],
"eur_ac": fields[83],
"eur_af": fields[84],
"amr_ac": fields[85],
"amr_af": fields[86],
"asn_ac": fields[87],
"asn_af": fields[88]
},
"esp6500":
{
"aa_af": fields[89],
"ea_af": fields[90]
},
"aric5606":
{
"aa_ac": fields[91],
"aa_af": fields[92],
"ea_ac": fields[93],
"ea_af": fields[94]
},
"clinvar":
{
"rs": fields[95],
"clin_sig": fields[96],
"trait": fields[97]
}
}
}
one_snp_json = list_split(dict_sweep(unlist(value_convert(one_snp_json)), vals=["."]), ";")
one_snp_json["dbnsfp"]["chrom"] = str(one_snp_json["dbnsfp"]["chrom"])
return one_snp_json
def _map_line_to_json(cp):
try:
clinical_significance = cp.ReferenceClinVarAssertion.\
ClinicalSignificance.Description
except:
clinical_significance = None
rcv_accession = cp.ReferenceClinVarAssertion.ClinVarAccession.Acc
try:
review_status = cp.ReferenceClinVarAssertion.ClinicalSignificance.\
ReviewStatus
except:
review_status = None
try:
last_evaluated = cp.ReferenceClinVarAssertion.ClinicalSignificance.\
DateLastEvaluated
except:
last_evaluated = None
variant_id = cp.ReferenceClinVarAssertion.MeasureSet.ID
number_submitters = len(cp.ClinVarAssertion)
# some items in clinvar_xml doesn't have origin information
try:
origin = cp.ReferenceClinVarAssertion.ObservedIn[0].Sample.Origin
except:
origin = None
trait = cp.ReferenceClinVarAssertion.TraitSet.Trait[0]
synonyms = []
conditions_name = ''
for name in trait.Name:
if name.ElementValue.Type == 'Alternate':
synonyms.append(name.ElementValue.get_valueOf_())
if name.ElementValue.Type == 'Preferred':
conditions_name += name.ElementValue.get_valueOf_()
identifiers = {}
for item in trait.XRef:
if item.DB == 'Human Phenotype Ontology':
key = 'Human_Phenotype_Ontology'
else:
key = item.DB
identifiers[key.lower()] = item.ID
for symbol in trait.Symbol:
if symbol.ElementValue.Type == 'Preferred':
conditions_name += ' (' + symbol.ElementValue.get_valueOf_() + ')'
age_of_onset = ''
for _set in trait.AttributeSet:
if _set.Attribute.Type == 'age of onset':
age_of_onset = _set.Attribute.get_valueOf_()
# MeasureSet.Measure return a list, there might be multiple
# Measure under one MeasureSet
for Measure in cp.ReferenceClinVarAssertion.MeasureSet.Measure:
variation_type = Measure.Type
# exclude any item of which types belong to
# 'Variation', 'protein only' or 'Microsatellite'
if variation_type == 'Variation' or variation_type\
== 'protein only' or variation_type == 'Microsatellite':
continue
allele_id = Measure.ID
chrom = None
chromStart = None
chromEnd = None
chromStart_38 = None
chromEnd_38 = None
ref = None
alt = None
if Measure.SequenceLocation:
for SequenceLocation in Measure.SequenceLocation:
# In this version, only accept information concerning GRCh37
if 'GRCh37' in SequenceLocation.Assembly:
chrom = SequenceLocation.Chr
chromStart = SequenceLocation.start
chromEnd = SequenceLocation.stop
ref = SequenceLocation.referenceAllele
alt = SequenceLocation.alternateAllele
if 'GRCh38' in SequenceLocation.Assembly:
chromStart_38 = SequenceLocation.start
chromEnd_38 = SequenceLocation.stop
if not ref:
ref = SequenceLocation.referenceAllele
if not alt:
alt = SequenceLocation.alternateAllele
if Measure.MeasureRelationship:
try:
symbol = Measure.MeasureRelationship[0].\
Symbol[0].get_ElementValue().valueOf_
except:
symbol = None
gene_id = Measure.MeasureRelationship[0].XRef[0].ID
else:
symbol = None
gene_id = None
if Measure.Name:
name = Measure.Name[0].ElementValue.valueOf_
else:
name = None
if len(Measure.CytogeneticLocation) == 1:
cytogenic = Measure.CytogeneticLocation[0]
else:
cytogenic = Measure.CytogeneticLocation
hgvs_coding = None
hgvs_genome = None
HGVS = {'genomic': [], 'coding': [], 'non-coding': [], 'protein': []}
coding_hgvs_only = None
hgvs_id = None
# hgvs_not_validated = None
if Measure.AttributeSet:
# 'copy number loss' or 'gain' have format different\
# from other types, should be dealt with seperately
if (variation_type == 'copy number loss') or \
(variation_type == 'copy number gain'):
for AttributeSet in Measure.AttributeSet:
if 'HGVS, genomic, top level' in AttributeSet.\
Attribute.Type:
if AttributeSet.Attribute.integerValue == 37:
hgvs_genome = AttributeSet.Attribute.get_valueOf_()
if 'genomic' in AttributeSet.Attribute.Type:
HGVS['genomic'].append(
AttributeSet.Attribute.get_valueOf_())
elif 'non-coding' in AttributeSet.Attribute.Type:
HGVS['non-coding'].append(
AttributeSet.Attribute.get_valueOf_())
elif 'coding' in AttributeSet.Attribute.Type:
HGVS['coding'].append(
AttributeSet.Attribute.get_valueOf_())
elif 'protein' in AttributeSet.Attribute.Type:
HGVS['protein'].append(
AttributeSet.Attribute.get_valueOf_())
else:
for AttributeSet in Measure.AttributeSet:
if 'genomic' in AttributeSet.Attribute.Type:
HGVS['genomic'].append(
AttributeSet.Attribute.get_valueOf_())
elif 'non-coding' in AttributeSet.Attribute.Type:
HGVS['non-coding'].append(
AttributeSet.Attribute.get_valueOf_())
elif 'coding' in AttributeSet.Attribute.Type:
HGVS['coding'].append(
AttributeSet.Attribute.get_valueOf_())
elif 'protein' in AttributeSet.Attribute.Type:
HGVS['protein'].append(
AttributeSet.Attribute.get_valueOf_())
if AttributeSet.Attribute.Type == 'HGVS, coding, RefSeq':
hgvs_coding = AttributeSet.Attribute.get_valueOf_()
elif AttributeSet.Attribute.Type == \
'HGVS, genomic, top level, previous':
hgvs_genome = AttributeSet.Attribute.get_valueOf_()
break
if chrom and chromStart and chromEnd:
if variation_type == 'single nucleotide variant':
hgvs_id = "chr%s:g.%s%s>%s" % (chrom, chromStart, ref, alt)
# items whose type belong to 'Indel, Insertion, \
# Duplication' might not hava explicit alt information, \
# so we will parse from hgvs_genome
elif variation_type == 'Indel':
if hgvs_genome:
indel_position = hgvs_genome.find('del')
indel_alt = hgvs_genome[indel_position + 3:]
hgvs_id = "chr%s:g.%s_%sdel%s" % \
(chrom, chromStart, chromEnd, indel_alt)
elif variation_type == 'Deletion':
hgvs_id = "chr%s:g.%s_%sdel" % \
(chrom, chromStart, chromEnd)
elif variation_type == 'Insertion':
if hgvs_genome:
ins_position = hgvs_genome.find('ins')
if 'ins' in hgvs_genome:
ins_ref = hgvs_genome[ins_position + 3:]
hgvs_id = "chr%s:g.%s_%sins%s" % \
(chrom, chromStart, chromEnd, ins_ref)
elif variation_type == 'Duplication':
if hgvs_genome:
dup_position = hgvs_genome.find('dup')
if 'dup' in hgvs_genome:
dup_ref = hgvs_genome[dup_position + 3:]
hgvs_id = "chr%s:g.%s_%sdup%s" % \
(chrom, chromStart, chromEnd, dup_ref)
elif variation_type == 'copy number loss' or\
variation_type == 'copy number gain':
if hgvs_genome:
hgvs_id = "chr" + hgvs_genome.split('.')[1] +\
hgvs_genome.split('.')[2]
elif hgvs_coding:
hgvs_id = hgvs_coding
coding_hgvs_only = True
else:
print "couldn't find any id", rcv_accession
return
else:
print 'no measure.attribute', rcv_accession
return
for key in HGVS:
HGVS[key].sort()
rsid = None
cosmic = None
dbvar = None
uniprot = None
omim = None
# loop through XRef to find rsid as well as other ids
if Measure.XRef:
for XRef in Measure.XRef:
if XRef.Type == 'rs':
rsid = 'rs' + str(XRef.ID)
elif XRef.DB == 'COSMIC':
cosmic = XRef.ID
elif XRef.DB == 'OMIM':
omim = XRef.ID
elif XRef.DB == 'UniProtKB/Swiss-Prot':
uniprot = XRef.ID
elif XRef.DB == 'dbVar':
dbvar = XRef.ID
# make sure the hgvs_id is not none
if hgvs_id:
one_snp_json = {
"_id": hgvs_id,
"clinvar": {
"allele_id": allele_id,
"variant_id": variant_id,
"chrom": chrom,
"omim": omim,
"cosmic": cosmic,
"uniprot": uniprot,
"dbvar": dbvar,
"hg19": {
"start": chromStart,
"end": chromEnd
},
"hg38": {
"start": chromStart_38,
"end": chromEnd_38
},
"type": variation_type,
"gene": {
"id": gene_id,
"symbol": symbol
},
"rcv": {
"accession": rcv_accession,
"clinical_significance": clinical_significance,
"number_submitters": number_submitters,
"review_status": review_status,
"last_evaluated": str(last_evaluated),
"preferred_name": name,
"origin": origin,
"conditions": {
"name": conditions_name,
"synonyms": synonyms,
"identifiers": identifiers,
"age_of_onset": age_of_onset
}
},
"rsid": rsid,
"cytogenic": cytogenic,
"hgvs": HGVS,
"coding_hgvs_only": coding_hgvs_only,
"ref": ref,
"alt": alt
}
}
obj = (dict_sweep(
unlist(
value_convert(one_snp_json, [
'chrom', 'omim', 'id', 'orphanet', 'gene',
'rettbase_(cdkl5)', 'cosmic', 'dbrbc'
])), [None, '', 'None']))
yield obj
def _map_line_to_json(cp):
clinical_siginificance = cp.ReferenceClinVarAssertion.\
ClinicalSignificance.Description
rcv_accession = cp.ReferenceClinVarAssertion.ClinVarAccession.Acc
review_status = cp.ReferenceClinVarAssertion.ClinicalSignificance.\
ReviewStatus
last_evaluated = cp.ReferenceClinVarAssertion.ClinicalSignificance.\
DateLastEvaluated
CLINVAR_ID = cp.ReferenceClinVarAssertion.MeasureSet.ID
number_submitters = len(cp.ClinVarAssertion)
# some items in clinvar_xml doesn't have origin information
try:
origin = cp.ReferenceClinVarAssertion.ObservedIn[0].Sample.Origin
except:
origin = None
# MeasureSet.Measure return a list, there might be multiple
# Measure under one MeasureSet
for Measure in cp.ReferenceClinVarAssertion.MeasureSet.Measure:
variation_type = Measure.Type
# exclude any item of which types belong to
# 'Variation', 'protein only' or 'Microsatellite'
if variation_type == 'Variation' or variation_type\
== 'protein only' or variation_type == 'Microsatellite':
continue
allele_id = Measure.ID
chrom = None
chromStart = None
chromEnd = None
ref = None
alt = None
if Measure.SequenceLocation:
for SequenceLocation in Measure.SequenceLocation:
# In this version, only accept information concerning GRCh37
if 'GRCh37' in SequenceLocation.Assembly:
chrom = SequenceLocation.Chr
chromStart = SequenceLocation.start
chromEnd = SequenceLocation.stop
ref = SequenceLocation.referenceAllele
alt = SequenceLocation.alternateAllele
if Measure.MeasureRelationship:
try:
symbol = Measure.MeasureRelationship[0].\
Symbol[0].get_ElementValue().valueOf_
except:
symbol = None
gene_id = Measure.MeasureRelationship[0].XRef[0].ID
else:
symbol = None
gene_id = None
if Measure.Name:
name = Measure.Name[0].ElementValue.valueOf_
else:
name = None
if len(Measure.CytogeneticLocation) == 1:
cytogenic = Measure.CytogeneticLocation[0]
else:
cytogenic = Measure.CytogeneticLocation
hgvs_coding = None
hgvs_genome = None
HGVS = {'genomic': [], 'coding': [], 'non-coding': [], 'protein': []}
coding_hgvs_only = None
hgvs_id = None
# hgvs_not_validated = None
if Measure.AttributeSet:
# 'copy number loss' or 'gain' have format different\
# from other types, should be dealt with seperately
if (variation_type == 'copy number loss') or \
(variation_type == 'copy number gain'):
for AttributeSet in Measure.AttributeSet:
if 'HGVS, genomic, top level' in AttributeSet.\
Attribute.Type:
if AttributeSet.Attribute.integerValue == 37:
hgvs_genome = AttributeSet.Attribute.get_valueOf_()
if 'genomic' in AttributeSet.Attribute.Type:
HGVS['genomic'].append(AttributeSet.Attribute.
get_valueOf_())
elif 'non-coding' in AttributeSet.Attribute.Type:
HGVS['non-coding'].append(AttributeSet.Attribute.
get_valueOf_())
elif 'coding' in AttributeSet.Attribute.Type:
HGVS['coding'].append(AttributeSet.Attribute.
get_valueOf_())
elif 'protein' in AttributeSet.Attribute.Type:
HGVS['protein'].append(AttributeSet.
Attribute.get_valueOf_())
else:
for AttributeSet in Measure.AttributeSet:
if 'genomic' in AttributeSet.Attribute.Type:
HGVS['genomic'].append(AttributeSet.
Attribute.get_valueOf_())
elif 'non-coding' in AttributeSet.Attribute.Type:
HGVS['non-coding'].append(AttributeSet.
Attribute.get_valueOf_())
elif 'coding' in AttributeSet.Attribute.Type:
HGVS['coding'].append(AttributeSet.Attribute.
get_valueOf_())
elif 'protein' in AttributeSet.Attribute.Type:
HGVS['protein'].append(AttributeSet.
Attribute.get_valueOf_())
if AttributeSet.Attribute.Type == 'HGVS, coding, RefSeq':
hgvs_coding = AttributeSet.Attribute.get_valueOf_()
elif AttributeSet.Attribute.Type == \
'HGVS, genomic, top level, previous':
hgvs_genome = AttributeSet.Attribute.get_valueOf_()
break
if chrom and chromStart and chromEnd:
if variation_type == 'single nucleotide variant':
hgvs_id = "chr%s:g.%s%s>%s" % (chrom, chromStart, ref, alt)
# items whose type belong to 'Indel, Insertion, \
# Duplication' might not hava explicit alt information, \
# so we will parse from hgvs_genome
elif variation_type == 'Indel':
if hgvs_genome:
indel_position = hgvs_genome.find('del')
indel_alt = hgvs_genome[indel_position+3:]
hgvs_id = "chr%s:g.%s_%sdel%s" % \
(chrom, chromStart, chromEnd, indel_alt)
elif variation_type == 'Deletion':
hgvs_id = "chr%s:g.%s_%sdel" % \
(chrom, chromStart, chromEnd)
elif variation_type == 'Insertion':
if hgvs_genome:
ins_position = hgvs_genome.find('ins')
if 'ins' in hgvs_genome:
ins_ref = hgvs_genome[ins_position+3:]
hgvs_id = "chr%s:g.%s_%sins%s" % \
(chrom, chromStart, chromEnd, ins_ref)
elif variation_type == 'Duplication':
if hgvs_genome:
dup_position = hgvs_genome.find('dup')
if 'dup' in hgvs_genome:
dup_ref = hgvs_genome[dup_position+3:]
hgvs_id = "chr%s:g.%s_%sdup%s" % \
(chrom, chromStart, chromEnd, dup_ref)
elif variation_type == 'copy number loss' or\
variation_type == 'copy number gain':
if hgvs_genome:
hgvs_id = "chr" + hgvs_genome.split('.')[1] +\
hgvs_genome.split('.')[2]
elif hgvs_coding:
hgvs_id = hgvs_coding
coding_hgvs_only = True
else:
print "couldn't find any id", rcv_accession
return
else:
print 'no measure.attribute', rcv_accession
return
other_ids = ''
rsid = None
# loop through XRef to find rsid as well as other ids
if Measure.XRef:
for XRef in Measure.XRef:
if XRef.Type == 'rs':
rsid = 'rs' + str(XRef.ID)
other_ids = other_ids + XRef.DB + ':' + XRef.ID + ';'
# make sure the hgvs_id is not none
if hgvs_id:
one_snp_json = {
"_id": hgvs_id,
"clinvar":
{
"allele_id": allele_id,
"chrom": chrom,
"hg19":
{
"start": chromStart,
"end": chromEnd
},
"type": variation_type,
"name": name,
"gene":
{
"id": gene_id,
"symbol": symbol
},
"clinical_significance": clinical_siginificance,
"rsid": rsid,
"rcv_accession": rcv_accession,
"origin": origin,
"cytogenic": cytogenic,
"review_status": review_status,
"hgvs": HGVS,
"number_submitters": number_submitters,
"last_evaluated": str(last_evaluated),
"other_ids": other_ids,
"clinvar_id": CLINVAR_ID,
"coding_hgvs_only": coding_hgvs_only,
"ref": ref,
"alt": alt
}
}
obj = (dict_sweep(unlist(value_convert(one_snp_json)), [None]))
yield obj
def _map_line_to_json(fields, version='hg19'):
# specific variable treatment
chrom = fields[0]
if chrom == 'M':
chrom = 'MT'
# fields[7] in version 2, represent hg18_pos
if fields[10] == ".":
hg18_end = "."
else:
hg18_end = int(fields[10])
chromStart = int(fields[8])
chromEnd = int(fields[8])
chromStart_38 = int(fields[1])
ref = fields[2].upper()
alt = fields[3].upper()
HGVS_19 = "chr%s:g.%d%s>%s" % (chrom, chromStart, ref, alt)
HGVS_38 = "chr%s:g.%d%s>%s" % (chrom, chromStart_38, ref, alt)
if version == 'hg19':
HGVS = HGVS_19
elif version == 'hg38':
HGVS = HGVS_38
if fields[69] == ".":
siphy = "."
else:
freq = fields[69].split(":")
siphy = {'a': freq[0], 'c': freq[1], 'g': freq[2], 't': freq[3]}
acc = fields[26].rstrip().rstrip(';').split(";")
pos = fields[28].rstrip().rstrip(';').split(";")
uniprot = map(dict, map(lambda t: zip(('acc', 'pos'), t), zip(acc, pos)))
# load as json data
one_snp_json = {
"_id": HGVS,
"dbnsfp": {
"rsid": fields[6],
"chrom": chrom,
"hg19": {
"start": fields[8],
"end": chromEnd
},
"hg18": {
"start": fields[10],
"end": hg18_end
},
"hg38": {
"start": fields[1],
"end": fields[1]
},
"ref": ref,
"alt": alt,
"aa": {
"ref": fields[4],
"alt": fields[5],
"pos": fields[22],
"refcodon": fields[13],
"codonpos": fields[14],
},
"genename": fields[11],
"uniprot": uniprot,
"interpro_domain": fields[111],
"cds_strand": fields[12],
"ancestral_allele": fields[16],
"ensembl": {
"geneid": fields[19],
"transcriptid": fields[20]
},
"sift": {
"score": fields[23],
"converted_rankscore": fields[24],
"pred": fields[25]
},
"polyphen2": {
"hdiv": {
"score": fields[29],
"rankscore": fields[30],
"pred": fields[31]
},
"hvar": {
"score": fields[32],
"rankscore": fields[33],
"pred": fields[34]
}
},
"lrt": {
"score": fields[35],
"converted_rankscore": fields[36],
"pred": fields[37],
"omega": fields[38]
},
"mutationtaster": {
"score": fields[39],
"converted_rankscore": fields[40],
"pred": fields[41],
"model": fields[42],
"AAE": fields[43]
},
"mutationassessor": {
"score": fields[46],
"rankscore": fields[47],
"pred": fields[48]
},
"fathmm": {
"score": fields[49],
"rankscore": fields[50],
"pred": fields[51]
},
"provean": {
"score": fields[52],
"rankscore": fields[53],
"pred": fields[54]
},
"metasvm": {
"score": fields[55],
"rankscore": fields[56],
"pred": fields[57]
},
"lr": {
"score": fields[58],
"rankscore": fields[59],
"pred": fields[60]
},
"reliability_index": fields[61],
"gerp++": {
"nr": fields[62],
"rs": fields[63],
"rs_rankscore": fields[64]
},
"phylop_7way": {
"vertebrate": fields[65],
"vertebrate_rankscore": fields[66]
},
"phastcons_7way": {
"vertebrate": fields[67],
"vertebrate_rankscore": fields[68]
},
"siphy_29way": {
"pi": siphy,
"logodds": fields[70],
"logodds_rankscore": fields[71]
},
"1000gp1": {
"ac": fields[72],
"af": fields[73],
"afr_ac": fields[74],
"afr_af": fields[75],
"eur_ac": fields[76],
"eur_af": fields[77],
"amr_ac": fields[78],
"amr_af": fields[79],
"eas_ac": fields[80],
"eas_af": fields[81],
"sas_ac": fields[82],
"sas_af": fields[83]
},
"twinsuk": {
"ac": fields[84],
"af": fields[85]
},
"alspac": {
"ac": fields[86],
"af": fields[87]
},
"esp6500": {
"aa_ac": fields[88],
"aa_af": fields[89],
"ea_ac": fields[90],
"ea_af": fields[91]
},
"exac": {
"ac": fields[92],
"af": fields[93],
"adj_ac": fields[94],
"adj_af": fields[95],
"afr_ac": fields[96],
"afr_af": fields[97],
"amr_ac": fields[98],
"amr_af": fields[99],
"eas_ac": fields[100],
"eas_af": fields[101],
"fin_ac": fields[102],
"fin_af": fields[103],
"nfe_ac": fields[104],
"nfe_af": fields[105],
"sas_ac": fields[106],
"sas_af": fields[107]
},
"clinvar": {
"rs": fields[108],
"clinsig": fields[109],
"trait": fields[110]
}
}
}
one_snp_json = list_split(
dict_sweep(unlist(value_convert(one_snp_json)), vals=["."]), ";")
one_snp_json["dbnsfp"]["chrom"] = str(one_snp_json["dbnsfp"]["chrom"])
return one_snp_json
def _map_line_to_json(fields):
chrInfo = fields[0].split(":") # grch37
chrom = chrInfo[0]
chromStart = int(chrInfo[1])
ma_fin_percent = fields[7].split("/")
if fields[3]:
mutation = fields[3].split(">")
ref = mutation[0]
alt = mutation[1]
HGVS = get_hgvs_from_vcf(chrom, chromStart, ref, alt)
hg19 = get_pos_start_end(chrom, chromStart, ref, alt)
hg38 = get_pos_start_end(chrom, int(fields[30].split(":")[1]), ref, alt)
# load as json data
if HGVS is None:
return
one_snp_json = {
"_id": HGVS,
"evs":
{
"chrom": chrom,
"hg19":
{
"start": hg19[0],
"end": hg19[1]
},
"hg38":
{
"start": hg38[0],
"end": hg38[1]
},
"rsid": fields[1],
"dbsnp_version": get_dbsnp(fields[2]),
"ref": ref,
"alt": alt,
"allele_count":
{
"european_american": count_dict(fields[4]),
"african_american": count_dict(fields[5]),
"all": count_dict(fields[6])
},
"ma_fin_percent":
{
"european_american": ma_fin_percent[0],
"african_american": ma_fin_percent[1],
"all": ma_fin_percent[2]
},
"genotype_count":
{
"european_american": count_dict(fields[8]),
"african_american": count_dict(fields[9]),
"all_genotype": count_dict(fields[10])
},
"avg_sample_read": fields[11],
"gene":
{
"symbol": fields[12],
"accession": fields[13]
},
"function_gvs": fields[14],
"hgvs":
{
"coding": fields[16],
"protein": fields[15]
},
"coding_dna_size": fields[17],
"conservation":
{
"phast_cons": fields[18],
"gerp": fields[19]
},
"grantham_score": fields[20],
"polyphen2":
{
"class": polyphen(fields[21])[0],
"score": polyphen(fields[21])[1]
},
"ref_base_ncbi": fields[22],
"chimp_allele": fields[23],
"clinical_info": fields[24],
"filter_status": fields[25],
"on_illumina_human_exome_chip": fields[26],
"gwas_pubmed_info": fields[27],
"estimated_age_kyrs":
{
"ea": fields[28],
"aa": fields[29]
}
}
}
return dict_sweep(value_convert(one_snp_json), vals=["NA", "none", "unknown"])
def annotate_by_snpeff(self, varobj_list):
'''load data'''
# title of vcf
vcf_stdin = '#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO\n'
# extract each item from list, transform into vcf format
snpeff_valid_id = []
for item in varobj_list:
if '>' in item:
hgvs_info = self.snp_hgvs_id_parser(item)
try:
vcf_stdin += self.snp_vcf_constructer(hgvs_info)
except TypeError:
print(item)
continue
snpeff_valid_id.append(item)
elif item.endswith('del'):
hgvs_info = self.del_hgvs_id_parser(item)
try:
vcf_stdin += self.del_vcf_constructor(hgvs_info)
except TypeError:
print(item)
continue
snpeff_valid_id.append(item)
elif 'ins' in item and 'del' not in item:
hgvs_info = self.ins_hgvs_id_parser(item)
try:
vcf_stdin += self.ins_vcf_constructor(hgvs_info)
except TypeError:
print(item)
continue
snpeff_valid_id.append(item)
elif 'delins' in item:
hgvs_info = self.delins_hgvs_id_parser(item)
try:
vcf_stdin += self.delins_vcf_constructor(hgvs_info)
except TypeError:
print(item)
continue
else:
print(item)
print('beyond current capacity')
proc = subprocess.Popen(SNPEFF_CMD, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
(stdout, stderr) = proc.communicate(vcf_stdin)
assert stderr == '', stderr
vcf_stdout_raw = stdout.split('\n')
for vcf_line in vcf_stdout_raw:
if vcf_line.startswith('#'):
continue
elif vcf_line == '':
continue
else:
# assume the first item is 'ANN'
ann_info = vcf_line.split(';')[0]
ann = []
# Multiple annotations per VCF line
for item in ann_info.split(','):
if len(item.split('|')) > 1:
(effect, putative_impact, gene_name, gene_id, feature_type, feature_id) = item.split('|')[1:7]
(transcript_biotype, exon, hgvs_coding, hgvs_protein, cdna, cds, protein, distance_to_feature) = item.split('|')[7:15]
print(effect)
if cdna:
(cdna_position, cdna_len) = cdna.split('/')
else:
cdna_position = None
cdna_len = None
if cds:
(cds_position, cds_len) = cds.split('/')
else:
cds_position = None
cds_len = None
if protein:
(protein_position, protein_len) = protein.split('/')
else:
protein_position = None
protein_len = None
if exon:
(rank, total) = exon.split('/')
else:
rank = None
total = None
ann.append({
"effect": effect,
"putative_impact": putative_impact,
"genename": gene_name,
"gene_id": gene_id,
"feature_type": feature_type,
"feature_id": feature_id,
"transcript_biotype": transcript_biotype,
"rank": rank,
"total": total,
"hgvs.c": hgvs_coding,
"hgvs.p": hgvs_protein,
"cdna": {
"position": cdna_position,
"length": cdna_len
},
"cds": {
"position": cds_position,
"length": cds_len
},
"protein": {
"position": protein_position,
"length": protein_len
},
"distance_to_feature": distance_to_feature
})
print(ann)
# not all annotations include lof & nmd information. Set them to 'None' as default
lof = None
nmd = None
# the case that annotation include 'ann' & 'lof' & 'nmd'
if len(vcf_line.split(';')) == 3:
(lof_info, nmd_info) = vcf_line.split(';')[1:3]
# assume the second item is 'lof'
assert lof_info.startswith('LOF')
# the information to be parsed is like this: 'LOF=(PTEN|PTEN|1|1.00)'
lof_info = lof_info.split('(')[1].split(')')[0]
nmd_info = nmd_info.split('(')[1].split(')')[0]
(id_lof, name_lof, nt_lof, pt_lof) = lof_info.split('|')
(id_nmd, name_nmd, nt_nmd, pt_nmd) = nmd_info.split('|')
lof = {
"gene_id": id_lof,
"genename": name_lof,
"number_of_transcripts_in_gene": nt_lof,
"percent_of_transcripts_affected": pt_lof
}
nmd = {
"gene_id": id_nmd,
"genename": name_nmd,
"number_of_transcripts_in_gene": nt_nmd,
"percent_of_transcripts_affected": pt_nmd
}
# the case that annotation include 'ann' & 'lof or nmd'
elif len(vcf_line.split(';')) == 2:
(ann_info, idk_info) = vcf_line.split(';')
if idk_info.startswith('LOF'):
lof_info = idk_info.split('(')[1].split(')')[0]
(id_lof, name_lof, nt_lof, pt_lof) = lof_info.split('|')
lof = {
"gene_id": id_lof,
"genename": name_lof,
"number_of_transcripts_in_gene": nt_lof,
"percent_of_transcripts_affected": pt_lof
}
else:
nmd_info = idk_info.split('(')[1].split(')')[0]
(id_nmd, name_nmd, nt_nmd, pt_nmd) = nmd_info.split('|')
nmd = {
"gene_id": id_nmd,
"genename": name_nmd,
"number_of_transcripts_in_gene": nt_nmd,
"percent_of_transcripts_affected": pt_nmd
}
(chrom, pos, _id, ref, alt) = ann_info.split('\t')[0:5]
hgvs_id = get_hgvs_from_vcf(chrom, pos, ref, alt)
one_snp_json = {
"id": hgvs_id,
"snpeff": {
"ann": ann,
"lof": lof,
"nmd": nmd,
"vcf": {
"position": pos,
"ref": ref,
"alt": alt
}
}
}
snpeff_json = dict_sweep(unlist(one_snp_json), vals=['', None])
yield snpeff_json
def _map_line_to_json(fields, version):
# specific variable treatment
chrom = fields[0]
if chrom == 'M':
chrom = 'MT'
# fields[7] in version 2, represent hg18_pos
if fields[10] == ".":
hg18_end = "."
else:
hg18_end = int(fields[10])
# in case of no hg19 position provided, remove the item
if fields[8] == '.':
return None
else:
chromStart = int(fields[8])
chromEnd = int(fields[8])
chromStart_38 = int(fields[1])
ref = fields[2].upper()
alt = fields[3].upper()
HGVS_19 = "chr%s:g.%d%s>%s" % (chrom, chromStart, ref, alt)
HGVS_38 = "chr%s:g.%d%s>%s" % (chrom, chromStart_38, ref, alt)
if version == 'hg19':
HGVS = HGVS_19
elif version == 'hg38':
HGVS = HGVS_38
if fields[105] == ".":
siphy = "."
else:
freq = fields[105].split(":")
siphy = {'a': freq[0], 'c': freq[1], 'g': freq[2], 't': freq[3]}
gtex_gene = fields[181].split('|')
gtex_tissue = fields[182].split('|')
gtex = map(
dict,
map(lambda t: zip(('gene', 'tissue'), t), zip(gtex_gene, gtex_tissue)))
acc = fields[26].rstrip().rstrip(';').split(";")
pos = fields[28].rstrip().rstrip(';').split(";")
uniprot = map(dict, map(lambda t: zip(('acc', 'pos'), t), zip(acc, pos)))
provean_score = fields[52].split(';')
sift_score = fields[23].split(';')
hdiv_score = fields[29].split(';')
hvar_score = fields[32].split(';')
lrt_score = fields[35].split(';')
dann_score = fields[69].split(';')
mutationtaster_score = fields[39].split(';')
mutationassessor_score = fields[46].split(';')
vest3_score = fields[57].split(';')
metasvm_score = fields[59].split(';')
fathmm_score = fields[49].split(';')
lr_score = fields[62].split(';')
fathmm_coding_score = fields[71].split(';')
integrated_fitcons_score = fields[82].split(';')
gm12878_fitcons_score = fields[85].split(';')
h1_hesc_fitcons_score = fields[88].split(';')
huvec_fitcons_score = fields[91].split(';')
if len(provean_score) > 1:
for i in range(len(provean_score)):
if provean_score[i] == '.':
provean_score[i] = None
if len(sift_score) > 1:
for i in range(len(sift_score)):
if sift_score[i] == '.':
sift_score[i] = None
if len(hdiv_score) > 1:
for i in range(len(hdiv_score)):
if hdiv_score[i] == '.':
hdiv_score[i] = None
if len(hvar_score) > 1:
for i in range(len(hvar_score)):
if hvar_score[i] == '.':
hvar_score[i] = None
if len(lrt_score) > 1:
for i in range(len(lrt_score)):
if lrt_score[i] == '.':
lrt_score[i] = None
if len(mutationtaster_score) > 1:
for i in range(len(mutationtaster_score)):
if mutationtaster_score[i] == '.':
mutationtaster_score[i] = None
if len(mutationassessor_score) > 1:
for i in range(len(mutationassessor_score)):
if mutationassessor_score[i] == '.':
mutationassessor_score[i] = None
if len(metasvm_score) > 1:
for i in range(len(metasvm_score)):
if metasvm_score[i] == '.':
metasvm_score[i] = None
if len(vest3_score) > 1:
for i in range(len(vest3_score)):
if vest3_score[i] == '.':
vest3_score[i] = None
if len(fathmm_score) > 1:
for i in range(len(fathmm_score)):
if fathmm_score[i] == '.':
fathmm_score[i] = None
if len(lr_score) > 1:
for i in range(len(lr_score)):
if lr_score[i] == '.':
lr_score[i] = None
if len(fathmm_coding_score) > 1:
for i in range(len(fathmm_coding_score)):
if fathmm_coding_score[i] == '.':
fathmm_coding_score[i] = None
if len(dann_score) > 1:
for i in range(len(dann_score)):
if dann_score[i] == '.':
dann_score[i] = None
if len(integrated_fitcons_score) > 1:
for i in range(len(integrated_fitcons_score)):
if integrated_fitcons_score[i] == '.':
integrated_fitcons_score[i] = None
if len(gm12878_fitcons_score) > 1:
for i in range(len(gm12878_fitcons_score)):
if gm12878_fitcons_score[i] == '.':
gm12878_fitcons_score[i] = None
if len(h1_hesc_fitcons_score) > 1:
for i in range(len(h1_hesc_fitcons_score)):
if h1_hesc_fitcons_score[i] == '.':
h1_hesc_fitcons_score[i] = None
if len(huvec_fitcons_score) > 1:
for i in range(len(huvec_fitcons_score)):
if huvec_fitcons_score[i] == '.':
huvec_fitcons_score[i] = None
# load as json data
one_snp_json = {
"_id": HGVS,
"dbnsfp": {
"rsid": fields[6],
#"rsid_dbSNP144": fields[6],
"chrom": chrom,
"hg19": {
"start": chromStart,
"end": chromEnd
},
"hg18": {
"start": fields[10],
"end": hg18_end
},
"hg38": {
"start": fields[1],
"end": fields[1]
},
"ref": ref,
"alt": alt,
"aa": {
"ref": fields[4],
"alt": fields[5],
"pos": fields[22],
"refcodon": fields[13],
"codonpos": fields[14],
"codon_degeneracy": fields[15]
},
"genename": fields[11],
"uniprot": uniprot,
"interpro_domain": fields[180],
"cds_strand": fields[12],
"ancestral_allele": fields[16],
#"altaineandertal": fields[17],
#"denisova": fields[18]
"ensembl": {
"geneid": fields[19],
"transcriptid": fields[20],
"proteinid": fields[21]
},
"sift": {
"score": sift_score,
"converted_rankscore": fields[24],
"pred": fields[25]
},
"polyphen2": {
"hdiv": {
"score": hdiv_score,
"rankscore": fields[30],
"pred": fields[31]
},
"hvar": {
"score": hvar_score,
"rankscore": fields[33],
"pred": fields[34]
}
},
"lrt": {
"score": lrt_score,
"converted_rankscore": fields[36],
"pred": fields[37],
"omega": fields[38]
},
"mutationtaster": {
"score": mutationtaster_score,
"converted_rankscore": fields[40],
"pred": fields[41],
"model": fields[42],
"AAE": fields[43]
},
"mutationassessor": {
"score": mutationassessor_score,
"rankscore": fields[47],
"pred": fields[48]
},
"fathmm": {
"score": fathmm_score,
"rankscore": fields[50],
"pred": fields[51]
},
"provean": {
"score": provean_score,
"rankscore": fields[53],
"pred": fields[54]
},
"vest3": {
"score": vest3_score,
"rankscore": fields[57],
"transcriptid": fields[55],
"transcriptvar": fields[56]
},
"fathmm-mkl": {
"coding_score": fathmm_coding_score,
"coding_rankscore": fields[72],
"coding_pred": fields[73],
"coding_group": fields[74]
},
"eigen": {
"raw": fields[75],
"phred": fields[76],
"raw_rankscore": fields[77]
},
"eigen-pc": {
"raw": fields[78],
"raw_rankscore": fields[79]
},
"genocanyon": {
"score": fields[80],
"rankscore": fields[81]
},
"metasvm": {
"score": metasvm_score,
"rankscore": fields[60],
"pred": fields[61]
},
"metalr": {
"score": lr_score,
"rankscore": fields[63],
"pred": fields[64]
},
"reliability_index": fields[65],
"dann": {
"score": dann_score,
"rankscore": fields[70]
},
"gerp++": {
"nr": fields[94],
"rs": fields[95],
"rs_rankscore": fields[96]
},
"integrated": {
"fitcons_score": integrated_fitcons_score,
"fitcons_rankscore": fields[83],
"confidence_value": fields[84]
},
"gm12878": {
"fitcons_score": gm12878_fitcons_score,
"fitcons_rankscore": fields[86],
"confidence_value": fields[87]
},
"h1-hesc": {
"fitcons_score": h1_hesc_fitcons_score,
"fitcons_rankscore": fields[89],
"confidence_value": fields[90]
},
"huvec": {
"fitcons_score": huvec_fitcons_score,
"fitcons_rankscore": fields[92],
"confidence_value": fields[93]
},
"phylo": {
"p100way": {
"vertebrate": fields[97],
"vertebrate_rankscore": fields[98]
},
"p20way": {
"mammalian": fields[99],
"mammalian_rankscore": fields[100]
}
},
"phastcons": {
"100way": {
"vertebrate": fields[101],
"vertebrate_rankscore": fields[102]
},
"20way": {
"mammalian": fields[103],
"mammalian_rankscore": fields[104]
}
},
"siphy_29way": {
"pi": siphy,
"logodds": fields[106],
"logodds_rankscore": fields[107]
},
"1000gp3": {
"ac": fields[108],
"af": fields[109],
"afr_ac": fields[110],
"afr_af": fields[111],
"eur_ac": fields[112],
"eur_af": fields[113],
"amr_ac": fields[114],
"amr_af": fields[115],
"eas_ac": fields[116],
"eas_af": fields[117],
"sas_ac": fields[118],
"sas_af": fields[119]
},
"twinsuk": {
"ac": fields[120],
"af": fields[121]
},
"alspac": {
"ac": fields[122],
"af": fields[123]
},
"esp6500": {
"aa_ac": fields[124],
"aa_af": fields[125],
"ea_ac": fields[126],
"ea_af": fields[127]
},
"exac": {
"ac": fields[128],
"af": fields[129],
"adj_ac": fields[130],
"adj_af": fields[131],
"afr_ac": fields[132],
"afr_af": fields[133],
"amr_ac": fields[134],
"amr_af": fields[135],
"eas_ac": fields[136],
"eas_af": fields[137],
"fin_ac": fields[138],
"fin_af": fields[139],
"nfe_ac": fields[140],
"nfe_af": fields[141],
"sas_ac": fields[142],
"sas_af": fields[143]
},
"exac_nontcga": {
"ac": fields[144],
"af": fields[145],
"adj_ac": fields[146],
"adj_af": fields[147],
"afr_ac": fields[148],
"afr_af": fields[149],
"amr_ac": fields[150],
"amr_af": fields[151],
"eas_ac": fields[152],
"eas_af": fields[153],
"fin_ac": fields[154],
"fin_af": fields[155],
"nfe_ac": fields[156],
"nfe_af": fields[157],
"sas_ac": fields[158],
"sas_af": fields[159]
},
"exac_nonpsych": {
"ac": fields[160],
"af": fields[161],
"adj_ac": fields[162],
"adj_af": fields[163],
"afr_ac": fields[164],
"afr_af": fields[165],
"amr_ac": fields[166],
"amr_af": fields[167],
"eas_ac": fields[168],
"eas_af": fields[169],
"fin_ac": fields[170],
"fin_af": fields[171],
"nfe_ac": fields[172],
"nfe_af": fields[173]
},
"clinvar": {
"rs": fields[176],
"clinsig": fields[177],
"trait": fields[178],
"golden_stars": fields[179]
},
"gtex": gtex
}
}
one_snp_json = list_split(
dict_sweep(unlist(value_convert(one_snp_json)), vals=["."]), ";")
one_snp_json["dbnsfp"]["chrom"] = str(one_snp_json["dbnsfp"]["chrom"])
return one_snp_json
def _map_line_to_json(df, version, index=0):
# specific variable treatment
chrom = df["#chr"]
if chrom == 'M':
chrom = 'MT'
# fields[7] in version 2, represent hg18_pos
hg18_end = df["hg18_pos(1-coor)"]
if hg18_end == ".":
hg18_end = "."
else:
hg18_end = int(hg18_end)
# in case of no hg19 position provided, remove the item
if df["pos(1-coor)"] == '.':
return None
else:
chromStart = int(df["pos(1-coor)"])
chromEnd = chromStart
chromStart_38 = int(df["hg38_pos"])
ref = df["ref"].upper()
alt = df["alt"].upper()
HGVS_19 = "chr%s:g.%d%s>%s" % (chrom, chromStart, ref, alt)
HGVS_38 = "chr%s:g.%d%s>%s" % (chrom, chromStart_38, ref, alt)
if version == 'hg19':
HGVS = HGVS_19
elif version == 'hg38':
HGVS = HGVS_38
siphy_29way_pi = df["SiPhy_29way_pi"]
if siphy_29way_pi == ".":
siphy = "."
else:
freq = siphy_29way_pi.split(":")
siphy = {'a': freq[0], 'c': freq[1], 'g': freq[2], 't': freq[3]}
acc = df["Uniprot_acc"].rstrip().rstrip(';').split(";")
pos = df["Uniprot_aapos"].rstrip().rstrip(';').split(";")
uniprot = map(dict, map(lambda t: zip(('acc', 'pos'), t), zip(acc, pos)))
provean_score = df["PROVEAN_score"].split(';')
sift_score = df["SIFT_score"].split(';')
hdiv_score = df["Polyphen2_HDIV_score"].split(';')
hvar_score = df["Polyphen2_HVAR_score"].split(';')
lrt_score = df["LRT_score"].split(';')
m_cap_score = df["M-CAP_score"].split(';')
mutationtaster_score = df["MutationTaster_score"].split(';')
mutationassessor_score = df["MutationAssessor_score"].split(';')
vest3_score = df["VEST3_score"].split(';')
metasvm_score = df["MetaSVM_score"].split(';')
fathmm_score = df["FATHMM_score"].split(';')
metalr_score = df["MetaLR_score"].split(';')
revel_score = df["REVEL_score"].split(';')
'''
parse mutpred top 5 features
'''
def modify_pvalue(pvalue):
return float(pvalue.strip('P = '))
mutpred_mechanisms = df["MutPred_Top5features"]
if mutpred_mechanisms not in ['.', ',', '-']:
mutpred_mechanisms = mutpred_mechanisms.split(
" (") and mutpred_mechanisms.split(";")
mutpred_mechanisms = [m.rstrip(")") for m in mutpred_mechanisms]
mutpred_mechanisms = [i.split(" (") for i in mutpred_mechanisms]
mutpred_mechanisms = sum(mutpred_mechanisms, [])
mechanisms = [{
"mechanism": mutpred_mechanisms[0],
"p_val": modify_pvalue(mutpred_mechanisms[1])
}, {
"mechanism": mutpred_mechanisms[2],
"p_val": modify_pvalue(mutpred_mechanisms[3])
}, {
"mechanism": mutpred_mechanisms[4],
"p_val": modify_pvalue(mutpred_mechanisms[5])
}, {
"mechanism": mutpred_mechanisms[6],
"p_val": modify_pvalue(mutpred_mechanisms[7])
}, {
"mechanism": mutpred_mechanisms[8],
"p_val": modify_pvalue(mutpred_mechanisms[9])
}]
else:
mechanisms = '.'
# normalize scores
def norm(arr):
return [None if item == '.' else item for item in arr]
provean_score = norm(provean_score)
sift_score = norm(sift_score)
hdiv_score = norm(hdiv_score)
hvar_score = norm(hvar_score)
lrt_score = norm(lrt_score)
m_cap_score = norm(m_cap_score)
mutationtaster_score = norm(mutationtaster_score)
mutationassessor_score = norm(mutationassessor_score)
vest3_score = norm(vest3_score)
metasvm_score = norm(metasvm_score)
fathmm_score = norm(fathmm_score)
metalr_score = norm(metalr_score)
revel_score = norm(revel_score)
# load as json data
one_snp_json = {
"_id": HGVS,
"dbnsfp": {
"rsid": df["rs_dbSNP147"],
#"rsid_dbSNP144": fields[6],
"chrom": chrom,
"hg19": {
"start": chromStart,
"end": chromEnd
},
"hg18": {
"start": df["hg18_pos(1-coor)"],
"end": hg18_end
},
"hg38": {
"start": df["hg38_pos"],
"end": df["hg38_pos"]
},
"ref": ref,
"alt": alt,
"aa": {
"ref": df["aaref"],
"alt": df["aaalt"],
"pos": df["aapos"],
"refcodon": df["refcodon"],
"codonpos": df["codonpos"]
},
"genename": df["genename"],
"uniprot": list(uniprot),
"interpro_domain": df["Interpro_domain"],
"cds_strand": df["cds_strand"],
"ancestral_allele": df["Ancestral_allele"],
#"altaineandertal": fields[17],
#"denisova": fields[18]
"ensembl": {
"geneid": df["Ensembl_geneid"],
"transcriptid": df["Ensembl_transcriptid"]
},
"sift": {
"score": sift_score,
"converted_rankscore": df["SIFT_converted_rankscore"],
"pred": df["SIFT_pred"]
},
"polyphen2": {
"hdiv": {
"score": hdiv_score,
"rankscore": df["Polyphen2_HDIV_rankscore"],
"pred": df["Polyphen2_HDIV_pred"]
},
"hvar": {
"score": hvar_score,
"rankscore": df["Polyphen2_HVAR_rankscore"],
"pred": df["Polyphen2_HVAR_pred"]
}
},
"lrt": {
"score": lrt_score,
"converted_rankscore": df["LRT_converted_rankscore"],
"pred": df["LRT_pred"],
"omega": df["LRT_Omega"]
},
"mutationtaster": {
"score": mutationtaster_score,
"converted_rankscore":
df["MutationTaster_converted_rankscore"],
"pred": df["MutationTaster_pred"]
},
"mutationassessor": {
"score": mutationassessor_score,
"rankscore": df["MutationAssessor_rankscore"],
"pred": df["MutationAssessor_pred"]
},
"fathmm": {
"score": fathmm_score,
"rankscore": df["FATHMM_rankscore"],
"pred": df["FATHMM_pred"]
},
"provean": {
"score": provean_score,
"rankscore": df["PROVEAN_converted_rankscore"],
"pred": df["PROVEAN_pred"]
},
"vest3": {
"score": vest3_score,
"rankscore": df["VEST3_rankscore"]
},
"eigen": {
"coding_or_noncoding": df["Eigen_coding_or_noncoding"],
"raw": df["Eigen-raw"],
"phred": df["Eigen-phred"]
},
"eigen-pc": {
"raw": df["Eigen-PC-raw"],
"phred": df["Eigen-PC-phred"],
"raw_rankscore": df["Eigen-PC-raw_rankscore"]
},
"metasvm": {
"score": metasvm_score,
"rankscore": df["MetaSVM_rankscore"],
"pred": df["MetaSVM_pred"]
},
"metalr": {
"score": metalr_score,
"rankscore": df["MetaLR_rankscore"],
"pred": df["MetaLR_pred"]
},
"reliability_index": df["Reliability_index"],
"m_cap_score": {
"score": m_cap_score,
"rankscore": df["M-CAP_rankscore"],
"pred": df["M-CAP_pred"]
},
"revel": {
"score": revel_score,
"rankscore": df["REVEL_rankscore"]
},
"mutpred": {
"score": df["MutPred_score"],
"rankscore": df["MutPred_rankscore"],
"accession": df["MutPred_protID"],
"aa_change": df["MutPred_AAchange"],
"pred": mechanisms
},
"gerp++": {
"nr": df["GERP++_NR"],
"rs": df["GERP++_RS"],
"rs_rankscore": df["GERP++_RS_rankscore"]
},
"phylo": {
"p100way": {
"vertebrate": df["phyloP100way_vertebrate"],
"vertebrate_rankscore":
df["phyloP100way_vertebrate_rankscore"]
},
"p46way": {
"placental": df["phyloP46way_placental"],
"placental_rankscore":
df["phyloP46way_placental_rankscore"],
"primate": df["phyloP46way_primate"],
"primate_rankscore": df["phyloP46way_primate_rankscore"]
}
},
"phastcons": {
"100way": {
"vertebrate":
df["phastCons100way_vertebrate"],
"vertebrate_rankscore":
df["phastCons100way_vertebrate_rankscore"]
},
"46way": {
"placental": df["phastCons46way_placental"],
"placental_rankscore":
df["phastCons46way_placental_rankscore"],
"primate": df["phastCons46way_primate"],
"primate_rankscore": df["phastCons46way_primate_rankscore"]
}
},
"siphy_29way": {
"pi": siphy,
"logodds": df["SiPhy_29way_logOdds"],
"logodds_rankscore": df["SiPhy_29way_logOdds_rankscore"]
},
"1000gp1": {
"ac": df["1000Gp1_AC"],
"af": df["1000Gp1_AF"],
"afr_ac": df["1000Gp1_AFR_AC"],
"afr_af": df["1000Gp1_AFR_AF"],
"eur_ac": df["1000Gp1_EUR_AC"],
"eur_af": df["1000Gp1_EUR_AF"],
"amr_ac": df["1000Gp1_AMR_AC"],
"amr_af": df["1000Gp1_AMR_AF"],
"asn_ac": df["1000Gp1_ASN_AC"],
"asn_af": df["1000Gp1_ASN_AF"]
},
"esp6500": {
"aa_af": df["ESP6500_AA_AF"],
"ea_af": df["ESP6500_EA_AF "]
},
"exac": {
"ac": df["ExAC_AC"],
"af": df["ExAC_AF"],
"adj_ac": df["ExAC_Adj_AC"],
"adj_af": df["ExAC_Adj_AF"],
"afr_ac": df["ExAC_AFR_AC"],
"afr_af": df["ExAC_AFR_AF"],
"amr_ac": df["ExAC_AMR_AC"],
"amr_af": df["ExAC_AMR_AF"],
"eas_ac": df["ExAC_EAS_AC"],
"eas_af": df["ExAC_EAS_AF"],
"fin_ac": df["ExAC_FIN_AC"],
"fin_af": df["ExAC_FIN_AF"],
"nfe_ac": df["ExAC_NFE_AC"],
"nfe_af": df["ExAC_NFE_AF"],
"sas_ac": df["ExAC_SAS_AC"],
"sas_af": df["ExAC_SAS_AF"]
},
"aric5606": {
"aa_ac": df["ARIC5606_AA_AC"],
"aa_af": df["ARIC5606_AA_AF"],
"ea_ac": df["ARIC5606_EA_AC"],
"ea_af": df["ARIC5606_EA_AF"]
},
"clinvar": {
"rs":
df["clinvar_rs"],
"clinsig":
list(
map(int, [
i for i in df["clinvar_clnsig"].split("|") if i != "."
])),
"trait":
[i for i in df["clinvar_trait"].split("|") if i != "."],
"golden_stars":
list(
map(int, [
i for i in df["clinvar_golden_stars"].split("|")
if i != "."
]))
}
}
}
one_snp_json = list_split(
dict_sweep(unlist(value_convert_to_number(one_snp_json)),
vals=[".", None]), ";")
one_snp_json["dbnsfp"]["chrom"] = str(one_snp_json["dbnsfp"]["chrom"])
return one_snp_json
def _map_line_to_json(df, version, index):
# specific variable treatment
chrom = df.get_value(index, "#chr")
if chrom == 'M':
chrom = 'MT'
# fields[7] in version 2, represent hg18_pos
hg18_end = df.get_value(index, "hg18_pos(1-based)")
if hg18_end == ".":
hg18_end = "."
else:
hg18_end = int(hg18_end)
# in case of no hg19 position provided, remove the item
if df.get_value(index, "hg19_pos(1-based)") == '.':
return None
else:
chromStart = int(df.get_value(index, "hg19_pos(1-based)"))
chromEnd = chromStart
chromStart_38 = int(df.get_value(index, "pos(1-based)"))
ref = df.get_value(index, "ref").upper()
alt = df.get_value(index, "alt").upper()
HGVS_19 = "chr%s:g.%d%s>%s" % (chrom, chromStart, ref, alt)
HGVS_38 = "chr%s:g.%d%s>%s" % (chrom, chromStart_38, ref, alt)
if version == 'hg19':
HGVS = HGVS_19
elif version == 'hg38':
HGVS = HGVS_38
siphy_29way_pi = df.get_value(index, "SiPhy_29way_pi")
if siphy_29way_pi == ".":
siphy = "."
else:
freq = siphy_29way_pi.split(":")
siphy = {'a': freq[0], 'c': freq[1], 'g': freq[2], 't': freq[3]}
gtex_gene = df.get_value(index, "GTEx_V6_gene").split('|')
gtex_tissue = df.get_value(index, "GTEx_V6_tissue").split('|')
gtex = map(
dict,
map(lambda t: zip(('gene', 'tissue'), t), zip(gtex_gene, gtex_tissue)))
acc = df.get_value(index,
"Uniprot_acc_Polyphen2").rstrip().rstrip(';').split(";")
pos = df.get_value(
index, "Uniprot_aapos_Polyphen2").rstrip().rstrip(';').split(";")
uniprot = map(dict, map(lambda t: zip(('acc', 'pos'), t), zip(acc, pos)))
provean_score = df.get_value(index, "PROVEAN_score").split(';')
sift_score = df.get_value(index, "SIFT_score").split(';')
hdiv_score = df.get_value(index, "Polyphen2_HDIV_score").split(';')
hvar_score = df.get_value(index, "Polyphen2_HVAR_score").split(';')
lrt_score = df.get_value(index, "LRT_score").split(';')
m_cap_score = df.get_value(index, "M-CAP_score").split(';')
mutationtaster_score = df.get_value(index,
"MutationTaster_score").split(';')
mutationassessor_score = df.get_value(index,
"MutationAssessor_score").split(';')
vest3_score = df.get_value(index, "VEST3_score").split(';')
metasvm_score = df.get_value(index, "MetaSVM_score").split(';')
fathmm_score = df.get_value(index, "FATHMM_score").split(';')
metalr_score = df.get_value(index, "MetaLR_score").split(';')
modify_score_list = [
provean_score, sift_score, hdiv_score, hvar_score, lrt_score,
m_cap_score, mutationtaster_score, mutationassessor_score, vest3_score,
metasvm_score, fathmm_score, metalr_score
]
for _score in modify_score_list:
[None if item == '.' else item for item in _score]
# load as json data
one_snp_json = {
"_id": HGVS,
"dbnsfp": {
"rsid": df.get_value(index, "rs_dbSNP147"),
#"rsid_dbSNP144": fields[6],
"chrom": chrom,
"hg19": {
"start": chromStart,
"end": chromEnd
},
"hg18": {
"start": df.get_value(index, "hg18_pos(1-based)"),
"end": hg18_end
},
"hg38": {
"start": df.get_value(index, "pos(1-based)"),
"end": df.get_value(index, "pos(1-based)")
},
"ref": ref,
"alt": alt,
"aa": {
"ref": df.get_value(index, "aaref"),
"alt": df.get_value(index, "aaalt"),
"pos": df.get_value(index, "aapos"),
"refcodon": df.get_value(index, "refcodon"),
"codonpos": df.get_value(index, "codonpos"),
"codon_degeneracy": df.get_value(index, "codon_degeneracy"),
},
"genename": df.get_value(index, "genename"),
"uniprot": uniprot,
"interpro_domain": df.get_value(index, "Interpro_domain"),
"cds_strand": df.get_value(index, "cds_strand"),
"ancestral_allele": df.get_value(index, "Ancestral_allele"),
#"altaineandertal": fields[17],
#"denisova": fields[18]
"ensembl": {
"geneid": df.get_value(index, "Ensembl_geneid"),
"transcriptid": df.get_value(index, "Ensembl_transcriptid"),
"proteinid": df.get_value(index, "Ensembl_proteinid")
},
"sift": {
"score":
sift_score,
"converted_rankscore":
df.get_value(index, "SIFT_converted_rankscore"),
"pred":
df.get_value(index, "SIFT_pred")
},
"polyphen2": {
"hdiv": {
"score": hdiv_score,
"rankscore": df.get_value(index,
"Polyphen2_HDIV_rankscore"),
"pred": df.get_value(index, "Polyphen2_HDIV_pred")
},
"hvar": {
"score": hvar_score,
"rankscore": df.get_value(index,
"Polyphen2_HVAR_rankscore"),
"pred": df.get_value(index, "Polyphen2_HVAR_pred")
}
},
"lrt": {
"score":
lrt_score,
"converted_rankscore":
df.get_value(index, "LRT_converted_rankscore"),
"pred":
df.get_value(index, "LRT_pred"),
"omega":
df.get_value(index, "LRT_Omega")
},
"mutationtaster": {
"score":
mutationtaster_score,
"converted_rankscore":
df.get_value(index, "MutationTaster_converted_rankscore"),
"pred":
df.get_value(index, "MutationTaster_pred"),
"model":
df.get_value(index, "MutationTaster_model"),
"AAE":
df.get_value(index, "MutationTaster_AAE")
},
"mutationassessor": {
"score":
mutationassessor_score,
"rankscore":
df.get_value(index, "MutationAssessor_score_rankscore"),
"pred":
df.get_value(index, "MutationAssessor_pred")
},
"fathmm": {
"score": fathmm_score,
"rankscore": df.get_value(index, "FATHMM_converted_rankscore"),
"pred": df.get_value(index, "FATHMM_pred")
},
"provean": {
"score": provean_score,
"rankscore": df.get_value(index,
"PROVEAN_converted_rankscore"),
"pred": df.get_value(index, "PROVEAN_pred")
},
"vest3": {
"score": vest3_score,
"rankscore": df.get_value(index, "VEST3_rankscore"),
"transcriptid": df.get_value(index, "Transcript_id_VEST3"),
"transcriptvar": df.get_value(index, "Transcript_var_VEST3")
},
"fathmm-mkl": {
"coding_score":
df.get_value(index, "fathmm-MKL_coding_score"),
"coding_rankscore":
df.get_value(index, "fathmm-MKL_coding_rankscore"),
"coding_pred":
df.get_value(index, "fathmm-MKL_coding_pred"),
"coding_group":
df.get_value(index, "fathmm-MKL_coding_group")
},
"eigen": {
"coding_or_noncoding":
df.get_value(index, "Eigen_coding_or_noncoding"),
"raw":
df.get_value(index, "Eigen-raw"),
"phred":
df.get_value(index, "Eigen-phred")
},
"eigen-pc": {
"raw": df.get_value(index, "Eigen-PC-raw"),
"phred": df.get_value(index, "Eigen-PC-phred"),
"raw_rankscore": df.get_value(index, "Eigen-PC-raw_rankscore")
},
"genocanyon": {
"score": df.get_value(index, "GenoCanyon_score"),
"rankscore": df.get_value(index, "GenoCanyon_score_rankscore")
},
"metasvm": {
"score": metasvm_score,
"rankscore": df.get_value(index, "MetaSVM_rankscore"),
"pred": df.get_value(index, "MetaSVM_pred")
},
"metalr": {
"score": metalr_score,
"rankscore": df.get_value(index, "MetaLR_rankscore"),
"pred": df.get_value(index, "MetaLR_pred")
},
"reliability_index": df.get_value(index, "Reliability_index"),
"m_cap_score": {
"score": m_cap_score,
"rankscore": df.get_value(index, "M-CAP_rankscore"),
"pred": df.get_value(index, "M-CAP_pred")
},
"dann": {
"score": df.get_value(index, "DANN_score"),
"rankscore": df.get_value(index, "DANN_rankscore")
},
"gerp++": {
"nr": df.get_value(index, "GERP++_NR"),
"rs": df.get_value(index, "GERP++_RS"),
"rs_rankscore": df.get_value(index, "GERP++_RS_rankscore")
},
"integrated": {
"fitcons_score":
df.get_value(index, "integrated_fitCons_score"),
"fitcons_rankscore":
df.get_value(index, "integrated_fitCons_score_rankscore"),
"confidence_value":
df.get_value(index, "integrated_confidence_value")
},
"gm12878": {
"fitcons_score":
df.get_value(index, "GM12878_fitCons_score"),
"fitcons_rankscore":
df.get_value(index, "GM12878_fitCons_score_rankscore"),
"confidence_value":
df.get_value(index, "GM12878_confidence_value")
},
"h1-hesc": {
"fitcons_score":
df.get_value(index, "H1-hESC_fitCons_score"),
"fitcons_rankscore":
df.get_value(index, "H1-hESC_fitCons_score_rankscore"),
"confidence_value":
df.get_value(index, "H1-hESC_confidence_value")
},
"huvec": {
"fitcons_score":
df.get_value(index, "HUVEC_fitCons_score"),
"fitcons_rankscore":
df.get_value(index, "HUVEC_fitCons_score_rankscore"),
"confidence_value":
df.get_value(index, "HUVEC_confidence_value")
},
"phylo": {
"p100way": {
"vertebrate":
df.get_value(index, "phyloP100way_vertebrate"),
"vertebrate_rankscore":
df.get_value(index, "phyloP100way_vertebrate_rankscore")
},
"p20way": {
"mammalian":
df.get_value(index, "phyloP20way_mammalian"),
"mammalian_rankscore":
df.get_value(index, "phyloP20way_mammalian_rankscore")
}
},
"phastcons": {
"100way": {
"vertebrate":
df.get_value(index, "phastCons100way_vertebrate"),
"vertebrate_rankscore":
df.get_value(index, "phastCons100way_vertebrate_rankscore")
},
"20way": {
"mammalian":
df.get_value(index, "phastCons20way_mammalian"),
"mammalian_rankscore":
df.get_value(index, "phastCons20way_mammalian_rankscore")
}
},
"siphy_29way": {
"pi":
siphy,
"logodds":
df.get_value(index, "SiPhy_29way_logOdds"),
"logodds_rankscore":
df.get_value(index, "SiPhy_29way_logOdds_rankscore")
},
"1000gp3": {
"ac": df.get_value(index, "1000Gp3_AC"),
"af": df.get_value(index, "1000Gp3_AF"),
"afr_ac": df.get_value(index, "1000Gp3_AFR_AC"),
"afr_af": df.get_value(index, "1000Gp3_AFR_AF"),
"eur_ac": df.get_value(index, "1000Gp3_EUR_AC"),
"eur_af": df.get_value(index, "1000Gp3_EUR_AF"),
"amr_ac": df.get_value(index, "1000Gp3_AMR_AC"),
"amr_af": df.get_value(index, "1000Gp3_AMR_AF"),
"eas_ac": df.get_value(index, "1000Gp3_EAS_AC"),
"eas_af": df.get_value(index, "1000Gp3_EAS_AF"),
"sas_ac": df.get_value(index, "1000Gp3_SAS_AC"),
"sas_af": df.get_value(index, "1000Gp3_SAS_AF")
},
"twinsuk": {
"ac": df.get_value(index, "TWINSUK_AC"),
"af": df.get_value(index, "TWINSUK_AF")
},
"alspac": {
"ac": df.get_value(index, "ALSPAC_AC"),
"af": df.get_value(index, "ALSPAC_AF")
},
"esp6500": {
"aa_ac": df.get_value(index, "ESP6500_AA_AC"),
"aa_af": df.get_value(index, "ESP6500_AA_AF"),
"ea_ac": df.get_value(index, "ESP6500_EA_AC"),
"ea_af": df.get_value(index, "ESP6500_EA_AF")
},
"exac": {
"ac": df.get_value(index, "ExAC_AC"),
"af": df.get_value(index, "ExAC_AF"),
"adj_ac": df.get_value(index, "ExAC_Adj_AC"),
"adj_af": df.get_value(index, "ExAC_Adj_AF"),
"afr_ac": df.get_value(index, "ExAC_AFR_AC"),
"afr_af": df.get_value(index, "ExAC_AFR_AF"),
"amr_ac": df.get_value(index, "ExAC_AMR_AC"),
"amr_af": df.get_value(index, "ExAC_AMR_AF"),
"eas_ac": df.get_value(index, "ExAC_EAS_AC"),
"eas_af": df.get_value(index, "ExAC_EAS_AF"),
"fin_ac": df.get_value(index, "ExAC_FIN_AC"),
"fin_af": df.get_value(index, "ExAC_FIN_AF"),
"nfe_ac": df.get_value(index, "ExAC_NFE_AC"),
"nfe_af": df.get_value(index, "ExAC_NFE_AF"),
"sas_ac": df.get_value(index, "ExAC_SAS_AC"),
"sas_af": df.get_value(index, "ExAC_SAS_AF")
},
"exac_nontcga": {
"ac": df.get_value(index, "ExAC_nonTCGA_AC"),
"af": df.get_value(index, "ExAC_nonTCGA_AF"),
"adj_ac": df.get_value(index, "ExAC_nonTCGA_Adj_AC"),
"adj_af": df.get_value(index, "ExAC_nonTCGA_Adj_AF"),
"afr_ac": df.get_value(index, "ExAC_nonTCGA_AFR_AC"),
"afr_af": df.get_value(index, "ExAC_nonTCGA_AFR_AF"),
"amr_ac": df.get_value(index, "ExAC_nonTCGA_AMR_AC"),
"amr_af": df.get_value(index, "ExAC_nonTCGA_AMR_AF"),
"eas_ac": df.get_value(index, "ExAC_nonTCGA_EAS_AC"),
"eas_af": df.get_value(index, "ExAC_nonTCGA_EAS_AF"),
"fin_ac": df.get_value(index, "ExAC_nonTCGA_FIN_AC"),
"fin_af": df.get_value(index, "ExAC_nonTCGA_FIN_AF"),
"nfe_ac": df.get_value(index, "ExAC_nonTCGA_NFE_AC"),
"nfe_af": df.get_value(index, "ExAC_nonTCGA_NFE_AF"),
"sas_ac": df.get_value(index, "ExAC_nonTCGA_SAS_AC"),
"sas_af": df.get_value(index, "ExAC_nonTCGA_SAS_AF")
},
"exac_nonpsych": {
"ac": df.get_value(index, "ExAC_nonpsych_AC"),
"af": df.get_value(index, "ExAC_nonpsych_AF"),
"adj_ac": df.get_value(index, "ExAC_nonpsych_Adj_AC"),
"adj_af": df.get_value(index, "ExAC_nonpsych_Adj_AF"),
"afr_ac": df.get_value(index, "ExAC_nonpsych_AFR_AC"),
"afr_af": df.get_value(index, "ExAC_nonpsych_AFR_AF"),
"amr_ac": df.get_value(index, "ExAC_nonpsych_AMR_AC"),
"amr_af": df.get_value(index, "ExAC_nonpsych_AMR_AF"),
"eas_ac": df.get_value(index, "ExAC_nonpsych_EAS_AC"),
"eas_af": df.get_value(index, "ExAC_nonpsych_EAS_AF"),
"fin_ac": df.get_value(index, "ExAC_nonpsych_FIN_AC"),
"fin_af": df.get_value(index, "ExAC_nonpsych_FIN_AF"),
"nfe_ac": df.get_value(index, "ExAC_nonpsych_NFE_AC"),
"nfe_af": df.get_value(index, "ExAC_nonpsych_NFE_AF"),
"sas_ac": df.get_value(index, "ExAC_nonpsych_SAS_AC"),
"sas_af": df.get_value(index, "ExAC_nonpsych_SAS_AF")
},
"clinvar": {
"rs": df.get_value(index, "clinvar_rs"),
"clinsig": df.get_value(index, "clinvar_clnsig"),
"trait": df.get_value(index, "clinvar_trait"),
"golden_stars": df.get_value(index, "clinvar_golden_stars")
},
"gtex": gtex
}
}
one_snp_json = list_split(
dict_sweep(unlist(value_convert(one_snp_json)), vals=["."]), ";")
one_snp_json["dbnsfp"]["chrom"] = str(one_snp_json["dbnsfp"]["chrom"])
return one_snp_json
def annotate_by_snpeff(self, varobj_list):
'''load data'''
# title of vcf
vcf_stdin = '#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO\n'
# extract each item from list, transform into vcf format
snpeff_valid_id = []
for item in varobj_list:
if '>' in item:
hgvs_info = self.snp_hgvs_id_parser(item)
try:
vcf_stdin += self.snp_vcf_constructer(hgvs_info)
except TypeError:
print(item)
continue
snpeff_valid_id.append(item)
elif item.endswith('del'):
hgvs_info = self.del_hgvs_id_parser(item)
try:
vcf_stdin += self.del_vcf_constructor(hgvs_info)
except TypeError:
print(item)
continue
snpeff_valid_id.append(item)
elif 'ins' in item and 'del' not in item:
hgvs_info = self.ins_hgvs_id_parser(item)
try:
vcf_stdin += self.ins_vcf_constructor(hgvs_info)
except TypeError:
print(item)
continue
snpeff_valid_id.append(item)
elif 'delins' in item:
hgvs_info = self.delins_hgvs_id_parser(item)
try:
vcf_stdin += self.delins_vcf_constructor(hgvs_info)
except TypeError:
print(item)
continue
else:
print(item)
print('beyond current capacity')
proc = subprocess.Popen(SNPEFF_CMD,
stdin=subprocess.PIPE,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
(stdout, stderr) = proc.communicate(vcf_stdin)
assert stderr == '', stderr
vcf_stdout_raw = stdout.split('\n')
for vcf_line in vcf_stdout_raw:
if vcf_line.startswith('#'):
continue
elif vcf_line == '':
continue
else:
# assume the first item is 'ANN'
ann_info = vcf_line.split(';')[0]
ann = []
# Multiple annotations per VCF line
for item in ann_info.split(','):
if len(item.split('|')) > 1:
(effect, putative_impact, gene_name, gene_id,
feature_type, feature_id) = item.split('|')[1:7]
(transcript_biotype, exon, hgvs_coding, hgvs_protein,
cdna, cds, protein,
distance_to_feature) = item.split('|')[7:15]
print(effect)
if cdna:
(cdna_position, cdna_len) = cdna.split('/')
else:
cdna_position = None
cdna_len = None
if cds:
(cds_position, cds_len) = cds.split('/')
else:
cds_position = None
cds_len = None
if protein:
(protein_position,
protein_len) = protein.split('/')
else:
protein_position = None
protein_len = None
if exon:
(rank, total) = exon.split('/')
else:
rank = None
total = None
ann.append({
"effect": effect,
"putative_impact": putative_impact,
"genename": gene_name,
"gene_id": gene_id,
"feature_type": feature_type,
"feature_id": feature_id,
"transcript_biotype": transcript_biotype,
"rank": rank,
"total": total,
"hgvs.c": hgvs_coding,
"hgvs.p": hgvs_protein,
"cdna": {
"position": cdna_position,
"length": cdna_len
},
"cds": {
"position": cds_position,
"length": cds_len
},
"protein": {
"position": protein_position,
"length": protein_len
},
"distance_to_feature": distance_to_feature
})
print(ann)
# not all annotations include lof & nmd information. Set them to 'None' as default
lof = None
nmd = None
# the case that annotation include 'ann' & 'lof' & 'nmd'
if len(vcf_line.split(';')) == 3:
(lof_info, nmd_info) = vcf_line.split(';')[1:3]
# assume the second item is 'lof'
assert lof_info.startswith('LOF')
# the information to be parsed is like this: 'LOF=(PTEN|PTEN|1|1.00)'
lof_info = lof_info.split('(')[1].split(')')[0]
nmd_info = nmd_info.split('(')[1].split(')')[0]
(id_lof, name_lof, nt_lof, pt_lof) = lof_info.split('|')
(id_nmd, name_nmd, nt_nmd, pt_nmd) = nmd_info.split('|')
lof = {
"gene_id": id_lof,
"genename": name_lof,
"number_of_transcripts_in_gene": nt_lof,
"percent_of_transcripts_affected": pt_lof
}
nmd = {
"gene_id": id_nmd,
"genename": name_nmd,
"number_of_transcripts_in_gene": nt_nmd,
"percent_of_transcripts_affected": pt_nmd
}
# the case that annotation include 'ann' & 'lof or nmd'
elif len(vcf_line.split(';')) == 2:
(ann_info, idk_info) = vcf_line.split(';')
if idk_info.startswith('LOF'):
lof_info = idk_info.split('(')[1].split(')')[0]
(id_lof, name_lof, nt_lof,
pt_lof) = lof_info.split('|')
lof = {
"gene_id": id_lof,
"genename": name_lof,
"number_of_transcripts_in_gene": nt_lof,
"percent_of_transcripts_affected": pt_lof
}
else:
nmd_info = idk_info.split('(')[1].split(')')[0]
(id_nmd, name_nmd, nt_nmd,
pt_nmd) = nmd_info.split('|')
nmd = {
"gene_id": id_nmd,
"genename": name_nmd,
"number_of_transcripts_in_gene": nt_nmd,
"percent_of_transcripts_affected": pt_nmd
}
(chrom, pos, _id, ref, alt) = ann_info.split('\t')[0:5]
hgvs_id = get_hgvs_from_vcf(chrom, pos, ref, alt)
one_snp_json = {
"id": hgvs_id,
"snpeff": {
"ann": ann,
"lof": lof,
"nmd": nmd,
"vcf": {
"position": pos,
"ref": ref,
"alt": alt
}
}
}
snpeff_json = dict_sweep(unlist(one_snp_json), vals=['', None])
yield snpeff_json
def _map_line_to_json(fields):
assert len(fields) == VALID_COLUMN_NO
chrom = fields[0]
chromStart = fields[1]
ref = fields[2]
alt = fields[4]
HGVS = get_hgvs_from_vcf(chrom, chromStart, ref, alt)
# load as json data
if HGVS is None:
return
one_snp_json = {
"_id": HGVS,
"cadd": {
'chrom': fields[0],
'pos': fields[1],
'ref': fields[2],
'anc': fields[3],
'alt': fields[4],
'type': fields[5],
'length': fields[6],
'istv': fields[7],
'isderived': fields[8],
'annotype': fields[9],
'consequence': fields[10],
'consscore': fields[11],
'consdetail': fields[12],
'gc': fields[13],
'cpg': fields[14],
'mapability': {
'20bp': fields[15],
'35bp': fields[16]
},
'scoresegdup': fields[17],
'phast_cons': {
'primate': fields[18],
'mammalian': fields[19],
'vertebrate': fields[20]
},
'phylop': {
'primate': fields[21],
'mammalian': fields[22],
'vertebrate': fields[23]
},
'gerp': {
'n': fields[24],
's': fields[25],
'rs': fields[26],
'rs_pval': fields[27]
},
'bstatistic': fields[28],
'mutindex': fields[29],
'dna': {
'helt': fields[30],
'mgw': fields[31],
'prot': fields[32],
'roll': fields[33]
},
'mirsvr': {
'score': fields[34],
'e': fields[35],
'aln': fields[36]
},
'targetscans': fields[37],
'fitcons': fields[38],
'chmm': {
'tssa': fields[39],
'tssaflnk': fields[40],
'txflnk': fields[41],
'tx': fields[42],
'txwk': fields[43],
'enh': fields[44],
# 'enh': fields[45],
'znfrpts': fields[46],
'het': fields[47],
'tssbiv': fields[48],
'bivflnk': fields[49],
'enhbiv': fields[50],
'reprpc': fields[51],
'reprpcwk': fields[52],
'quies': fields[53],
},
'encode': {
'exp': fields[54],
'h3k27ac': fields[55],
'h3k4me1': fields[56],
'h3k4me3': fields[57],
'nucleo': fields[58],
'occ': fields[59],
'p_val': {
'comb': fields[60],
'dnas': fields[61],
'faire': fields[62],
'polii': fields[63],
'ctcf': fields[64],
'mycp': fields[65]
},
'sig': {
'dnase': fields[66],
'faire': fields[67],
'polii': fields[68],
'ctcf': fields[69],
'myc': fields[70]
},
},
'segway': fields[71],
'motif': {
'toverlap': fields[72],
'dist': fields[73],
'ecount': fields[74],
'ename': fields[75],
'ehipos': fields[76],
'escorechng': fields[77]
},
'tf': {
'bs': fields[78],
'bs_peaks': fields[79],
'bs_peaks_max': fields[80]
},
'isknownvariant': fields[81],
'esp': {
'af': fields[82],
'afr': fields[83],
'eur': fields[84]
},
'1000g': {
'af': fields[85],
'asn': fields[86],
'amr': fields[87],
'afr': fields[88],
'eur': fields[89]
},
'min_dist_tss': fields[90],
'min_dist_tse': fields[91],
'gene': {
'gene_id': fields[92],
'feature_id': fields[93],
'ccds_id': fields[94],
'genename': fields[95],
'cds': {
'cdna_pos': fields[96],
'rel_cdna_pos': fields[97],
'cds_pos': fields[98],
'rel_cds_pos': fields[99]
},
'prot': {
'protpos': fields[100],
'rel_prot_pos': fields[101],
'domain': fields[102]
}
},
'dst2splice': fields[103],
'dst2spltype': fields[104],
'exon': fields[105],
'intron': fields[106],
'oaa': fields[107], # ref aa
'naa': fields[108], # alt aa
'grantham': fields[109],
'polyphen': {
'cat': fields[110],
'val': fields[111]
},
'sift': {
'cat': fields[112],
'val': fields[113]
},
'rawscore': fields[114], # raw CADD score
'phred': fields[115] # log-percentile of raw CADD score
}
}
obj = dict_sweep(unlist(value_convert(one_snp_json)), ["NA"])
yield obj
def _map_line_to_json(df, version, index=0):
# specific variable treatment
chrom = df["#chr"]
if chrom == 'M':
chrom = 'MT'
# fields[7] in version 2, represent hg18_pos
hg18_end = df["hg18_pos(1-coor)"]
if hg18_end == ".":
hg18_end = "."
else:
hg18_end = int(hg18_end)
# in case of no hg19 position provided, remove the item
if df["pos(1-coor)"] == '.':
return None
else:
chromStart = int(df["pos(1-coor)"])
chromEnd = chromStart
chromStart_38 = int(df["hg38_pos"])
ref = df["ref"].upper()
alt = df["alt"].upper()
HGVS_19 = "chr%s:g.%d%s>%s" % (chrom, chromStart, ref, alt)
HGVS_38 = "chr%s:g.%d%s>%s" % (chrom, chromStart_38, ref, alt)
if version == 'hg19':
HGVS = HGVS_19
elif version == 'hg38':
HGVS = HGVS_38
siphy_29way_pi = df["SiPhy_29way_pi"]
if siphy_29way_pi == ".":
siphy = "."
else:
freq = siphy_29way_pi.split(":")
siphy = {'a': freq[0], 'c': freq[1], 'g': freq[2], 't': freq[3]}
acc = df["Uniprot_acc"].rstrip().rstrip(';').split(";")
pos = df["Uniprot_aapos"].rstrip().rstrip(';').split(";")
uniprot = map(dict, map(lambda t: zip(('acc', 'pos'), t), zip(acc, pos)))
provean_score = df["PROVEAN_score"].split(';')
sift_score = df["SIFT_score"].split(';')
hdiv_score = df["Polyphen2_HDIV_score"].split(';')
hvar_score = df["Polyphen2_HVAR_score"].split(';')
lrt_score = df["LRT_score"].split(';')
m_cap_score = df["M-CAP_score"].split(';')
mutationtaster_score = df["MutationTaster_score"].split(';')
mutationassessor_score = df["MutationAssessor_score"].split(';')
vest3_score = df["VEST3_score"].split(';')
metasvm_score = df["MetaSVM_score"].split(';')
fathmm_score = df["FATHMM_score"].split(';')
metalr_score = df["MetaLR_score"].split(';')
revel_score = df["REVEL_score"].split(';')
'''
parse mutpred top 5 features
'''
def modify_pvalue(pvalue):
return float(pvalue.strip('P = '))
mutpred_mechanisms = df["MutPred_Top5features"]
if mutpred_mechanisms not in ['.', ',', '-']:
mutpred_mechanisms = mutpred_mechanisms.split(" (") and mutpred_mechanisms.split(";")
mutpred_mechanisms = [m.rstrip(")") for m in mutpred_mechanisms]
mutpred_mechanisms = [i.split(" (") for i in mutpred_mechanisms]
mutpred_mechanisms = sum(mutpred_mechanisms, [])
mechanisms = [
{"mechanism": mutpred_mechanisms[0],
"p_val": modify_pvalue(mutpred_mechanisms[1])},
{"mechanism": mutpred_mechanisms[2],
"p_val": modify_pvalue(mutpred_mechanisms[3])},
{"mechanism": mutpred_mechanisms[4],
"p_val": modify_pvalue(mutpred_mechanisms[5])},
{"mechanism": mutpred_mechanisms[6],
"p_val": modify_pvalue(mutpred_mechanisms[7])},
{"mechanism": mutpred_mechanisms[8],
"p_val": modify_pvalue(mutpred_mechanisms[9])}
]
else:
mechanisms = '.'
# normalize scores
def norm(arr):
return [None if item == '.' else item for item in arr]
provean_score = norm(provean_score)
sift_score = norm(sift_score)
hdiv_score = norm(hdiv_score)
hvar_score = norm(hvar_score)
lrt_score = norm(lrt_score)
m_cap_score = norm(m_cap_score)
mutationtaster_score = norm(mutationtaster_score)
mutationassessor_score = norm(mutationassessor_score)
vest3_score = norm(vest3_score)
metasvm_score = norm(metasvm_score)
fathmm_score = norm(fathmm_score)
metalr_score = norm(metalr_score)
revel_score = norm(revel_score)
# load as json data
one_snp_json = {
"_id": HGVS,
"dbnsfp": {
"rsid": df["rs_dbSNP147"],
#"rsid_dbSNP144": fields[6],
"chrom": chrom,
"hg19": {
"start": chromStart,
"end": chromEnd
},
"hg18": {
"start": df["hg18_pos(1-coor)"],
"end": hg18_end
},
"hg38": {
"start": df["hg38_pos"],
"end": df["hg38_pos"]
},
"ref": ref,
"alt": alt,
"aa": {
"ref": df["aaref"],
"alt": df["aaalt"],
"pos": df["aapos"],
"refcodon": df["refcodon"],
"codonpos": df["codonpos"]
},
"genename": df["genename"],
"uniprot": list(uniprot),
"interpro_domain": df["Interpro_domain"],
"cds_strand": df["cds_strand"],
"ancestral_allele": df["Ancestral_allele"],
#"altaineandertal": fields[17],
#"denisova": fields[18]
"ensembl": {
"geneid": df["Ensembl_geneid"],
"transcriptid": df["Ensembl_transcriptid"]
},
"sift": {
"score": sift_score,
"converted_rankscore": df["SIFT_converted_rankscore"],
"pred": df["SIFT_pred"]
},
"polyphen2": {
"hdiv": {
"score": hdiv_score,
"rankscore": df["Polyphen2_HDIV_rankscore"],
"pred": df["Polyphen2_HDIV_pred"]
},
"hvar": {
"score": hvar_score,
"rankscore": df["Polyphen2_HVAR_rankscore"],
"pred": df["Polyphen2_HVAR_pred"]
}
},
"lrt": {
"score": lrt_score,
"converted_rankscore": df["LRT_converted_rankscore"],
"pred": df["LRT_pred"],
"omega": df["LRT_Omega"]
},
"mutationtaster": {
"score": mutationtaster_score,
"converted_rankscore": df["MutationTaster_converted_rankscore"],
"pred": df["MutationTaster_pred"]
},
"mutationassessor": {
"score": mutationassessor_score,
"rankscore": df["MutationAssessor_rankscore"],
"pred": df["MutationAssessor_pred"]
},
"fathmm": {
"score": fathmm_score,
"rankscore": df["FATHMM_rankscore"],
"pred": df["FATHMM_pred"]
},
"provean": {
"score": provean_score,
"rankscore": df["PROVEAN_converted_rankscore"],
"pred": df["PROVEAN_pred"]
},
"vest3": {
"score": vest3_score,
"rankscore": df["VEST3_rankscore"]
},
"eigen": {
"coding_or_noncoding": df["Eigen_coding_or_noncoding"],
"raw": df["Eigen-raw"],
"phred": df["Eigen-phred"]
},
"eigen-pc": {
"raw": df["Eigen-PC-raw"],
"phred": df["Eigen-PC-phred"],
"raw_rankscore": df["Eigen-PC-raw_rankscore"]
},
"metasvm": {
"score": metasvm_score,
"rankscore": df["MetaSVM_rankscore"],
"pred": df["MetaSVM_pred"]
},
"metalr": {
"score": metalr_score,
"rankscore": df["MetaLR_rankscore"],
"pred": df["MetaLR_pred"]
},
"reliability_index": df["Reliability_index"],
"m_cap_score": {
"score": m_cap_score,
"rankscore": df["M-CAP_rankscore"],
"pred": df["M-CAP_pred"]
},
"revel": {
"score": revel_score,
"rankscore": df["REVEL_rankscore"]
},
"mutpred": {
"score": df["MutPred_score"],
"rankscore": df["MutPred_rankscore"],
"accession": df["MutPred_protID"],
"aa_change": df["MutPred_AAchange"],
"pred": mechanisms
},
"gerp++": {
"nr": df["GERP++_NR"],
"rs": df["GERP++_RS"],
"rs_rankscore": df["GERP++_RS_rankscore"]
},
"phylo": {
"p100way": {
"vertebrate": df["phyloP100way_vertebrate"],
"vertebrate_rankscore": df["phyloP100way_vertebrate_rankscore"]
},
"p46way": {
"placental": df["phyloP46way_placental"],
"placental_rankscore": df["phyloP46way_placental_rankscore"],
"primate": df["phyloP46way_primate"],
"primate_rankscore": df["phyloP46way_primate_rankscore"]
}
},
"phastcons": {
"100way": {
"vertebrate": df["phastCons100way_vertebrate"],
"vertebrate_rankscore": df["phastCons100way_vertebrate_rankscore"]
},
"46way": {
"placental": df["phastCons46way_placental"],
"placental_rankscore": df["phastCons46way_placental_rankscore"],
"primate": df["phastCons46way_primate"],
"primate_rankscore": df["phastCons46way_primate_rankscore"]
}
},
"siphy_29way": {
"pi": siphy,
"logodds": df["SiPhy_29way_logOdds"],
"logodds_rankscore": df["SiPhy_29way_logOdds_rankscore"]
},
"1000gp1": {
"ac": df["1000Gp1_AC"],
"af": df["1000Gp1_AF"],
"afr_ac": df["1000Gp1_AFR_AC"],
"afr_af": df["1000Gp1_AFR_AF"],
"eur_ac": df["1000Gp1_EUR_AC"],
"eur_af": df["1000Gp1_EUR_AF"],
"amr_ac": df["1000Gp1_AMR_AC"],
"amr_af": df["1000Gp1_AMR_AF"],
"asn_ac": df["1000Gp1_ASN_AC"],
"asn_af": df["1000Gp1_ASN_AF"]
},
"esp6500": {
"aa_af": df["ESP6500_AA_AF"],
"ea_af": df["ESP6500_EA_AF "]
},
"exac": {
"ac": df["ExAC_AC"],
"af": df["ExAC_AF"],
"adj_ac": df["ExAC_Adj_AC"],
"adj_af": df["ExAC_Adj_AF"],
"afr_ac": df["ExAC_AFR_AC"],
"afr_af": df["ExAC_AFR_AF"],
"amr_ac": df["ExAC_AMR_AC"],
"amr_af": df["ExAC_AMR_AF"],
"eas_ac": df["ExAC_EAS_AC"],
"eas_af": df["ExAC_EAS_AF"],
"fin_ac": df["ExAC_FIN_AC"],
"fin_af": df["ExAC_FIN_AF"],
"nfe_ac": df["ExAC_NFE_AC"],
"nfe_af": df["ExAC_NFE_AF"],
"sas_ac": df["ExAC_SAS_AC"],
"sas_af": df["ExAC_SAS_AF"]
},
"aric5606": {
"aa_ac": df["ARIC5606_AA_AC"],
"aa_af": df["ARIC5606_AA_AF"],
"ea_ac": df["ARIC5606_EA_AC"],
"ea_af": df["ARIC5606_EA_AF"]
},
"clinvar": {
"rs": df["clinvar_rs"],
"clinsig": list(map(int,[i for i in df["clinvar_clnsig"].split("|") if i != "."])),
"trait": [i for i in df["clinvar_trait"].split("|") if i != "."],
"golden_stars": list(map(int,[i for i in df["clinvar_golden_stars"].split("|") if i != "."]))
}
}
}
one_snp_json = list_split(dict_sweep(unlist(value_convert_to_number(one_snp_json)), vals=[".", None]), ";")
one_snp_json["dbnsfp"]["chrom"] = str(one_snp_json["dbnsfp"]["chrom"])
return one_snp_json
def _map_line_to_json(fields):
assert len(fields) == VALID_COLUMN_NO
chr_info = re.findall(r"[\w']+", fields[17])
chrom = chr_info[0] # Mutation GRCh37 genome position
chromStart = chr_info[1]
chromEnd = chr_info[2]
HGVS = None
cds = fields[13]
sub = re.search(r'[ATCGMNHKRY]+>[ATCGMNHKRY]+', cds)
ins = re.search(r'ins[ATCGMN]+|ins[0-9]+', cds)
delete = cds.find('del') != -1
del_ins = re.search(r'[0-9]+>[ATCGMN]+', cds)
comp = re.search(r'[ATCGMN]+', cds)
if sub:
HGVS = "chr%s:g.%s%s" % (chrom, chromStart, sub.group())
elif ins:
HGVS = "chr%s:g.%s_%s%s" % (chrom, chromStart, chromEnd, ins.group())
elif delete:
HGVS = "chr%s:g.%s_%sdel" % (chrom, chromStart, chromEnd)
elif del_ins:
HGVS = "chr%s:g.%s_%sdelins%s" % (chrom, chromStart, chromEnd, comp.group())
# elif comp:
# HGVS = "chr%s:g.%s_%s%s" % (chrom, chromStart, chromEnd, comp.group())
else:
HGVS = fields[12]
print "Error2:", fields[15], cds, fields[17]
# load as json data
if HGVS is None:
return
one_snp_json = {
"sorter": fields[17] + fields[13],
"_id": HGVS,
"cosmic":
{
"gene":
{
"symbol": fields[0], # Gene name
"id": fields[3], # HGNC ID
"cds_length": fields[2]
},
"transcript": fields[1], # Accession Number
"sample":
{
"name": fields[4], # Sample name
"id": fields[5] # ID_sample
},
"tumour":
{
"id": fields[6], # ID_tumour
"primary_site": fields[7], # Primary site
"site_subtype": fields[8], # Site subtype
"primary_histology": fields[9], # Primary histology
"histology_subtype": fields[10], # Histology subtype
"origin": fields[1]
},
"mutation":
{
"id": "COSM" + fields[12], # Mutation ID
"cds": cds, # Mutation CDS
"aa": fields[14], # Mutation AA
"description": fields[15], # Mutation Description
"zygosity": fields[16], # Mutation zygosity
"somatic_status": fields[21] # Mutation somatic status
},
"chrom": chrom,
"hg19":
{
"start": chromStart,
"end": chromEnd
},
"pubmed": fields[22] # Pubmed_PMID
}
}
return dict_sweep(value_convert(one_snp_json), vals=[""])
def _map_line_to_json(fields):
if len(fields) == VALID_COLUMN_NO:
chrom = fields[0]
chromStart = fields[1]
allele1 = fields[2]
allele2 = fields[4]
HGVS = "chr%s:g.%s%s>%s" % (chrom, chromStart, allele1, allele2)
# load as json data
if HGVS is None:
return
one_snp_json = {
"_id": HGVS,
"cadd":
{
'chrom': fields[0],
'pos': fields[1],
'ref': fields[2],
'anc': fields[3],
'alt': fields[4],
'type': fields[5],
'length': fields[6],
'istv': fields[7],
'isderived': fields[8],
'annotype': fields[9],
'consequence': fields[10],
'consscore': fields[11],
'consdetail': fields[12],
'gc': fields[13],
'cpg': fields[14],
'mapability':
{
'20bp': fields[15],
'35bp': fields[16]
},
'scoresegdup': fields[17],
'phast_cons':
{
'primate': fields[18],
'mammalian': fields[19],
'vertebrate': fields[20]
},
'phylop':
{
'primate': fields[21],
'mammalian': fields[22],
'vertebrate': fields[23]
},
'gerp':
{
'n': fields[24],
's': fields[25],
'rs': fields[26],
'rs_pval': fields[27]
},
'bstatistic': fields[28],
'encode':
{
'exp': fields[29],
'h3k27ac': fields[30],
'h3k4me1': fields[31],
'h3k4me3': fields[32],
'nucleo': fields[33],
'occ': fields[34],
'p_val':
{
'comb': fields[35],
'dnas': fields[36],
'faire': fields[37],
'polii': fields[38],
'ctcf': fields[39],
'mycp': fields[40]
},
'sig':
{
'dnase': fields[41],
'faire': fields[42],
'polii': fields[43],
'ctcf': fields[44],
'myc': fields[45]
},
},
'segway': fields[46],
'motif':
{
'toverlap': fields[47],
'dist': fields[48],
'ecount': fields[49],
'ename': fields[50],
'ehipos': fields[51],
'escorechng': fields[52]
},
'tf':
{
'bs': fields[53],
'bs_peaks': fields[54],
'bs_peaks_max': fields[55]
},
'isknownvariant': fields[56],
'esp':
{
'af': fields[57],
'afr': fields[58],
'eur': fields[59]
},
'1000g':
{
'af': fields[60],
'asn': fields[61],
'amr': fields[62],
'afr': fields[63],
'eur': fields[64]
},
'min_dist_tss': fields[65],
'min_dist_tse': fields[66],
'gene':
{
'gene_id': fields[67],
'feature_id': fields[68],
'ccds_id': fields[69],
'genename': fields[70],
'cds':
{
'cdna_pos': fields[71],
'rel_cdna_pos': fields[72],
'cds_pos': fields[73],
'rel_cds_pos': fields[74]
},
'prot':
{
'protpos': fields[75],
'rel_prot_pos': fields[76],
'oaa': fields[81],
'naa': fields[82]
},
'dst_2_splice': fields[77],
'dst_2_spltype': fields[78],
'exon': fields[79],
'intron': fields[80]
},
'grantham': fields[83],
'polyphen':
{
'cat': fields[84],
'val': fields[85]
},
'sift':
{
'cat': fields[86],
'val': fields[87]
},
'rawscore': fields[88],
'phred': fields[89]
}
}
return dict_sweep(unlist(value_convert(one_snp_json)), "NA")
def _map_line_to_json(fields):
assert len(fields) == VALID_COLUMN_NO
chrom = fields[13]
chromStart = fields[14]
chromEnd = fields[15]
HGVS = None
cds = fields[18].split(":")
cds = cds[1]
replace = re.findall(r'[ATCGMNYR=]+', cds)
sub = re.search(r'\d([ATCGMNHKRY]>[ATCGMNHKRY])', cds)
ins = re.search(r'ins[ATCGMNHYR]+|ins[0-9]+', cds)
delete = fields[1] == 'deletion'
indel = fields[1] == 'indel'
dup = re.search(r'dup', cds)
inv = re.search(r'inv|inv[0-9]+|inv[ATCGMNHYR]+', cds)
if ins:
delete = None
indel = None
elif delete:
ins = None
indel = None
# parse from vcf file. Input chrom number
# and chromStart, and return REF, ALT
if chromStart:
record = vcf_reader.fetch(chrom, int(chromStart))
else:
record = None
if record:
REF = record.REF
ALT = record.ALT
ALT = ALT[0]
if record.is_snp and len(ALT) < 2:
mod = [REF, ALT]
else:
mod = ALT
else:
return
if sub and record.is_snp:
HGVS = "chr%s:g.%s%s>%s" % (chrom, chromStart, mod[0], mod[1])
elif ins:
HGVS = "chr%s:g.%s_%sins%s" % (chrom, chromStart, chromEnd, mod)
elif delete:
HGVS = "chr%s:g.%s_%sdel" % (chrom, chromStart, chromEnd)
elif indel:
try:
HGVS = "chr%s:g.%s_%sdelins%s" % (chrom, chromStart, chromEnd, mod)
except AttributeError:
print "ERROR:", fields[1], cds
elif dup:
HGVS = "chr%s:g.%s_%sdup%s" % (chrom, chromStart, chromEnd, mod)
elif inv:
HGVS = "chr%s:g.%s_%sinv%s" % (chrom, chromStart, chromEnd, mod)
elif replace:
HGVS = "chr%s:g.%s_%s%s" % (chrom, chromStart, chromEnd, mod)
else:
print 'ERROR:', fields[1], cds
# load as json data
if HGVS is None:
print 'None:', fields[1], cds
return None
one_snp_json = {
"_id": HGVS,
"clinvar":
{
"allele_id": fields[0],
"hg19":
{
"chr": fields[13],
"start": fields[14],
"end": fields[15]
},
"type": fields[1],
"name": fields[2],
"gene":
{
"id": fields[3],
"symbol": fields[4]
},
"clinical_significance": fields[5].split(";"),
"rsid": 'rs' + str(fields[6]),
"nsv_dbvar": fields[7],
"rcv_accession": fields[8].split(";"),
"tested_in_gtr": fields[9],
"phenotype_id": other_id(fields[10]),
"origin": fields[11],
"cytogenic": fields[16],
"review_status": fields[17],
"hgvs":
{
"coding": fields[18],
"protein": fields[19]
},
"number_submitters": fields[20],
"last_evaluated": fields[21],
"guidelines": fields[22],
"other_ids": other_id(fields[23]),
"clinvar_id": fields[24]
}
}
return dict_sweep(unlist(value_convert(one_snp_json)), vals=["-"])
def _map_line_to_json(fields):
assert len(fields) == VALID_COLUMN_NO
chrom = fields[13]
chromStart = fields[14]
chromEnd = fields[15]
HGVS = None
cds = fields[18].split(":")
cds = cds[1]
replace = re.findall(r'[ATCGMNYR=]+', cds)
sub = re.search(r'\d([ATCGMNHKRY]>[ATCGMNHKRY])', cds)
ins = re.search(r'ins[ATCGMNHYR]+|ins[0-9]+', cds)
delete = fields[1] == 'deletion'
indel = fields[1] == 'indel'
dup = re.search(r'dup', cds)
inv = re.search(r'inv|inv[0-9]+|inv[ATCGMNHYR]+', cds)
if ins:
delete = None
indel = None
elif delete:
ins = None
indel = None
# parse from vcf file. Input chrom number
# and chromStart, and return REF, ALT
if chromStart:
record = vcf_reader.fetch(chrom, int(chromStart))
else:
record = None
if record:
REF = record.REF
ALT = record.ALT
ALT = ALT[0]
if record.is_snp and len(ALT) < 2:
mod = [REF, ALT]
else:
mod = ALT
else:
return
if sub and record.is_snp:
HGVS = "chr%s:g.%s%s>%s" % (chrom, chromStart, mod[0], mod[1])
elif ins:
HGVS = "chr%s:g.%s_%sins%s" % (chrom, chromStart, chromEnd, mod)
elif delete:
HGVS = "chr%s:g.%s_%sdel" % (chrom, chromStart, chromEnd)
elif indel:
try:
HGVS = "chr%s:g.%s_%sdelins%s" % (chrom, chromStart, chromEnd, mod)
except AttributeError:
print "ERROR:", fields[1], cds
elif dup:
HGVS = "chr%s:g.%s_%sdup%s" % (chrom, chromStart, chromEnd, mod)
elif inv:
HGVS = "chr%s:g.%s_%sinv%s" % (chrom, chromStart, chromEnd, mod)
elif replace:
HGVS = "chr%s:g.%s_%s%s" % (chrom, chromStart, chromEnd, mod)
else:
print 'ERROR:', fields[1], cds
# load as json data
if HGVS is None:
print 'None:', fields[1], cds
return None
one_snp_json = {
"_id": HGVS,
"clinvar": {
"allele_id": fields[0],
"hg19": {
"chr": fields[13],
"start": fields[14],
"end": fields[15]
},
"type": fields[1],
"name": fields[2],
"gene": {
"id": fields[3],
"symbol": fields[4]
},
"clinical_significance": fields[5].split(";"),
"rsid": 'rs' + str(fields[6]),
"nsv_dbvar": fields[7],
"rcv_accession": fields[8].split(";"),
"tested_in_gtr": fields[9],
"phenotype_id": other_id(fields[10]),
"origin": fields[11],
"cytogenic": fields[16],
"review_status": fields[17],
"hgvs": {
"coding": fields[18],
"protein": fields[19]
},
"number_submitters": fields[20],
"last_evaluated": fields[21],
"guidelines": fields[22],
"other_ids": other_id(fields[23]),
"clinvar_id": fields[24]
}
}
return dict_sweep(unlist(value_convert(one_snp_json)), vals=["-"])
