A common use case for the VEP is as a first step towards identifying the causal genetic variant of a rare phenotype from whole genome/exome sequencing. The VEP tells you which genes are hit, what effects they have on them, and you have to begin the long laborious process of filtering those down. Things you might consider include allele frequency, association with genes known to be involved in rare disease and whether both genes in a diploid organism are affected. Rather than faffing about doing this manually, you can use the G2P (genotype to phenotype) plugin instead, which was recently published as a preprint.

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Trixie the Triceratops

Ensembl produce high quality gene annotation for a number of species, but getting it to the high quality we expect takes time. This means there are many species and strains where we don’t have annotation yet. If you’re working with a species without Ensembl annotation (like Trixie the Triceratops here) or even a specific strain that we don’t have, you can still make use of VEP for predicting the effect of variants on genes and transcripts, using your own annotation. All you need is a GFF or GTF of the transcripts, and a FASTA file of the genome.

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One of the biggest headaches when working with insertions and deletions is how many different ways you can represent the same variant. If you’re looking to find out if there’s already known allele frequencies or phenotypes at a locus, you want to make sure that you find the right one. The VEP can take that headache away through normalisation of variants.

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Some Variant Effect Predictor (VEP) jobs are small, just ten or fewer variants, and that’s easy. Some VEP jobs are big, if you do variant calling on one whole human genome, that’s five million variants! The more variants you have, the more computing power the VEP needs to process them, which can make it slow. But there are ways to speed it up.

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