Annotating and prioritising genomic variants using the Ensembl Variant Effect Predictor - a tutorial

The Ensembl Variant Effect Predictor (VEP) is a freely available, open source tool for the annotation and filtering of genomic variants. It predicts variant molecular consequence using the Ensembl/GENCODE or RefSeq gene sets. It also reports phenotype associations from databases such as ClinVar, allele frequencies from studies including gnomAD, and predictions of deleteriousness from tools such as SIFT and CADD. Ensembl VEP includes filtering options to customise variant prioritisation. It is well supported and updated roughly quarterly to incorporate the latest gene, variant and phenotype association information. Ensembl VEP analysis can be performed using a highly configurable, extensible command-line tool, a Representational State Transfer (REST) application programming interface (API) and a user-friendly web interface. These access methods are designed to suit different levels of bioinformatics experience and meet different needs in terms of data size, visualisation and flexibility. In this tutorial, we will describe performing variant annotation using the Ensembl VEP web tool, which enables sophisticated analysis through a simple interface.

The Ensembl COVID-19 resource: Ongoing integration of public SARS-CoV-2 data

ABSTRACTThe Ensembl COVID-19 browser (covid-19.ensembl.org) was launched in May 2020 in response to the ongoing pandemic. It is Ensembl’s contribution to the global efforts to develop treatments, diagnostics and vaccines for COVID-19, and it supports research into the genomic epidemiology and evolution of the SARS-CoV-2 virus. This freely available resource incorporates a new Ensembl gene set, multiple sets of variants, and alignments of annotation from several resources against the reference assembly for SARS-CoV-2. It represents the first virus to be encompassed within the Ensembl platform. Additional data are being continually integrated via our new rapid release protocols alongside tools such as the Ensembl Variant Effect Predictor. Here we describe the data and infrastructure behind the resource and discuss future work.

Ensembl 2021

The Ensembl project (https://www.ensembl.org) annotates genomes and disseminates genomic data for vertebrate species. We create detailed and comprehensive annotation of gene structures, regulatory elements and variants, and enable comparative genomics by inferring the evolutionary history of genes and genomes. Our integrated genomic data are made available in a variety of ways, including genome browsers, search interfaces, specialist tools such as the Ensembl Variant Effect Predictor, download files and programmatic interfaces. Here, we present recent Ensembl developments including two new website portals. Ensembl Rapid Release (http://rapid.ensembl.org) is designed to provide core tools and services for genomes as soon as possible and has been deployed to support large biodiversity sequencing projects. Our SARS-CoV-2 genome browser (https://covid-19.ensembl.org) integrates our own annotation with publicly available genomic data from numerous sources to facilitate the use of genomics in the international scientific response to the COVID-19 pandemic. We also report on other updates to our annotation resources, tools and services. All Ensembl data and software are freely available without restriction.

A plugin for the Ensembl Variant Effect Predictor that uses MaxEntScan to predict variant spliceogenicity

Summary Assessing the pathogenicity of genetic variants can be a complex and challenging task. Spliceogenic variants, which alter mRNA splicing, may yield mature transcripts that encode non-functional protein products, an important predictor of Mendelian disease risk. However, most variant annotation tools do not adequately assess spliceogenicity outside the native splice site and thus the disease-causing potential of variants in other intronic and exonic regions is often overlooked. Here, we present a plugin for the Ensembl Variant Effect Predictor that packages MaxEntScan and extends its functionality to provide splice site predictions using a maximum entropy model. The plugin incorporates a sliding window algorithm to predict splice site loss or gain for any variant that overlaps a transcript feature. We also demonstrate the utility of the plugin by comparing our predictions to two mRNA splicing datasets containing several cancer-susceptibility genes. Availability and implementation Source code is freely available under the Apache License, Version 2.0: https://github.com/Ensembl/VEP_plugins. Supplementary information Supplementary data are available at Bioinformatics online.

A variant by any name: quantifying annotation discordance across tools and clinical databases

ABSTRACTBackgroundClinical genomic testing is dependent on the robust identification and reporting of variant-level information in relation to disease. With the shift to high-throughput sequencing, a major challenge for clinical diagnostics is the cross-identification of variants called on their genomic position to resources that rely on transcript- or protein-based descriptions.MethodsWe evaluated the accuracy of three tools (SnpEff, Variant Effect Predictor and Variation Reporter) that generate transcript and protein-based variant nomenclature from genomic coordinates according to guidelines by the Human Genome Variation Society (HGVS). Our evaluation was based on comparisons to a manually curated list of 127 test variants of various types drawn from data sources, each with HGVS-compliant transcript and protein descriptors. We further evaluated the concordance between annotations generated by Snpeff and Variant Effect Predictor with those in major germline and cancer databases: ClinVar and COSMIC, respectively.ResultsWe find that there is substantial discordance between the annotation tools and databases in the description of insertion and/or deletions. Accuracy based on our ground truth set was between 80-90% for coding and 50-70% for protein variants, numbers that are not adequate for clinical reporting. Exact concordance for SNV syntax was over 99.5% between ClinVar and Variant Effect Predictor (VEP) and SnpEff, but less than 90% for non-SNV variants. For COSMIC, exact concordance for coding and protein SNVs were between 65 and 88%, and less than 15% for insertions. Across the tools and datasets, there was a wide range of equivalent expressions describing protein variants.ConclusionOur results reveal significant inconsistency in variant representation across tools and databases. These results highlight the urgent need for the adoption and adherence to uniform standards in variant annotation, with consistent reporting on the genomic reference, to enable accurate and efficient data-driven clinical care.