VarElect: the phenotype-based variation prioritizer of the GeneCards Suite. (June 2016)

• Record Type: Journal Article
• Authors: Stelzer, Gil; Plaschkes, Inbar; Oz-Levi, Danit; Alkelai, Anna; Olender, Tsviya; Zimmerman, Shahar; Twik, Michal; Belinky, Frida; Fishilevich, Simon; Nudel, Ron; Guan-Golan, Yaron; Warshawsky, David; Dahary, Dvir; Kohn, Asher; Mazor, Yaron; Kaplan, Sergey; Iny Stein, Tsippi; Baris, Hagit; Rappaport, Noa; Safran, Marilyn; Lancet, Doron
• Is Part Of: BMC genomics. Volume 17:Number 2(2016)
• Page Start: 195
• Page End: 206
• Publication Date: 2016-06
• Subjects: Variant selection -- Gene prioritization -- Phenotyping -- Phenotype interpretation -- Next generation sequencing analysis -- Guilt by association; Genomes -- Periodicals; Gene mapping -- Periodicals; Genomics -- Periodicals; Base Sequence -- Periodicals; Chromosome Mapping -- Periodicals; Genetic Techniques -- Periodicals; Sequence Analysis, DNA -- Periodicals; 572.8605
• DOI: 10.1186/s12864-016-2722-2
• Languages: English
• ISSNs: 1471-2164

Abstract

Abstract Background Next generation sequencing (NGS) provides a key technology for deciphering the genetic underpinnings of human diseases. Typical NGS analyses of a patient depict tens of thousands non-reference coding variants, but only one or very few are expected to be significant for the relevant disorder. In a filtering stage, one employs family segregation, rarity in the population, predicted protein impact and evolutionary conservation as a means for shortening the variation list. However, narrowing down further towards culprit disease genes usually entails laborious seeking of gene-phenotype relationships, consulting numerous separate databases. Thus, a major challenge is to transition from the few hundred shortlisted genes to the most viable disease-causing candidates. Results We describe a novel tool, VarElect (http://ve.genecards.org ), a comprehensive phenotype-dependent variant/gene prioritizer, based on the widely-used GeneCards, which helps rapidly identify causal mutations with extensive evidence. The GeneCards suite offers an effective and speedy alternative, whereby >120 gene-centric automatically-mined data sources are jointly available for the task. VarElect cashes on this wealth of information, as well as on GeneCards' powerful free-text Boolean search and scoring capabilities, proficiently matching variant-containing genes to submitted disease/symptom keywords. The tool also leverages the rich disease and pathway information of MalaCards, the human disease database, and PathCards, the unified pathway (SuperPaths) database, both within the GeneCards Suite. The VarElect algorithm infers direct as well as indirect links between genes and phenotypes, the latter benefitting from GeneCards' diverse gene-to-gene data links in GenesLikeMe. Finally, our tool offers an extensive gene-phenotype evidence portrayal ("MiniCards") and hyperlinks to the parent databases. Conclusions We demonstrate that VarElect compares favorably with several often-used NGS phenotyping tools, thus providing a robust facility for ranking genes, pointing out their likelihood to be related to a patient's disease. VarElect's capacity to automatically process numerous NGS cases, either in stand-alone format or in VCF-analyzer mode (TGex and VarAnnot), is indispensable for emerging clinical projects that involve thousands of whole exome/genome NGS analyses.