VarElect: the phenotype-based variation prioritizer of the GeneCards Suite

被引:193
作者
Stelzer, Gil [1 ,2 ]
Plaschkes, Inbar [2 ]
Oz-Levi, Danit [1 ]
Alkelai, Anna [1 ]
Olender, Tsviya [1 ]
Zimmerman, Shahar [1 ]
Twik, Michal [1 ]
Belinky, Frida [3 ]
Fishilevich, Simon [1 ]
Nudel, Ron [1 ]
Guan-Golan, Yaron [4 ]
Warshawsky, David [4 ]
Dahary, Dvir [2 ,5 ]
Kohn, Asher [4 ]
Mazor, Yaron [2 ]
Kaplan, Sergey [2 ]
Stein, Tsippi Iny [1 ]
Baris, Hagit N. [6 ,7 ]
Rappaport, Noa [1 ]
Safran, Marilyn [1 ]
Lancet, Doron [1 ]
机构
[1] Weizmann Inst Sci, Dept Mol Genet, Rehovot, Israel
[2] LifeMap Sci Ltd, Tel Aviv, Israel
[3] NIH, Natl Ctr Biotechnol Informat, Natl Lib Med, Bethesda, MD 20894 USA
[4] LifeMap Sci Inc, Marshfield, MA 02050 USA
[5] Toldot Genet Ltd, Hod Hasharon, Israel
[6] Rambam Hlth Care Campus, Genet Inst, Haifa, Israel
[7] Technion Israel Inst Technol, Rappaport Sch Med, Haifa, Israel
关键词
Variant selection; Gene prioritization; Phenotyping; Phenotype interpretation; Next generation sequencing analysis; Guilt by association; EXOME; GENES; IDENTIFICATION; DIAGNOSIS; GENOMICS;
D O I
10.1186/s12864-016-2722-2
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 [微生物学]; 090105 [作物生产系统与生态工程];
摘要
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.
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页数:12
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