The Ensembl Variant Effect Predictor

McLaren, William; Gil, Laurent; Hunt, Sarah; Riat, Harpreet Singh; Ritchie, Graham R. S.; Thormann, Anja; Flicek, Paul; Cunningham, Fiona

doi:10.1101/042374

Cited by 1,197 publications

(1,454 citation statements)

References 73 publications

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“…Data were mapped to NCBI hg19/GRCh37 human genome reference sequence using BWA, 23 and then analyzed using GATK Best Practices recommendations, 24 HaplotypeCaller, 25,26 Variant Effect Predictor, 27 and Seqr. Analysis of mtDNA was performed as described previously.…”

Section: Whole-exome Sequencingmentioning

confidence: 99%

Biallelic Mutations in MRPS34 Lead to Instability of the Small Mitoribosomal Subunit and Leigh Syndrome

Lake

Webb

Stroud

et al. 2017

The American Journal of Human Genetics

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Section: Whole-exome Sequencingmentioning

confidence: 99%

Biallelic Mutations in MRPS34 Lead to Instability of the Small Mitoribosomal Subunit and Leigh Syndrome

Lake

Webb

Stroud

et al. 2017

The American Journal of Human Genetics

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“…To increase accuracy, additional filters were applied to high-confidence calls (those passing Stelka's filters); details are given in Supplemental Table S6. Functional annotation of the resulting variants was performed using Variant Effect Predictor (VEP release 75) (McLaren et al 2016). …”

Section: Mutation Callingmentioning

confidence: 99%

A comparative analysis of whole genome sequencing of esophageal adenocarcinoma pre- and post-chemotherapy

et al. 2017

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The scientific community has avoided using tissue samples from patients that have been exposed to systemic chemotherapy to infer the genomic landscape of a given cancer. Esophageal adenocarcinoma is a heterogeneous, chemoresistant tumor for which the availability and size of pretreatment endoscopic samples are limiting. This study compares whole-genome sequencing data obtained from chemo-naive and chemo-treated samples. The quality of whole-genomic sequencing data is comparable across all samples regardless of chemotherapy status. Inclusion of samples collected post-chemotherapy increased the proportion of late-stage tumors. When comparing matched pre-and post-chemotherapy samples from 10 cases, the mutational signatures, copy number, and SNV mutational profiles reflect the expected heterogeneity in this disease. Analysis of SNVs in relation to allele-specific copy-number changes pinpoints the common ancestor to a point prior to chemotherapy. For cases in which pre-and post-chemotherapy samples do show substantial differences, the timing of the divergence is near-synchronous with endoreduplication. Comparison across a large prospective cohort (62 treatment-naive, 58 chemotherapy-treated samples) reveals no significant differences in the overall mutation rate, mutation signatures, specific recurrent point mutations, or copy-number events in respect to chemotherapy status. In conclusion, whole-genome sequencing of samples obtained following neoadjuvant chemotherapy is representative of the genomic landscape of esophageal adenocarcinoma. Excluding these samples reduces the material available for cataloging and introduces a bias toward the earlier stages of cancer.

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“…Blue objects are only supported by easyGWAS. the corresponding data using the Ensemble REST interface for the Variant Effect Predictor tool (Yates et al, 2014;McLaren et al, 2010McLaren et al, , 2016. Figure 2B shows different clickable tabs that allow the user to explore all the results of the experiment.…”

Section: Results Viewsmentioning

confidence: 99%

easyGWAS: A Cloud-Based Platform for Comparing the Results of Genome-Wide Association Studies

et al. 2016

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The ever-growing availability of high-quality genotypes for a multitude of species has enabled researchers to explore the underlying genetic architecture of complex phenotypes at an unprecedented level of detail using genome-wide association studies (GWAS). The systematic comparison of results obtained from GWAS of different traits opens up new possibilities, including the analysis of pleiotropic effects. Other advantages that result from the integration of multiple GWAS are the ability to replicate GWAS signals and to increase statistical power to detect such signals through meta-analyses. In order to facilitate the simple comparison of GWAS results, we present easyGWAS, a powerful, species-independent online resource for computing, storing, sharing, annotating, and comparing GWAS. The easyGWAS tool supports multiple species, the uploading of private genotype data and summary statistics of existing GWAS, as well as advanced methods for comparing GWAS results across different experiments and data sets in an interactive and user-friendly interface. easyGWAS is also a public data repository for GWAS data and summary statistics and already includes published data and results from several major GWAS. We demonstrate the potential of easyGWAS with a case study of the model organism Arabidopsis thaliana, using flowering and growth-related traits.

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The Ensembl Variant Effect Predictor

Cited by 1,197 publications

References 73 publications

Biallelic Mutations in MRPS34 Lead to Instability of the Small Mitoribosomal Subunit and Leigh Syndrome

Biallelic Mutations in MRPS34 Lead to Instability of the Small Mitoribosomal Subunit and Leigh Syndrome

A comparative analysis of whole genome sequencing of esophageal adenocarcinoma pre- and post-chemotherapy

easyGWAS: A Cloud-Based Platform for Comparing the Results of Genome-Wide Association Studies

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