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VarElect:基因卡片套件中基于表型的变异优先级排序工具。

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

作者信息

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 N, Rappaport Noa, Safran Marilyn, Lancet Doron

机构信息

Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel.

LifeMap Sciences Ltd, Tel Aviv, Israel.

出版信息

BMC Genomics. 2016 Jun 23;17 Suppl 2(Suppl 2):444. doi: 10.1186/s12864-016-2722-2.

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.

摘要

背景

新一代测序(NGS)为解读人类疾病的遗传基础提供了关键技术。对患者进行典型的NGS分析会描绘出数万个非参考编码变异,但预计其中只有一个或极少数对相关疾病具有重要意义。在筛选阶段,人们利用家系分离、人群中的罕见性、预测的蛋白质影响和进化保守性来缩短变异列表。然而,进一步缩小范围至致病疾病基因通常需要费力地寻找基因与表型的关系,并查阅众多独立的数据库。因此,一个主要挑战是从几百个入围基因过渡到最有可能致病的候选基因。

结果

我们描述了一种新型工具VarElect(http://ve.genecards.org),这是一种基于广泛使用的GeneCards的全面的依赖表型的变异/基因优先级排序工具,有助于快速识别具有充分证据的致病突变。GeneCards套件提供了一种有效且快速的替代方法,有超过120个以基因为中心的自动挖掘数据源可共同用于此项任务。VarElect利用了这些丰富的信息,以及GeneCards强大的自由文本布尔搜索和评分功能,将含变异的基因与提交的疾病/症状关键词进行有效匹配。该工具还利用了GeneCards套件中的人类疾病数据库MalaCards和统一通路(超级通路)数据库PathCards丰富的疾病和通路信息。VarElect算法推断基因与表型之间的直接和间接联系,后者受益于GeneCards中GenesLikeMe里多样的基因到基因的数据链接。最后,我们的工具提供了广泛的基因-表型证据描述(“MiniCards”)以及到父数据库的超链接。

结论

我们证明VarElect与几种常用的NGS表型分析工具相比具有优势,从而为基因排名提供了一个强大的工具,指出它们与患者疾病相关的可能性。VarElect以独立格式或VCF分析模式(TGex和VarAnnot)自动处理大量NGS病例的能力,对于涉及数千次全外显子组/基因组NGS分析的新兴临床项目来说是不可或缺的。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/14fa/4928145/a85e2c595b2d/12864_2016_2722_Fig1_HTML.jpg

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