Abstract
The Ensembl Variant Effect Predictor is an integrative computational platform which could provide analysis, genomic annotation, and pathogenicity predictions of genetic sequence variants lying both protein-coding and noncoding regions of the human genome. This webserver acts as a gateway to a diverse range of genomic annotations and one step platform to enter mutation data and analyze different formats of prediction outcomes. This webserver is open access and easy to use and provides reproducible results. VEP simplifies variant analysis and interpretation in diverse study settings of the human genome. This chapter describes basic navigation for VEP users and illustrates how they could use the web-based interface to analyze the single-nucleotide variants (SNVs). This includes (i) data input, (ii) pathogenicity predictions, (iii) preview of results, and (iv) downloading the results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Adzhubei I, Jordan DM, Sunyaev SR (2013) Predicting functional effect of human missense mutations using PolyPhen-2. Curr Protoc Hum Genet:1–41
Antonarakis SE, Beckmann JS (2006) Mendelian disorders deserve more attention. Nat Rev Genet 7:277–282
Apweiler R, Bairoch A, Wu CH, Barker WC, Boeckmann B, Ferro S et al (2004) UniProt: the universal protein knowledgebase. Nucleic Acids Res 32(Database issue):D115–D119. Available from: https://www.ncbi.nlm.nih.gov/pubmed/14681372
Bairoch A, Apweiler R (2000) The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000. Nucleic Acids Res 28(1):45–48. Available from: https://www.ncbi.nlm.nih.gov/pubmed/10592178
Carter H, Douville C, Stenson PD, Cooper DN, Karchin R (2013) Identifying Mendelian disease genes with the variant effect scoring tool. BMC Genomics 14(Suppl 3):S3. Available from:https://www.ncbi.nlm.nih.gov/pubmed/23819870
Choi Y, Chan AP, Craig TJ (2015) PROVEAN web server: a tool to predict the functional effect of amino acid substitutions and indels. Bioinformatics 31:2745–2747
Cirulli ET, Goldstein DB (2010) Uncovering the roles of rare variants in common disease through whole-genome sequencing. Nat Rev Genet 11:415. Available from: https://doi.org/10.1038/nrg2779
Consortium IH (2003) The international HapMap project. Nature 426:789–796
Dewey FE, Grove ME, Pan C, Goldstein BA, Bernstein JA, Chaib H et al (2014) Clinical interpretation and implications of whole-genome sequencing. JAMA 311(10):1035–1045. Available from: https://doi.org/10.1001/jama.2014.1717
Dong C, Wei P, Jian X, Gibbs R, Boerwinkle E, Wang K et al (2015) Comparison and integration of deleteriousness prediction methods for nonsynonymous SNVs in whole exome sequencing studies. Hum Mol Genet 24(8):2125–2137. Available from: https://www.ncbi.nlm.nih.gov/pubmed/25552646
Fadista J, Oskolkov N, Hansson O, Groop L (2017) LoFtool: a gene intolerance score based on loss-of-function variants in 60 706 individuals. Bioinformatics 33(4):471–474. Available from: https://doi.org/10.1093/bioinformatics/btv602
Finn RD, Bateman A, Clements J, Coggill P, Eberhardt Y, Eddy SR et al (2014) Pfam : the protein families database. Nucleic Acids Res 42:222–230
Glanzmann B, Herbst H, Kinnear CJ, Möller M, Gamieldien J, Bardien S (2016) A new tool for prioritization of sequence variants from whole exome sequencing data. Source Code Biol Med 11(1):10. Available from: https://doi.org/10.1186/s13029-016-0056-8
González-Pérez A, López-Bigas N (2011) Improving the assessment of the outcome of nonsynonymous SNVs with a consensus deleteriousness score, Condel. Am J Hum Genet 88(4):440–449. Available from: https://www.ncbi.nlm.nih.gov/pubmed/21457909
Gulko B, Hubisz MJ, Gronau I, Siepel A (2015) A method for calculating probabilities of fitness consequences for point mutations across the human genome. Nat Genet 47(3):276–283. Available from: https://www.ncbi.nlm.nih.gov/pubmed/25599402
Hulo N, Bairoch A, Bulliard V, Cerutti L, De Castro E, Langendijk-Genevaux PS et al (2006) The PROSITE database. Nucleic Acids Res 34(suppl_1):D227–D230. Available from: https://doi.org/10.1093/nar/gkj063
Hunter S, Apweiler R, Attwood TK, Bairoch A, Bateman A, Binns D et al (2009) InterPro: the integrative protein signature database. Nucleic Acids Res 37(suppl_1):D211–D215. Available from: https://doi.org/10.1093/nar/gkn785
International Human Genome Sequencing Consortium (2004) Finishing the euchromatic sequence of the human genome. Nature 431(7011):931–945
Ioannidis NM, Rothstein JH, Pejaver V, Middha S, McDonnell SK, Baheti S et al (2016) REVEL: an ensemble method for predicting the pathogenicity of rare missense variants. Am J Hum Genet 99(4):877–885. Available from: https://www.ncbi.nlm.nih.gov/pubmed/27666373
Ionita-Laza I, McCallum K, Xu B, Buxbaum JD (2016) A spectral approach integrating functional genomic annotations for coding and noncoding variants. Nat Genet 48(2):214–220. Available from: https://www.ncbi.nlm.nih.gov/pubmed/26727659
Jagadeesh KA, Wenger AM, Berger MJ, Guturu H, Stenson PD, Cooper DN et al (2016) M-CAP eliminates a majority of variants of uncertain significance in clinical exomes at high sensitivity. Nat Genet 48:1581. Available from: https://doi.org/10.1038/ng.3703
Jian X, Boerwinkle E, Liu X (2014) In silico tools for splicing defect prediction: a survey from the viewpoint of end users. Genet Med 16(7):497–503. Available from: https://www.ncbi.nlm.nih.gov/pubmed/24263461
Karczewski KJ, Weisburd B, Thomas B, Solomonson M, Ruderfer DM, Kavanagh D et al (2017) The ExAC browser: displaying reference data information from over 60 000 exomes. Nucleic Acids Res 45(D1):D840–D845. Available from: https://www.ncbi.nlm.nih.gov/pubmed/27899611
Kim S, Jhong J-H, Lee J, Koo J-Y (2017) Meta-analytic support vector machine for integrating multiple omics data. BioData Min 10:2. Available from: https://www.ncbi.nlm.nih.gov/pubmed/28149325
Kircher M, Witten DM, Jain P, O’Roak BJ, Cooper GM, Shendure J (2014) A general framework for estimating the relative pathogenicity of human genetic variants. Nat Genet 46(3):310–315. Available from: https://www.ncbi.nlm.nih.gov/pubmed/24487276
Liu X, Jian X, Boerwinkle E (2013) dbNSFP v2.0: a database of human non-synonymous SNVs and their functional predictions and annotations. Hum Mutat 34(9):E2393–E2402. Available from: https://doi.org/10.1002/humu.22376
Lu Q, Hu Y, Sun J, Cheng Y, Cheung K-H, Zhao H (2015) A statistical framework to predict functional non-coding regions in the human genome through integrated analysis of annotation data. Sci Rep 5:10576. Available from: https://www.ncbi.nlm.nih.gov/pubmed/26015273
McLaren W, Gil L, Hunt SE, Riat HS, Ritchie GRS, Thormann A et al (2016) The Ensembl variant effect predictor. Genome Biol 17:1):1–1)14. Available from: https://doi.org/10.1186/s13059-016-0974-4
Münz M, Ruark E, Renwick A, Ramsay E, Clarke M, Mahamdallie S et al (2015) CSN and CAVA: variant annotation tools for rapid, robust next-generation sequencing analysis in the clinical setting. Genome Med 7(1):76. Available from: https://doi.org/10.1186/s13073-015-0195-6
Pejaver V, Urresti J, Lugo-martinez J, Kymberleigh A, Lin GN, Nam H et al (2017) MutPred2: inferring the molecular and phenotypic impact of amino acid variants, pp 1–28
Project T 1000 G, Auton A, Abecasis GR, Altshuler DM, Durbin RM, Abecasis GR et al (2015) A global reference for human genetic variation. Nature 526:68. Available from: https://doi.org/10.1038/nature15393
Quang D, Chen Y, Xie X (2015) DANN: a deep learning approach for annotating the pathogenicity of genetic variants. Bioinformatics 31(5):761–763. Available from: https://www.ncbi.nlm.nih.gov/pubmed/25338716
Reva B, Antipin Y, Sander C (2011) Predicting the functional impact of protein mutations: application to cancer genomics. Nucleic Acids Res 39(17):e118. Available from: https://doi.org/10.1093/nar/gkr407
Sachidanandam R, Weissman D, Schmidt SC, Kakol JM, Stein LD, Marth G et al (2001) A map of human genome sequence variation containing 1.42 million single nucleotide polymorphisms. Nature 409:928. Available from: https://doi.org/10.1038/35057149
Schmutz J, Wheeler J, Grimwood J, Dickson M, Yang J, Caoile C et al (2004) Quality assessment of the human genome sequence. Nature 429(6990):365–368
Schwarz JM, Cooper DN, Schuelke M, Seelow D (2014) MutationTaster2: mutation prediction for the deep-sequencing age. Nat Methods 11:361. Available from: https://doi.org/10.1038/nmeth.2890
Sharma R (2013) Birth defects in India: hidden truth, need for urgent attention. Indian J Hum Genet 19(2):125–129. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3758715&tool=pmcentrez&rendertype=abstract
Sim N, Kumar P, Hu J, Henikoff S, Schneider G, Ng PC (2012) SIFT web server : predicting effects of amino acid substitutions on proteins. Nucleic Acids Res 40:452–457
Stenson P, Ball E, Mort M, Phillips A, Shiel J, Thomas N et al (2003) Human gene mutation database (HGMD): 2003 update. Hum Mutat 21(6):577–581
Yang Y, Muzny DM, Reid JG, Bainbridge MN, Willis A, Ward PA et al (2013) Clinical whole-exome sequencing for the diagnosis of Mendelian disorders. N Engl J Med 369(16):1502–1511. Available from: https://doi.org/10.1056/NEJMoa1306555
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Khimsuriya, Y., Vaniyawala, S., Banaganapalli, B., Khan, M., Elango, R., Shaik, N.A. (2019). Finding a Needle in a Haystack: Variant Effect Predictor (VEP) Prioritizes Disease Causative Variants from Millions of Neutral Ones. In: Shaik, N., Hakeem, K., Banaganapalli, B., Elango, R. (eds) Essentials of Bioinformatics, Volume II. Springer, Cham. https://doi.org/10.1007/978-3-030-18375-2_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-18375-2_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-18374-5
Online ISBN: 978-3-030-18375-2
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)