CADD: predicting the deleteriousness of variants throughout the human genome.
CADD: predicting the deleteriousness of variants throughout the human genome.
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DOI：
10.1093/nar/gky1016
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发表时间：
2019-01-08
影响因子：
14.9
通讯作者：
Kircher M
中科院分区：
文献类型：
--
作者：
Rentzsch P;Witten D;Cooper GM;Shendure J;Kircher M
联合注释依赖性耗竭（CADD）是一种广泛使用的变异敏感性指标，可以有效地在遗传分析中优先考虑因果变异，特别是严重孟德尔疾病的高度外显贡献者。CADD是由60多个基因组特征构建的综合注释，可以对参考组件中任何地方的人类单核苷酸变体和短插入和缺失进行评分。CADD使用了一个机器学习模型，该模型在模拟的从头变异和自从人类和黑猩猩分裂以来在人类群体中出现并固定的变异之间进行了二元区分;前者没有选择压力，因此可能包括中性和有害等位基因，而后者绝大多数是中性的（或者，最多是微弱有害的），因为它们在数百万年的净化选择中幸存下来。在这里，我们回顾了CADD的最新更新，包括最新版本1.4，它支持人类基因组构建GRCh 38。我们还对我们的网站进行了更新，包括简化的变体查找，扩展的文档，应用程序接口和改进的机制，用于将CADD分数集成到其他工具或应用程序中。CADD分数、软件和文档可在https://cadd.gs.washington.edu上获得。
Combined Annotation-Dependent Depletion (CADD) is a widely used measure of variant deleteriousness that can effectively prioritize causal variants in genetic analyses, particularly highly penetrant contributors to severe Mendelian disorders. CADD is an integrative annotation built from more than 60 genomic features, and can score human single nucleotide variants and short insertion and deletions anywhere in the reference assembly. CADD uses a machine learning model trained on a binary distinction between simulated de novo variants and variants that have arisen and become fixed in human populations since the split between humans and chimpanzees; the former are free of selective pressure and may thus include both neutral and deleterious alleles, while the latter are overwhelmingly neutral (or, at most, weakly deleterious) by virtue of having survived millions of years of purifying selection. Here we review the latest updates to CADD, including the most recent version, 1.4, which supports the human genome build GRCh38. We also present updates to our website that include simplified variant lookup, extended documentation, an Application Program Interface and improved mechanisms for integrating CADD scores into other tools or applications. CADD scores, software and documentation are available at https://cadd.gs.washington.edu.
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影响因子：
64.8
作者：
Findlay GM;Daza RM;Martin B;Zhang MD;Leith AP;Gasperini M;Janizek JD;Huang X;Starita LM;Shendure J
通讯作者：
Shendure J
影响因子：
10.7
作者：
Arciero E;Kraaijenbrink T;Asan;Haber M;Mezzavilla M;Ayub Q;Wang W;Pingcuo Z;Yang H;Wang J;Jobling MA;van Driem G;Xue Y;de Knijff P;Tyler-Smith C
通讯作者：
Tyler-Smith C
影响因子：
3
作者：
Groß C;de Ridder D;Reinders M
通讯作者：
Reinders M
影响因子：
9.3
作者：
Gray, Vanessa E.;Hause, Ronald J.;Fowler, Douglas M.
通讯作者：
Fowler, Douglas M.
影响因子：
30.8
作者：
Kircher, Martin;Witten, Daniela M.;Jain, Preti;O'Roak, Brian J.;Cooper, Gregory M.;Shendure, Jay
通讯作者：
Shendure, Jay