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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()

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

if row[0].find("/") != -1:

optFlag = 1

if optFlag == 0:

s = row[0].split("_")

# print('s:', s)

chrom = s[0]

pos = int(s[1])

ref = s[2]

alt = s[3]

elif optFlag == 1:

s = row[0].split("_")

# print('s:', s)

chrom = s[0]

pos = int(s[1])

s = s[2].split("/")

ref = s[0]

alt = s[1]

# get the start and stop from second column like '1:10203-10204'

if "-" in row[1]:

s = row[1].split(":")

tmp = s[1]

s = tmp.split("-")

# print('s:',s)

start = int(s[0])

stop = int(s[1])

else:

# start and stop the same

s = row[1].split(":")

start = int(s[1])

stop = int(s[1])

# print('chrom:', chrom,'pos:',pos,'ref:',ref,'alt:',alt,'start:',start,'stop:',stop)

# change chrom X and Y and MT to numbers

if chrom == "X":

chrom = 23

elif chrom == "Y":

chrom = 24

elif chrom == "MT":

chrom = 25

elif re.search(r"GL", chrom):

chrom = 26

chrom = int(chrom)

# if it is hg38 get its hg19 coordinates

# CL 03-14-2023: we have separate database for hg19 and hg38,

# we don't need to use LiftOver which is inaccurate

# related codes commented and modified

if genomeRef == "hg38":

varObj.hg38Chrom = chrom

varObj.hg38Pos = pos

varObj.chrom = chrom

varObj.pos = pos

varObj.start = start

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:

varObj.hg19Chrom = chrom

varObj.hg19Pos = pos

varObj.chrom = chrom

varObj.pos = pos

varObj.start = start

varObj.stop = stop

geneSymbol = row.SYMBOL

# print('gene:', geneSymbol)

varObj.geneSymbol = geneSymbol

varObj.CADD_phred = row.CADD_phred

varObj.CADD_PHRED = row.CADD_PHRED

# assign

varObj.ref = ref

varObj.alt = alt

varObj.varId_dash = "-".join([str(chrom), str(start), ref, alt])

# print('varId dash:', varObj.varId_dash)

varId = "_".join([str(chrom), str(pos), ref, alt, transcriptId])

varObj.varId = varId

if "ZYG" in row:

varObj.zyg = row.ZYG

varObj.geneEnsId = row.Gene

varObj.rsId = row.Existing_variation

varObj.GERPpp_RS = row.GERPpp_RS

varObj.featureType = row.Feature_type

varObj.gnomadAF = row.gnomAD_AF

varObj.gnomadAFg = row.gnomADg_AF

varObj.CLIN_SIG = row.CLIN_SIG # CL: useless but kept for now

varObj.LRT_Omega = row.LRT_Omega

varObj.LRT_score = row.LRT_score

varObj.phyloP100way_vertebrate = row.phyloP100way_vertebrate

varObj.IMPACT = row.IMPACT

varObj.Consequence = row.Consequence

varObj.HGVSc = row.HGVSc

varObj.HGVSp = row.HGVSp

# dbnsfp attributes

varObj.GERPpp_NR = row.GERPpp_NR

varObj.DANN_score = row.DANN_score

varObj.FATHMM_pred = row.FATHMM_pred

varObj.FATHMM_score = row.FATHMM_score

varObj.GTEx_V8_gene = row.GTEx_V8_gene

varObj.GTEx_V8_tissue = row.GTEx_V8_tissue

varObj.Polyphen2_HDIV_score = row.Polyphen2_HDIV_score

varObj.Polyphen2_HVAR_score = row.Polyphen2_HVAR_score

varObj.REVEL_score = row.REVEL_score

varObj.SIFT_score = row.SIFT_score

varObj.clinvar_AlleleID = row.clinvar # Clinvar allele ID from clinvar.vcf.gz

varObj.clinvar_clnsig = (

row.clinvar_CLNSIG

) # CL: Clinvar SIG from clinvar.vcf.gz

# varObj.clinvar_clnsig = row.clinvar_clnsig #CL: Clinvar SIG from VEP, deleted

varObj.clinvar_CLNREVSTAT = (

row.clinvar_CLNREVSTAT

) # CL: Clinvar STAT from clinvar.vcf.gz, for interface only

varObj.clinvar_CLNSIGCONF = (

row.clinvar_CLNSIGCONF

) # CL: Clinvar SIGCONF from clinvar.vcf.gz

varObj.clin_code = row.clinvar_CLNSIG # CL: feature name for ai

varObj.fathmm_MKL_coding_score = row.fathmm_MKL_coding_score

varObj.LRT_score = row.LRT_score

varObj.LRT_Omega = row.LRT_Omega

varObj.phyloP100way_vertebrate = row.phyloP100way_vertebrate

varObj.M_CAP_score = row.M_CAP_score

varObj.MutationAssessor_score = row.MutationAssessor_score

varObj.MutationTaster_score = row.MutationTaster_score

varObj.ESP6500_AA_AC = row.ESP6500_AA_AC

varObj.ESP6500_AA_AF = row.ESP6500_AA_AF

varObj.ESP6500_EA_AC = row.ESP6500_EA_AC

varObj.ESP6500_EA_AF = row.ESP6500_EA_AF

varObj.VARIANT_CLASS = row.VARIANT_CLASS

varObj.Feature = row.Feature

varObj.hom = row.gnomADg_controls_nhomalt

varObj.hgmd_id = row.hgmd # CL added

varObj.hgmd_symbol = row.hgmd_GENE # CL added

varObj.hgmd_rs = row.hgmd_RANKSCORE

varObj.hgmd_PHEN = row.hgmd_PHEN # CL added

varObj.hgmd_CLASS = row.hgmd_CLASS # CL added

if row.clinvar_CLNSIGCONF != "-":

clin_dict = dict()

for ro in row.clinvar_CLNSIGCONF.split("|_"):

temp = ro.split("(")

clin_dict[temp[0]] = int(temp[1][0])

PLP_sum = clin_dict.get("Pathogenic", 0) + clin_dict.get(

"Likely_pathogenic", 0

)

varObj.clin_dict = clin_dict

varObj.clin_PLP = PLP_sum

varObj.clin_PLP_perc = PLP_sum / sum(clin_dict.values())

else:

if "benign" in row.clinvar_clnsig.lower():

varObj.clin_PLP_perc = 0

elif "pathogenic" in row.clinvar_clnsig.lower():

varObj.clin_PLP_perc = 1

else:

varObj.clin_PLP_perc = "-"

varObj.clin_PLP = "-"

varObj.clin_dict = "-"

if row.SpliceAI_pred != "-":

varObj.spliceAI = row.SpliceAI_pred

temp = row.SpliceAI_pred.split("|")

varObj.spliceAImax = max(

float(temp[1]), float(temp[2]), float(temp[3]), float(temp[4])

)

else:

varObj.spliceAI = "-"

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]

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:

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:

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