Finding genes/proteins from variant files
After getting a variant file and using a software like VEP or snpEff to annotate variants, I want to look up ensembl for validating the presence of frameshift variants that may be contributing towards a phenotype. Instead of studying each predicted variant manually, by checking if a locus has a gene and the variant is within the gene, do I have a better option of generating a file containing the gene/protein information for each of the entries in my vcf or annotation file? How can I do this??
• 1,630 views
•
link
written
by
deepti1rao •
0 answers
No answers yet.
Log in to answer this question.
More posts like this
-
VEP classifies exonic variants as downstream
written by tvrlisml •
-
Locally annotating SNP IDs and Gene names of called variants
written by entropy
-
Variant calling in WES vs WGS
written by fifty_fifty •
-
Filtering data from snpEFF annotated file
written by Peerzada •
-
Annotating variants using latest version fof the genome
written by prasundutta87
-
Discordance HGVS name of variant between annovar annotation and ClinVar
written by hudahui •
-
Annotating varscan2 output
written by ar.satkhol18 •
-
intersection of multiple VCF files
written by JoeDoasi •
-
Annotation of genomic variants
written by onemoreuser •
-
annotate DNA missense variants with protein sequence context
written by Irsan
VEP and snpEff both will give affected gene and protein information and also predicted clinical significance.
With "clinical significance" do you mean the amino acid change (in case of CDS mutations) or some statistical significance calculation?
Words to be high lighted are: predicted clinical significance, not clinical significance. Predicted clinical significance from polyphen and sift. Refer to Protein function predictions section in http://grch37.ensembl.org/info/genome/variation/predicted_data.html. Something like this:
cpad's comments are good but, just to be aware, there are in fact >60 different in silico prediction tools out there, many that can now provide functional significance for non-coding variants: A: pathogenicity predictors of cancer mutations