The Ensembl Variant Effect Predictor

By William M. McLaren, Laurent Gil, S. Hunt, H. Riat, G. Ritchie, Anja Thormann, Paul Flicek, Fiona Cunningham. 2016

7,148 citations recorded by the source corpus.

The Ensembl Variant Effect Predictor is a powerful toolset for the analysis, annotation, and prioritization of genomic variants in coding and non-coding regions. It provides access to an extensive collection of genomic annotation, with a variety of interfaces to suit different requirements, and simple options for configuring and extending analysis. It is open source, free to use, and supports full reproducibility of results. The Ensembl Variant Effect Predictor can simplify and accelerate variant interpretation in a wide range of study designs.

Subfields: Genetics, Molecular Biology

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Citations in the corpus

CADD: predicting the deleteriousness of variants throughout the human genome (2018, positive): “Annotations are obtained using Ensembl Variant Effect Predictor (VEP (15)), conservation and selection scores (e.”
LFMM 2: Fast and Accurate Inference of Gene-Environment Associations in Genome-Wide Studies (2019, methodology): “The results obtained from clumps with an expected FDR level of 1% were analyzed using the variant effect predictor (VEP) program (McLaren et al. 2016).”
Pathogenic germline variants in 10,389 adult cancers (2018, methodology): “We used evidence from SIFT (Kumar et al., 2009) and PolyPhen (Adzhubei et al., 2010), as annotated by VEP (McLaren et al., 2016).”
Multi-Ethnic Genome-wide Association Study for Atrial Fibrillation (2018, methodology): “7 using RefSeq as gene reference and the option "pick" to identify one consequence per variant with the default pick order.(55) We queried sentinel variants and their proxies to identify tagged variants with HIGH and MODERATE impact including the following”
The Polygenic and Monogenic Basis of Blood Traits and Diseases (2020, methodology): “We assigned sentinels to genes using a stringent variant effect predictor (VEP) worst-consequence annotation (McLaren et al., 2016) to obtain a distribution of functional categories.”
Open Targets Genetics: systematic identification of trait-associated genes using large-scale genetics and functional genomics (2020, methodology): “This analysis combines four different data types; molecular phenotype quantitative trait loci datasets (eQTLs and pQTLs), chromatin interaction and conformation datasets, in silico functional predictions (using the Variant Effect Predictor or VEP score), and distance from the canonical transcript st”
High-coverage whole-genome sequencing of the expanded 1000 Genomes Project cohort including 602 trios (2021, positive): “To assess functional consequences of SNVs and INDELs in the high coverage call set, we annotated all variant loci using the Ensembl Variant Effect Predictor (VEP) v104 tool (McLaren et al., 2016).”
Ensembl 2020 (2019, background): “Improving variant interpretation and reporting We have continued to enhance Ensembl VEP with a number of extensions.”
Demographic History and Genetic Adaptation in the Himalayan Region Inferred from Genome-Wide SNP Genotypes of 49 Populations (2018, methodology): “Finally, we used the Ensembl Variant Effect Predictor (VEP) (McLaren et al. 2016) to predict the consequences of variants of interest on gene expression and protein sequence.”
Allelic Landscape of Human Blood Cell Trait Variation and Links (2016, methodology): “(D and E) Violin plots showing the distribution of the absolute value of the estimated effect size stratified by VEP impact categories (D) or cell-matched chromatin segmentation states (E). p values correspond to Mann-Whitney-Wilcoxon tests comparing the distributions indicated.”