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import re
def getAnnotateInfoRow_2(
from .utils_1 import Variant
row,
from .utils_for_marrvel_flatfile import (
genomeRef,
getClinVarUsingMarrvelFlatFile,
clinvarGeneDf,
getHGMDUsingFlatFile,
clinvarAlleleDf,
getAnnotateInfoRow_2,
omimGeneSortedDf,
)
omimAlleleList,
hgmdDf,
moduleList,
def getAnnotateInfoRows_2(
decipherSortedDf,
varDf,
gnomadMetricsGeneSortedDf,
genomeRef,
clinvarGeneDf,
clinvarAlleleDf,
omimGeneSortedDf,
omimAlleleList,
hgmdHPOScoreDf,
moduleList,
decipherSortedDf,
gnomadMetricsGeneSortedDf,
):
):
# NOTE(JL): It is old implementation and not used.
# But left to for tracing purpose. Feel free to remove
def f(row):
return getAnnotateInfoRow_2(
row,
genomeRef,
clinvarGeneDf,
clinvarAlleleDf,
omimGeneSortedDf,
omimAlleleList,
hgmdHPOScoreDf,
moduleList,
decipherSortedDf,
gnomadMetricsGeneSortedDf,
)
annotateInfoDf = varDf.apply(f, axis=1, result_type='expand')
return annotateInfoDf
# CL 03-14-2023: commented all printing lines
# print('type of row:', type(row))
def getAnnotateInfoRow_3_1(row, genomeRef):
varObj = Variant()
varObj = Variant()
transcriptId = row.Feature
transcriptId = row.Feature
# s=row.Uploaded_variation.split('_') '1_10204_-/T' 1_1588250_T_A
####row[0]: 21_11039079_C/A
####s: ['21', '11039079', 'C/A']
# print('row[0]:', row[0])
# two ways of input of first column either 1_1588250_T_A OR 21_11039079_C/A, so use the option flag
optFlag = 0
optFlag = 0
if row[0].find("/") != -1:
if row[0].find("/") != -1:
optFlag = 1
optFlag = 1
if optFlag == 0:
if optFlag == 0:
s = row[0].split("_")
s = row[0].split("_")
# print('s:', s)
# print('s:', s)
chrom = s[0]
chrom = s[0]
pos = int(s[1])
pos = int(s[1])
ref = s[2]
ref = s[2]
alt = s[3]
alt = s[3]
elif optFlag == 1:
elif optFlag == 1:
s = row[0].split("_")
s = row[0].split("_")
# print('s:', s)
# print('s:', s)
chrom = s[0]
chrom = s[0]
pos = int(s[1])
pos = int(s[1])
s = s[2].split("/")
s = s[2].split("/")
ref = s[0]
ref = s[0]
alt = s[1]
alt = s[1]
# get the start and stop from second column like '1:10203-10204'
# get the start and stop from second column like '1:10203-10204'
if "-" in row[1]:
if "-" in row[1]:
s = row[1].split(":")
s = row[1].split(":")
tmp = s[1]
tmp = s[1]
s = tmp.split("-")
s = tmp.split("-")
# print('s:',s)
# print('s:',s)
start = int(s[0])
start = int(s[0])
stop = int(s[1])
stop = int(s[1])
else:
else:
# start and stop the same
# start and stop the same
s = row[1].split(":")
s = row[1].split(":")
start = int(s[1])
start = int(s[1])
stop = int(s[1])
stop = int(s[1])
# print('chrom:', chrom,'pos:',pos,'ref:',ref,'alt:',alt,'start:',start,'stop:',stop)
# print('chrom:', chrom,'pos:',pos,'ref:',ref,'alt:',alt,'start:',start,'stop:',stop)
# change chrom X and Y and MT to numbers
# change chrom X and Y and MT to numbers
if chrom == "X":
if chrom == "X":
chrom = 23
chrom = 23
elif chrom == "Y":
elif chrom == "Y":
chrom = 24
chrom = 24
elif chrom == "MT":
elif chrom == "MT":
chrom = 25
chrom = 25
elif re.search(r"GL", chrom):
elif re.search(r"GL", chrom):
chrom = 26
chrom = 26
chrom = int(chrom)
chrom = int(chrom)
# if it is hg38 get its hg19 coordinates
# if it is hg38 get its hg19 coordinates
# CL 03-14-2023: we have separate database for hg19 and hg38,
# CL 03-14-2023: we have separate database for hg19 and hg38,
# we don't need to use LiftOver which is inaccurate
# we don't need to use LiftOver which is inaccurate
# related codes commented and modified
# related codes commented and modified
if genomeRef == "hg38":
if genomeRef == "hg38":
varObj.hg38Chrom = chrom
varObj.hg38Chrom = chrom
varObj.hg38Pos = pos
varObj.hg38Pos = pos
varObj.chrom = chrom
varObj.chrom = chrom
varObj.pos = pos
varObj.pos = pos
varObj.start = start
varObj.start = start
varObj.stop = stop
varObj.stop = stop
"""
retList=gethg19LocFromHg38(chrom, pos)#called from the utils_1.py
# retList=[newChrom, newPos]
varObj.hg19Chrom=retList[0]
varObj.hg19Pos=retList[1]
varObj.chrom=retList[0]
varObj.pos=retList[1]
#get the start
retList=gethg19LocFromHg38(chrom, start)
varObj.start=int(retList[1])
#get the stop
retList=gethg19LocFromHg38(chrom, stop)
varObj.stop=int(retList[1])
"""
else:
else:
varObj.hg19Chrom = chrom
varObj.hg19Chrom = chrom
varObj.hg19Pos = pos
varObj.hg19Pos = pos
varObj.chrom = chrom
varObj.chrom = chrom
varObj.pos = pos
varObj.pos = pos
varObj.start = start
varObj.start = start
varObj.stop = stop
varObj.stop = stop
geneSymbol = row.SYMBOL
geneSymbol = row.SYMBOL
# print('gene:', geneSymbol)
# print('gene:', geneSymbol)
varObj.geneSymbol = geneSymbol
varObj.geneSymbol = geneSymbol
varObj.CADD_phred = row.CADD_phred
varObj.CADD_phred = row.CADD_phred
varObj.CADD_PHRED = row.CADD_PHRED
varObj.CADD_PHRED = row.CADD_PHRED
# assign
# assign
varObj.ref = ref
varObj.ref = ref
varObj.alt = alt
varObj.alt = alt
varObj.varId_dash = "-".join([str(chrom), str(start), ref, alt])
varObj.varId_dash = "-".join([str(chrom), str(start), ref, alt])
# print('varId dash:', varObj.varId_dash)
# print('varId dash:', varObj.varId_dash)
varId = "_".join([str(chrom), str(pos), ref, alt, transcriptId])
varId = "_".join([str(chrom), str(pos), ref, alt, transcriptId])
varObj.varId = varId
varObj.varId = varId
if "ZYG" in row:
if "ZYG" in row:
varObj.zyg = row.ZYG
varObj.zyg = row.ZYG
varObj.geneEnsId = row.Gene
varObj.geneEnsId = row.Gene
varObj.rsId = row.Existing_variation
varObj.rsId = row.Existing_variation
varObj.GERPpp_RS = row.GERPpp_RS
varObj.GERPpp_RS = row.GERPpp_RS
varObj.featureType = row.Feature_type
varObj.featureType = row.Feature_type
varObj.gnomadAF = row.gnomAD_AF
varObj.gnomadAF = row.gnomAD_AF
varObj.gnomadAFg = row.gnomADg_AF
varObj.gnomadAFg = row.gnomADg_AF
varObj.CLIN_SIG = row.CLIN_SIG # CL: useless but kept for now
varObj.CLIN_SIG = row.CLIN_SIG # CL: useless but kept for now
varObj.LRT_Omega = row.LRT_Omega
varObj.LRT_Omega = row.LRT_Omega
varObj.LRT_score = row.LRT_score
varObj.LRT_score = row.LRT_score
varObj.phyloP100way_vertebrate = row.phyloP100way_vertebrate
varObj.phyloP100way_vertebrate = row.phyloP100way_vertebrate
varObj.IMPACT = row.IMPACT
varObj.IMPACT = row.IMPACT
varObj.Consequence = row.Consequence
varObj.Consequence = row.Consequence
varObj.HGVSc = row.HGVSc
varObj.HGVSc = row.HGVSc
varObj.HGVSp = row.HGVSp
varObj.HGVSp = row.HGVSp
# dbnsfp attributes
# dbnsfp attributes
varObj.GERPpp_NR = row.GERPpp_NR
varObj.GERPpp_NR = row.GERPpp_NR
varObj.DANN_score = row.DANN_score
varObj.DANN_score = row.DANN_score
varObj.FATHMM_pred = row.FATHMM_pred
varObj.FATHMM_pred = row.FATHMM_pred
varObj.FATHMM_score = row.FATHMM_score
varObj.FATHMM_score = row.FATHMM_score
varObj.GTEx_V8_gene = row.GTEx_V8_gene
varObj.GTEx_V8_gene = row.GTEx_V8_gene
varObj.GTEx_V8_tissue = row.GTEx_V8_tissue
varObj.GTEx_V8_tissue = row.GTEx_V8_tissue
varObj.Polyphen2_HDIV_score = row.Polyphen2_HDIV_score
varObj.Polyphen2_HDIV_score = row.Polyphen2_HDIV_score
varObj.Polyphen2_HVAR_score = row.Polyphen2_HVAR_score
varObj.Polyphen2_HVAR_score = row.Polyphen2_HVAR_score
varObj.REVEL_score = row.REVEL_score
varObj.REVEL_score = row.REVEL_score
varObj.SIFT_score = row.SIFT_score
varObj.SIFT_score = row.SIFT_score
varObj.clinvar_AlleleID = row.clinvar # Clinvar allele ID from clinvar.vcf.gz
varObj.clinvar_AlleleID = row.clinvar # Clinvar allele ID from clinvar.vcf.gz
varObj.clinvar_clnsig = (
varObj.clinvar_clnsig = (
row.clinvar_CLNSIG
row.clinvar_CLNSIG
) # CL: Clinvar SIG from clinvar.vcf.gz
) # CL: Clinvar SIG from clinvar.vcf.gz
# varObj.clinvar_clnsig = row.clinvar_clnsig #CL: Clinvar SIG from VEP, deleted
# varObj.clinvar_clnsig = row.clinvar_clnsig #CL: Clinvar SIG from VEP, deleted
varObj.clinvar_CLNREVSTAT = (
varObj.clinvar_CLNREVSTAT = (
row.clinvar_CLNREVSTAT
row.clinvar_CLNREVSTAT
) # CL: Clinvar STAT from clinvar.vcf.gz, for interface only
) # CL: Clinvar STAT from clinvar.vcf.gz, for interface only
varObj.clinvar_CLNSIGCONF = (
varObj.clinvar_CLNSIGCONF = (
row.clinvar_CLNSIGCONF
row.clinvar_CLNSIGCONF
) # CL: Clinvar SIGCONF from clinvar.vcf.gz
) # CL: Clinvar SIGCONF from clinvar.vcf.gz
varObj.clin_code = row.clinvar_CLNSIG # CL: feature name for ai
varObj.clin_code = row.clinvar_CLNSIG # CL: feature name for ai
varObj.fathmm_MKL_coding_score = row.fathmm_MKL_coding_score
varObj.fathmm_MKL_coding_score = row.fathmm_MKL_coding_score
varObj.LRT_score = row.LRT_score
varObj.LRT_score = row.LRT_score
varObj.LRT_Omega = row.LRT_Omega
varObj.LRT_Omega = row.LRT_Omega
varObj.phyloP100way_vertebrate = row.phyloP100way_vertebrate
varObj.phyloP100way_vertebrate = row.phyloP100way_vertebrate
varObj.M_CAP_score = row.M_CAP_score
varObj.M_CAP_score = row.M_CAP_score
varObj.MutationAssessor_score = row.MutationAssessor_score
varObj.MutationAssessor_score = row.MutationAssessor_score
varObj.MutationTaster_score = row.MutationTaster_score
varObj.MutationTaster_score = row.MutationTaster_score
varObj.ESP6500_AA_AC = row.ESP6500_AA_AC
varObj.ESP6500_AA_AC = row.ESP6500_AA_AC
varObj.ESP6500_AA_AF = row.ESP6500_AA_AF
varObj.ESP6500_AA_AF = row.ESP6500_AA_AF
varObj.ESP6500_EA_AC = row.ESP6500_EA_AC
varObj.ESP6500_EA_AC = row.ESP6500_EA_AC
varObj.ESP6500_EA_AF = row.ESP6500_EA_AF
varObj.ESP6500_EA_AF = row.ESP6500_EA_AF
varObj.VARIANT_CLASS = row.VARIANT_CLASS
varObj.VARIANT_CLASS = row.VARIANT_CLASS
varObj.Feature = row.Feature
varObj.Feature = row.Feature
varObj.hom = row.gnomADg_controls_nhomalt
varObj.hom = row.gnomADg_controls_nhomalt
varObj.hgmd_id = row.hgmd # CL added
varObj.hgmd_id = row.hgmd # CL added
varObj.hgmd_symbol = row.hgmd_GENE # CL added
varObj.hgmd_symbol = row.hgmd_GENE # CL added
varObj.hgmd_rs = row.hgmd_RANKSCORE
varObj.hgmd_rs = row.hgmd_RANKSCORE
varObj.hgmd_PHEN = row.hgmd_PHEN # CL added
varObj.hgmd_PHEN = row.hgmd_PHEN # CL added
varObj.hgmd_CLASS = row.hgmd_CLASS # CL added
varObj.hgmd_CLASS = row.hgmd_CLASS # CL added
if row.clinvar_CLNSIGCONF != "-":
if row.clinvar_CLNSIGCONF != "-":
clin_dict = dict()
clin_dict = dict()
for ro in row.clinvar_CLNSIGCONF.split("|_"):
for ro in row.clinvar_CLNSIGCONF.split("|_"):
temp = ro.split("(")
temp = ro.split("(")
clin_dict[temp[0]] = int(temp[1][0])
clin_dict[temp[0]] = int(temp[1][0])
PLP_sum = clin_dict.get("Pathogenic", 0) + clin_dict.get(
PLP_sum = clin_dict.get("Pathogenic", 0) + clin_dict.get(
"Likely_pathogenic", 0
"Likely_pathogenic", 0
)
)
varObj.clin_dict = clin_dict
varObj.clin_dict = clin_dict
varObj.clin_PLP = PLP_sum
varObj.clin_PLP = PLP_sum
varObj.clin_PLP_perc = PLP_sum / sum(clin_dict.values())
varObj.clin_PLP_perc = PLP_sum / sum(clin_dict.values())
else:
else:
if "benign" in row.clinvar_clnsig.lower():
if "benign" in row.clinvar_clnsig.lower():
varObj.clin_PLP_perc = 0
varObj.clin_PLP_perc = 0
elif "pathogenic" in row.clinvar_clnsig.lower():
elif "pathogenic" in row.clinvar_clnsig.lower():
varObj.clin_PLP_perc = 1
varObj.clin_PLP_perc = 1
else:
else:
varObj.clin_PLP_perc = "-"
varObj.clin_PLP_perc = "-"
varObj.clin_PLP = "-"
varObj.clin_PLP = "-"
varObj.clin_dict = "-"
varObj.clin_dict = "-"
if row.SpliceAI_pred != "-":
if row.SpliceAI_pred != "-":
varObj.spliceAI = row.SpliceAI_pred
varObj.spliceAI = row.SpliceAI_pred
temp = row.SpliceAI_pred.split("|")
temp = row.SpliceAI_pred.split("|")
varObj.spliceAImax = max(
varObj.spliceAImax = max(
float(temp[1]), float(temp[2]), float(temp[3]), float(temp[4])
float(temp[1]), float(temp[2]), float(temp[3]), float(temp[4])
)
)
else:
else:
varObj.spliceAI = "-"
varObj.spliceAI = "-"
varObj.spliceAImax = "-"
varObj.spliceAImax = "-"
if "conserve" in moduleList:
return vars(varObj)
# # get dgv: 1.3s
# # print('\nGetting DGV')
# dgvDictList = []
# typeList = []
# subtypeList = []
# dgvVarFound = 0
# dgvType = "-"
# dgvSubtype = "-"
# chromVal = int(varObj.chrom)
# posVal = int(varObj.pos)
# startVal = int(varObj.start)
# stopVal = int(varObj.stop)
# # CL 03-14-2023: changed column names to be compatible with hg38
# # vals=dgvDf[ ( dgvDf['hg19Chr'] == chromVal ) & ( dgvDf['hg19Start']<=startVal ) & (dgvDf['hg19Stop']>=stopVal) ]
# vals = dgvSortedDf.loc[chromVal].loc[:(startVal+1)].loc[:stopVal]
# numRows = len(vals.index)
# if numRows > 0:
# dgvVarFound = 1
# # print('\tnumrows:',numRows)
# # print('\t type of vals:', type(vals))
# # print('\tvals:', vals)
# dgvType = vals.iloc[0]["type"]
# dgvSubtype = vals.iloc[0]["subType"]
# # print('\tchrom:', chromVal,'posVal:', posVal,'start:', startVal,'stopVal:', stopVal)
# # print('\tdgvVarFound:',dgvVarFound,'dgvType:', dgvType, 'dgvsubtype:', dgvSubtype)
# typeList.append(dgvType)
# subtypeList.append(dgvSubtype)
# retList = [dgvDictList, typeList, subtypeList, dgvVarFound]
# varObj.dgvDictList = retList[0]
# varObj.dgvTypeList = retList[1]
# varObj.dgvSubtypeList = retList[2]
# varObj.dgvVarFound = retList[3]
# get decipher: 0.6s
def getAnnotateInfoRow_3_2(
decipherDictList = []
varObj,
decipherDeletionObsList = []
decipherSortedDf,
decipherStudyList = []
):
decipherVarFound = 0
# get decipher: 0.6s
deletionObs = "-"
decipherDictList = []
# get the varaint object info from varObj
decipherDeletionObsList = []
chromVal = int(varObj.chrom)
decipherStudyList = []
posVal = int(varObj.pos)
decipherVarFound = 0
startVal = int(varObj.start)
deletionObs = "-"
stopVal = int(varObj.stop)
# get the varaint object info from varObj
chromVal = int(varObj.chrom)
# CL 03-14-2023: changed column names to be compatible with hg38
posVal = int(varObj.pos)
# vals=decipherDf[ ( decipherDf['hg19Chr'] == chromVal ) & ( decipherDf['hg19Start']==startVal ) & (decipherDf['hg19Stop']==stopVal) ]
startVal = int(varObj.start)
if (chromVal, startVal, stopVal) in decipherSortedDf:
stopVal = int(varObj.stop)
vals = decipherSortedDf.loc[(chromVal, startVal, stopVal)]
decipherVarFound = 1
# CL 03-14-2023: changed column names to be compatible with hg38
deletionObs = vals.iloc[0]["deletion.obs"]
# vals=decipherDf[ ( decipherDf['hg19Chr'] == chromVal ) & ( decipherDf['hg19Start']==startVal ) & (decipherDf['hg19Stop']==stopVal) ]
decipherDeletionObsList.append(deletionObs)
if (chromVal, startVal, stopVal) in decipherSortedDf:
vals = decipherSortedDf.loc[(chromVal, startVal, stopVal)]
# print('\tchrom:', chromVal,'posVal:', posVal,'start:', startVal,'stopVal:', stopVal)
decipherVarFound = 1
# print('\tdecipherVarFound:',decipherVarFound,'decipherDeletionObs:', deletionObs)
deletionObs = vals.iloc[0]["deletion.obs"]
retList = [
decipherDeletionObsList.append(deletionObs)
decipherDictList,
decipherDeletionObsList,
decipherStudyList,
decipherVarFound,
]
# [decipherDictList,decipherDeletionObsList,decipherStudyList, decipherVarFound]
# print('\tchrom:', chromVal,'posVal:', posVal,'start:', startVal,'stopVal:', stopVal)
varObj.decipherDictList = retList[0]
# print('\tdecipherVarFound:',decipherVarFound,'decipherDeletionObs:', deletionObs)
varObj.decipherDeletionObsList = retList[1]
retList = [
varObj.decipherStudyList = retList[2]
decipherDictList,
varObj.decipherVarFound = retList[3]
decipherDeletionObsList,
decipherStudyList,
# get gnomad gene metrics from gnomad file: 3.1s
decipherVarFound,
if varObj.geneSymbol in gnomadMetricsGeneSortedDf.index: # pLI, oe_lof, oe_lof_upper,mis_z
]
val = gnomadMetricsGeneSortedDf.loc[varObj.geneSymbol]
gnomadGeneZscore = val["mis_z"]
gnomadGenePLI = val["pLI"]
gnomadGeneOELof = val["oe_lof"]
gnomadGeneOELofUpper = val["oe_lof_upper"]
else:
# get the values
gnomadGeneZscore = "-"
gnomadGenePLI = "-"
gnomadGeneOELof = "-"
gnomadGeneOELofUpper = "-"
retList = [gnomadGeneZscore, gnomadGenePLI, gnomadGeneOELof, gnomadGeneOELofUpper]
# [decipherDictList,decipherDeletionObsList,decipherStudyList, decipherVarFound]
# [decipherDictList,decipherDeletionObsList,decipherStudyList, decipherVarFound]
varObj.gnomadGeneZscore = retList[0]
return {
varObj.gnomadGenePLI = retList[1]
"decipherDictList": retList[0],
varObj.gnomadGeneOELof = retList[2] # O/E lof
"decipherDeletionObsList": retList[1],
varObj.gnomadGeneOELofUpper = retList[3] # O/E lof upper
"decipherStudyList": retList[2],
"decipherVarFound": retList[3],
}
if "curate" in moduleList:
# get OMIM: 2s
def getAnnotateInfoRow_3_3(
# print('\nGetting OMIM')
varObj,
# varObj.omimList=jsonDict['omim']
gnomadMetricsGeneSortedDf,
# retList=[varFound, geneFound, omimDict, omimGeneDict, omimAlleleDict]
):
inputSnpList = []
# get gnomad gene metrics from gnomad file: 3.1s
if "," in varObj.rsId:
if varObj.geneSymbol in gnomadMetricsGeneSortedDf.index: # pLI, oe_lof, oe_lof_upper,mis_z
inputSnpList = varObj.rsId.split(",")
val = gnomadMetricsGeneSortedDf.loc[varObj.geneSymbol]
gnomadGeneZscore = val["mis_z"]
gnomadGenePLI = val["pLI"]
gnomadGeneOELof = val["oe_lof"]
gnomadGeneOELofUpper = val["oe_lof_upper"]
else:
# get the values
gnomadGeneZscore = "-"
gnomadGenePLI = "-"
gnomadGeneOELof = "-"
gnomadGeneOELofUpper = "-"
retList = [gnomadGeneZscore, gnomadGenePLI, gnomadGeneOELof, gnomadGeneOELofUpper]
return {
"gnomadGeneZscore": retList[0],
"gnomadGenePLI": retList[1],
"gnomadGeneOELof": retList[2], # O/E lof
"gnomadGeneOELofUpper": retList[3], # O/E lof upper
}
def getAnnotateInfoRow_3_4(
varObj,
omimGeneSortedDf,
):
# get OMIM: 2s
inputSnpList = []
if "," in varObj.rsId:
inputSnpList = varObj.rsId.split(",")
else:
inputSnpList = varObj.rsId
varFound = 0
geneFound = 0
omimDict = {}
omimGeneDict = {}
omimAlleleDict = {}
phenoList = []
phenoInhList = []
phenoMimList = []
# check gene
# keys: dict_keys(['phenotypes', 'allelicVariants', 'mimNumber', 'status', 'title', 'description', 'geneEntrezId', 'geneSymbol'])
if varObj.geneSymbol in omimGeneSortedDf.index:
# print('\tgene:', varObj.geneSymbol, 'found')
geneFound = 1
omimGeneDict = omimGeneSortedDf.loc[varObj.geneSymbol]
snpList = []
for a in omimGeneDict["allelicVariants"]:
if "dbSnps" in a:
snpList.append(a["dbSnps"])
# check if input snpID matches the OMIM one
set1 = set(inputSnpList)
set2 = set(snpList)
if set1.intersection(set2):
varFound = 1
else:
else:
inputSnpList = varObj.rsId
varFound = 0
# print('\tinputSnpList:', inputSnpList)
varFound = 0
# get disease info from OMIM
geneFound = 0
# print('\tphenotypes:', type(omimGeneDict['phenotypes']), ' len:', len(omimGeneDict['phenotypes']) )
omimDict = {}
for a in omimGeneDict["phenotypes"]:
omimGeneDict = {}
# print('type:', type(a))
omimAlleleDict = {}
pheno = a["phenotype"]
phenoList = []
if "phenotypeMimNumber" in a:
phenoInhList = []
phenoMim = a["phenotypeMimNumber"]
phenoMimList = []
# check gene
# keys: dict_keys(['phenotypes', 'allelicVariants', 'mimNumber', 'status', 'title', 'description', 'geneEntrezId', 'geneSymbol'])
if varObj.geneSymbol in omimGeneSortedDf.index:
# print('\tgene:', varObj.geneSymbol, 'found')
geneFound = 1
omimGeneDict = omimGeneSortedDf.loc[varObj.geneSymbol]
snpList = []
for a in omimGeneDict["allelicVariants"]:
# print('a:', a)
# print('type:', type(a))
if "dbSnps" in a:
snpList.append(a["dbSnps"])
# print('\tsnpList:', snpList)
# print('\tlen snpList:', len(snpList))
# check if input snpID matches the OMIM one
set1 = set(inputSnpList)
set2 = set(snpList)
if set1.intersection(set2):
varFound = 1
else:
else:
varFound = 0
phenoMim = "-"
if "phenotypeInheritance" in a:
# get disease info from OMIM
phenoInh = a["phenotypeInheritance"]
# print('\tphenotypes:', type(omimGeneDict['phenotypes']), ' len:', len(omimGeneDict['phenotypes']) )
else:
for a in omimGeneDict["phenotypes"]:
phenoInh = "-"
# print('type:', type(a))
phenoList.append(pheno)
pheno = a["phenotype"]
phenoInhList.append(phenoInh)
if "phenotypeMimNumber" in a:
phenoMimList.append(str(phenoMim))
phenoMim = a["phenotypeMimNumber"]
# print('phenotype:', pheno,phenoMim,phenoInh)
else:
phenoMim = "-"
if "phenotypeInheritance" in a:
phenoInh = a["phenotypeInheritance"]
else:
phenoInh = "-"
phenoList.append(pheno)
phenoInhList.append(phenoInh)
phenoMimList.append(str(phenoMim))
# print('phenotype:', pheno,phenoMim,phenoInh)
# print('\tvarFound:', varFound)
# print('\tphenoList:', phenoList)
# print('\tphenoInhList:', phenoInhList)
# print('\tphenoMimList:', phenoMimList)
omimRet = [
varFound,
geneFound,
omimDict,
omimGeneDict,
omimAlleleDict,
phenoList,
phenoInhList,
phenoMimList,
]
varObj.omimVarFound = omimRet[0]
omimRet = [
varObj.omimGeneFound = omimRet[1]
varFound,
varObj.omimDict = omimRet[2]
geneFound,
varObj.omimGeneDict = omimRet[3]
omimDict,
varObj.omimAlleleDict = omimRet[4]
omimGeneDict,
varObj.phenoList = omimRet[5]
omimAlleleDict,
varObj.phenoInhList = omimRet[6]
phenoList,
varObj.phenoMimList = omimRet[7]
phenoInhList,
# print('OMIM res:')
phenoMimList,
# print('\tgeneFound:',varObj.omimGeneFound,'varFound:',varObj.omimVarFound )
]
# get clinvar: 0.1s
return {
# print('\nReading clinVar')
"omimVarFound": omimRet[0],
clinVarRet = getClinVarUsingMarrvelFlatFile(
"omimGeneFound": omimRet[1],
varObj, clinvarAlleleDf, clinvarGeneDf
"omimDict": omimRet[2],
"omimGeneDict": omimRet[3],
"omimAlleleDict": omimRet[4],
"phenoList": omimRet[5],
"phenoInhList": omimRet[6],
"phenoMimList": omimRet[7],
}
def getAnnotateInfoRow_3_5(
varObj,
clinvarGeneDf,
clinvarAlleleDf,
):
clinVarRet = getClinVarUsingMarrvelFlatFile(
varObj, clinvarAlleleDf, clinvarGeneDf
)
clinVarRet[10] = varObj.clinvar_clnsig # clinVarRet[10] #CL: changed to clinvar.vcf.gz annotation
return {
"clinVarVarFound": clinVarRet[0],
"clinVarVarDict": clinVarRet[1],
"clinVarGeneFound": clinVarRet[2],
"clinVarGeneDict": clinVarRet[3],
"clinvarTotalNumVars": clinVarRet[4],
"clinvarNumP": clinVarRet[5],
"clinvarNumLP": clinVarRet[6],
"clinvarNumLB": clinVarRet[7],
"clinvarNumB": clinVarRet[8],
"clinvarTitle": clinVarRet[9],
"clinvarSignDesc": clinVarRet[10],
"clinvarCondition": clinVarRet[11],
}
def getAnnotateInfoRow_3_6(
varObj,
hgmdHPOScoreDf,
):
hgmdRet = getHGMDUsingFlatFile(varObj, hgmdHPOScoreDf)
return {
"hgmdVarFound": hgmdRet[0],
"hgmdGeneFound": hgmdRet[1],
"hgmdVarPhenIdList": hgmdRet[2],
"hgmdVarHPOIdList": hgmdRet[3],
"hgmdVarHPOStrList": hgmdRet[4],
}
def getAnnotateInfoRows_3(
vepDf,
genomeRef,
clinvarGeneDf,
clinvarAlleleDf,
omimGeneSortedDf,
omimAlleleList,
hgmdHPOScoreDf,
moduleList,
decipherSortedDf,
gnomadMetricsGeneSortedDf,
):
def f1(row):
return getAnnotateInfoRow_3_1(row, genomeRef)
def f2(row):
if "curate" not in moduleList:
return row
return getAnnotateInfoRow_3_2(row, decipherSortedDf)
def f3(row):
if "conserve" not in moduleList:
return row
return getAnnotateInfoRow_3_3(row, gnomadMetricsGeneSortedDf)
def f4(row):
if "curate" not in moduleList:
return row
return getAnnotateInfoRow_3_4(row, omimGeneSortedDf)
def f5(row):
if "curate" not in moduleList:
return row
return getAnnotateInfoRow_3_5(row, clinvarGeneDf, clinvarAlleleDf)
def f6(row):
if "curate" not in moduleList:
return row
return getAnnotateInfoRow_3_6(
row, hgmdHPOScoreDf
)
)
varObj.clinVarVarFound = clinVarRet[0]
varObj.clinVarVarDict = clinVarRet[1]
varObj.clinVarGeneFound = clinVarRet[2]
varObj.clinVarGeneDict = clinVarRet[3]
varObj.clinvarTotalNumVars = clinVarRet[4]
varObj.clinvarNumP = clinVarRet[5]
varObj.clinvarNumLP = clinVarRet[6]
varObj.clinvarNumLB = clinVarRet[7]
varObj.clinvarNumB = clinVarRet[8]
varObj.clinvarTitle = clinVarRet[9]
varObj.clinvarSignDesc = (
row.clinvar_CLNSIG
) # clinVarRet[10] #CL: changed to clinvar.vcf.gz annotation
varObj.clinvarCondition = clinVarRet[11]
# print('clinVar res:')
"""
if debugFlag==1:
print('\tgeneFound::',varObj.clinVarGeneFound,'varFound:',varObj.clinVarVarFound)
print('\tnumVars:',varObj.clinvarTotalNumVars,'numPathologic:',varObj.clinvarNumP,'numBenign:',varObj.clinvarNumB)
print('\tsignDesc:', varObj.clinvarSignDesc)
"""
# get HGMD: 0.3s
if "curate" in moduleList:
# print('\nReading HGMD')
hgmdRet = getHGMDUsingFlatFile(varObj, hgmdDf)
# hgmdVarFound,hgmdGeneFound,hgmdVarPhenIdList,hgmdVarHPOIdList,hgmdVarHPOStrList
varObj.hgmdVarFound = hgmdRet[0]
varObj.hgmdGeneFound = hgmdRet[1]
varObj.hgmdVarPhenIdList = hgmdRet[2]
varObj.hgmdVarHPOIdList = hgmdRet[3]
varObj.hgmdVarHPOStrList = hgmdRet[4]
# print('HGMD results:')
# print('\thgmdVarFound:',varObj.hgmdVarFound,'hgmdGeneFound:',varObj.hgmdGeneFound,
# 'hgmdVarPhenIdList:',varObj.hgmdVarPhenIdList,'hgmdVarHPOIdList:',
# varObj.hgmdVarHPOIdList,
# 'hgmdVarHPOStrList:',varObj.hgmdVarHPOStrList)
return {
"hg19Chrom": varObj.hg19Chrom,
"hg19Pos": varObj.hg19Pos,
"chrom": varObj.chrom,
"pos": varObj.pos,
"start": varObj.start,
"stop": varObj.stop,
"geneSymbol": varObj.geneSymbol,
"CADD_phred": varObj.CADD_phred,
"CADD_PHRED": varObj.CADD_PHRED,
"ref": varObj.ref,
"alt": varObj.alt,
"varId": varObj.varId,
"ZYG": varObj.zyg,
"HGVSc": varObj.HGVSc,
"HGVSp": varObj.HGVSp,
"Gene": varObj.geneEnsId,
"Existing_variation": varObj.rsId,
"GERPpp_RS": varObj.GERPpp_RS,
"Feature_type": varObj.featureType,
"gnomadAF": varObj.gnomadAF,
"gnomadAFg": varObj.gnomadAFg,
"CLIN_SIG": varObj.CLIN_SIG,
"LRT_Omega": varObj.LRT_Omega,
"LRT_score": varObj.LRT_score,
"phyloP100way_vertebrate": varObj.phyloP100way_vertebrate,
# dbnsfp attributes
"GERPpp_NR": varObj.GERPpp_NR,
"DANN_score": varObj.DANN_score,
"FATHMM_pred": varObj.FATHMM_pred,
"FATHMM_score": varObj.FATHMM_score,
"GTEx_V8_gene": varObj.GTEx_V8_gene,
"GTEx_V8_tissue": varObj.GTEx_V8_tissue,
"Polyphen2_HDIV_score": varObj.Polyphen2_HDIV_score,
"Polyphen2_HVAR_score": varObj.Polyphen2_HVAR_score,
"REVEL_score": varObj.REVEL_score,
"SIFT_score": varObj.SIFT_score,
"clinvar_AlleleID": varObj.clinvar_AlleleID, # Clinvar allele ID from clinvar.vcf.gz
"clinvar_clnsig": varObj.clinvar_clnsig, # CL: Clinvar SIG from clinvar.vcf.gz
"clinvar_CLNREVSTAT": varObj.clinvar_CLNREVSTAT, # CL: Clinvar STAT from clinvar.vcf.gz, for interface only
"clinvar_CLNSIGCONF": varObj.clinvar_CLNSIGCONF, # CL: Clinvar SIGCONF from clinvar.vcf.gz
"clin_code": varObj.clin_code, # CL: feature for ai
"fathmm_MKL_coding_score": varObj.fathmm_MKL_coding_score,
"LRT_score": varObj.LRT_score,
"LRT_Omega": varObj.LRT_Omega,
"phyloP100way_vertebrate": varObj.phyloP100way_vertebrate,
"M_CAP_score": varObj.M_CAP_score,
"MutationAssessor_score": varObj.MutationAssessor_score,
"MutationTaster_score": varObj.MutationTaster_score,
"ESP6500_AA_AC": varObj.ESP6500_AA_AC,
"ESP6500_AA_AF": varObj.ESP6500_AA_AF,
"ESP6500_EA_AC": varObj.ESP6500_EA_AC,
"ESP6500_EA_AF": varObj.ESP6500_EA_AF,
# dbnsfp
"gnomadGeneZscore": varObj.gnomadGeneZscore,
"gnomadGenePLI": varObj.gnomadGenePLI,
"gnomadGeneOELof": varObj.gnomadGeneOELof, # O/E lof
"gnomadGeneOELofUpper": varObj.gnomadGeneOELofUpper, # O/E lof upper,
"IMPACT": varObj.IMPACT,
"Consequence": varObj.Consequence,
"omimVarFound": varObj.omimVarFound,
"omimGeneFound": varObj.omimGeneFound,
"omimDict": varObj.omimDict,
"omimGeneDict": varObj.omimGeneDict,
"omimAlleleDict": varObj.omimAlleleDict,
"phenoList": varObj.phenoList,
"phenoInhList": varObj.phenoInhList,
"phenoMimList": varObj.phenoMimList,
"clinVarVarFound": varObj.clinVarVarFound,
"clinVarVarDict": varObj.clinVarVarDict,
"clinVarGeneFound": varObj.clinVarGeneFound,
"clinVarGeneDict": varObj.clinVarGeneDict,
"clinvarTotalNumVars": varObj.clinvarTotalNumVars,
"clinvarNumP": varObj.clinvarNumP,
"clinvarNumLP": varObj.clinvarNumLP,
"clinvarNumLB": varObj.clinvarNumLB,
"clinvarNumB": varObj.clinvarNumB,
"clinvarTitle": varObj.clinvarTitle,
"clinvarSignDesc": varObj.clinvarSignDesc,
"clinvarCondition": varObj.clinvarCondition,
"hgmdVarFound": varObj.hgmdVarFound,
"hgmdGeneFound": varObj.hgmdGeneFound,
"hgmdVarPhenIdList": varObj.hgmdVarPhenIdList,
"hgmdVarHPOIdList": varObj.hgmdVarHPOIdList,
"hgmdVarHPOStrList": varObj.hgmdVarHPOStrList,
"varId_dash": varObj.varId_dash,
"dgvDictList": varObj.dgvDictList,
"dgvTypeList": varObj.dgvTypeList,
"dgvSubtypeList": varObj.dgvSubtypeList,
"dgvVarFound": varObj.dgvVarFound,
"decipherDictList": varObj.decipherDictList,
"decipherDeletionObsList": varObj.decipherDeletionObsList,
"decipherStudyList": varObj.decipherStudyList,
"decipherVarFound": varObj.decipherVarFound,
"gnomadGeneZscore": varObj.gnomadGeneZscore,
"gnomadGenePLI": varObj.gnomadGenePLI,
"gnomadGeneOELof": varObj.gnomadGeneOELof,
"gnomadGeneOELofUpper": varObj.gnomadGeneOELofUpper,
# symptom
"SymptomMatched": varObj.SymptomMatched,
"symptomScore": varObj.symptomScore,
"symptomName": varObj.symptomName,
"omimSymptomSimScore": varObj.omimSymptomSimScore,
"omimSymMatchFlag": varObj.omimSymMatchFlag,
"hgmdSymptomScore": varObj.hgmdSymptomScore,
"hgmdSymptomSimScore": varObj.hgmdSymptomSimScore,
"hgmdSymMatchFlag": varObj.hgmdSymMatchFlag,
"clinVarSymMatchFlag": varObj.clinVarSymMatchFlag,
"VARIANT_CLASS": varObj.VARIANT_CLASS,
"Feature": varObj.Feature,
"hom": varObj.hom,
"hgmd_rs": varObj.hgmd_rs,
"hgmd_id": varObj.hgmd_id, # CL added
"hgmd_symbol": varObj.hgmd_symbol, # CL added
"hgmd_PHEN": varObj.hgmd_PHEN, # CL added
"hgmd_CLASS": varObj.hgmd_CLASS, # CL added
"clin_dict": varObj.clin_dict,
"clin_PLP": varObj.clin_PLP,
"clin_PLP_perc": varObj.clin_PLP_perc,
"spliceAI": varObj.spliceAI,
"spliceAImax": varObj.spliceAImax,
"zyg": varObj.zyg,
annotateInfoDf = vepDf.apply(f1, axis=1, result_type='expand')
'geneEnsId': varObj.geneEnsId,
df = annotateInfoDf.apply(f2, axis=1, result_type='expand')
'rsId': varObj.rsId
annotateInfoDf[df.columns] = df
}
df = annotateInfoDf.apply(f3, axis=1, result_type='expand')
annotateInfoDf[df.columns] = df
df = annotateInfoDf.apply(f4, axis=1, result_type='expand')
annotateInfoDf[df.columns] = df
df = annotateInfoDf.apply(f5, axis=1, result_type='expand')
annotateInfoDf[df.columns] = df
df = annotateInfoDf.apply(f6, axis=1, result_type='expand')
annotateInfoDf[df.columns] = df
return annotateInfoDf