VCF file will be having seven fixed columns and INFO column. Chromosome, position, ID, ref, alt, qual, filter, and INFO column. This INFO column will be having the variant related information. In the INFO column CSQ field will be having multiple fields - 82 fields fixed with the delimeter "|" (piped). If no related information is present in that field. the pipe field will be empty. Actually, for each variant the vcf file annotations provides multiple multiple transcripts information of that variant. So information of this CSQ field will be more than 82 fields.
##INFO=ID=CSQ,Type=String,Description="Consequence annotations.
Format: Allele|Consequence|IMPACT|SYMBOL|Gene|Feature_type|Feature|BIOTYPE|EXON|INTRON|HGVSc|HGVSp|cDNA_position|CDS_position|Protein_position|Amino_acids|Codons|Existing_variation|DISTANCE|STRAND|FLAGS|VARIANT_CLASS|SYMBOL_SOURCE|HGNC_ID|CANONICAL|MANE_SELECT|MANE_PLUS_CLINICAL|TSL|APPRIS|CCDS|ENSP|SWISSPROT|TREMBL|UNIPARC|UNIPROT_ISOFORM|SOURCE|GENE_PHENO|SIFT|PolyPhen|DOMAINS|miRNA|HGVS_OFFSET|AF|AFR_AF|AMR_AF|EAS_AF|EUR_AF|SAS_AF|gnomADe_AF|gnomADe_AFR_AF|gnomADe_AMR_AF|gnomADe_ASJ_AF|gnomADe_EAS_AF|gnomADe_FIN_AF|gnomADe_NFE_AF|gnomADe_OTH_AF|gnomADe_SAS_AF|gnomADg_AF|gnomADg_AFR_AF|gnomADg_AMI_AF|gnomADg_AMR_AF|gnomADg_ASJ_AF|gnomADg_EAS_AF|gnomADg_FIN_AF|gnomADg_MID_AF|gnomADg_NFE_AF|gnomADg_OTH_AF|gnomADg_SAS_AF|MAX_AF|MAX_AF_POPS|CLIN_SIG|SOMATIC|PHENO|PUBMED|MOTIF_NAME|MOTIF_POS|HIGH_INF_POS|MOTIF_SCORE_CHANGE|TRANSCRIPTION_FACTORS|ClinVar|ClinVar_CLNSIG|ClinVar_CLNREVSTAT|ClinVar_CLNDN"
Splitting should be done accordingly to the CSQ fields. Add these headers information to the respective values of CSQ.
Example of VCF file:
chr1 65636536 rs145651189 A T . . RS=145651189;dbSNPBuildID=134;SSR=0;GENEINFO=LEPR:3953;VC=SNV;NSM;R3;GNO;FREQ=1000Genomes:0.9986,0.001405|ALSPAC:1,0|ExAC:0.9982,0.001845|GnomAD:0.9998,0.0001996|GnomAD_exomes:0.9984,0.001601|GoESP:0.9996,0.0003844|Korea1K:0.9995,0.0005459|MGP:0.9981,0.001873|Qatari:0.9861,0.01389|SGDP_PRJ:0.5,0.5|TOPMED:0.9997,0.0002645|TWINSUK:0.9995,0.0005394|dbGaP_PopFreq:0.9996,0.0004466;CLNVI=.,Illumina_Laboratory_Services\x2cIllumina:294897|Personalized_Diabetes_Medicine_Program\x2cUniversity_of_Maryland_School_of_Medicine:PDMP1158;CLNORIGIN=.,0|1;CLNSIG=.,0|0|3|15|2;CLNDISDB=.,OMIM:614963/MONDO:MONDO:0013992/MedGen:C3554225/OMIM:614963|MedGen:CN239457|MONDO:MONDO:0015967/MedGen:C3888631|MedGen:CN517202|MedGen:CN169374;CLNDN=.,Obesity_due_to_leptin_receptor_gene_deficiency|Monogenic_Non-Syndromic_Obesity|Monogenic_diabetes|not_provided|not_specified;CLNREVSTAT=.,single|single|single|mult|no_criteria;CLNACC=.,RCV000348481.3|RCV000405881.3|RCV000664073.2|RCV001699347.6|RCV001702416.1;CLNHGVS=NC_000001.11:g.65636536=,NC_000001.11:g.65636536A>T;CSQ=T|missense_variant|MODERATE|LEPR|ENSG00000116678|Transcript|ENST00000349533|protein_coding|20/20||ENST00000349533.11:c.3019A>T|ENSP00000330393.7:p.Ser1007Cys|3188/8211|3019/3498|1007/1165|S/C|Agt/Tgt|rs145651189|1||1||1|SNV|HGNC|HGNC:6554|YES|1|CCDS631.1|ENSP00000330393|P48357.215||UPI000014C37B|P48357-1|NM_002303.6|1|deleterious(0.05)|benign(0.039)|PANTHER:PTHR23036:SF109&PANTHER:PTHR23036||0.0018|0|0|0|0.003|0.0061|0|0.0005814|0.001601|0.0001235|0.000811|0.001791|0|0|0.0003715|0.002132|0.009737|likely_benign&uncertain_significance||1||||||,T|downstream_gene_variant|MODIFIER|LEPR|ENSG00000116678|Transcript|ENST00000371060|protein_coding||||||||||rs145651189|1|1108|1|||SNV|HGNC|HGNC:6554||1|CCDS30740.1|ENSP00000360099|P48357.215||UPI000002AD2A|P48357-2|NM_001003679.3|1|||||0.0018|0|0|0|0.003|0.0061|0|0.0005814|0.001601|0.0001235|0.000811|0.001791|0|0|0.0003715|0.002132|0.009737|likely_benign&uncertain_significance||1||||||,T|downstream_gene_variant|MODIFIER|LEPR|ENSG00000116678|Transcript|ENST00000616738|protein_coding||||||||||rs145651189|1|1127|1|||SNV|HGNC|HGNC:6554||1|CCDS30740.1|ENSP00000483390|P48357.215||UPI000002AD2A|P48357-2|NM_001198689.2|1|||||0.0018|0|0|0|0.003|0.0061|0|0.0005814|0.001601|0.0001235|0.000811|0.001791|0|0|0.0003715|0.002132|0.009737|likely_benign&uncertain_significance||1||||||
The output needed is that All this CSQ fields should be splitted into 82 fields and all the other related transcripts information should be filled in that 82 fields only seperated with "," seperated.
I tried to split using vcf2maf tool but it is considering only the 1st 82 fields of CSQ and giving output. But, I want all the other transcripts information too. And also bcftools +split-vep tool also. tried working out with ensembl vep also to seperate into tabular format.
Some programming help is much appreciated.
This is not twitter, don't use
#to add subject matter tags. There is a separate field for tags that you added just "bioinformatics" to, which makes no sense - the ENTIRE SITE is about bioinformatics. I've fixed this for you now, but please pay more attention in the future.