Predicting variant deleteriousness in non-human species: applying the CADD approach in mouse.
Predicting variant deleteriousness in non-human species: applying the CADD approach in mouse.
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DOI：
10.1186/s12859-018-2337-5
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发表时间：
2018-10-12
影响因子：
3
通讯作者：
Reinders M
中科院分区：
文献类型：
--
作者：
Groß C;de Ridder D;Reinders M
随着联合注释依赖耗竭(CADD)方法的引入，预测所观察到的基因组变异的有害程度向前迈进了一步，该方法根据丰富的可用人类基因组信息来训练分类器。这引发了一个问题，那就是是否可以用更少的非人类物种数据来完成这项工作。在这里，我们调查了为非人类物种构建基于CADD的模型的先决条件。小鼠模型的性能与人类CADD模型相当，也好于PhastCons、保护分数和SIFT等已有的方法。就像人类的情况一样，不同的基因组区域的表现有所不同，最适合编码区域。我们还展示了生成物种特定模型的好处，而不是将变体提升到不同的物种或应用通用模型。在基因组注释较少的情况下，测试集和三个验证集上的性能仍然很好。即使在表观遗传标记等注释不可用的情况下，构建特定物种的CADD模型也是可行的。对这些模型的最低要求是获得一组密切相关物种的基因组，这些基因组可以用来推断祖先基因组和数据生成的替换率。本文的在线版本(10.1186/s12859-0182337-5)包含补充材料，可供授权用户使用。
Predicting the deleteriousness of observed genomic variants has taken a step forward with the introduction of the Combined Annotation Dependent Depletion (CADD) approach, which trains a classifier on the wealth of available human genomic information. This raises the question whether it can be done with less data for non-human species. Here, we investigate the prerequisites to construct a CADD-based model for a non-human species. Performance of the mouse model is competitive with that of the human CADD model and better than established methods like PhastCons conservation scores and SIFT. Like in the human case, performance varies for different genomic regions and is best for coding regions. We also show the benefits of generating a species-specific model over lifting variants to a different species or applying a generic model. With fewer genomic annotations, performance on the test set as well as on the three validation sets is still good. It is feasible to construct species-specific CADD models even when annotations such as epigenetic markers are not available. The minimal requirement for these models is the availability of a set of genomes of closely related species that can be used to infer an ancestor genome and substitution rates for the data generation. The online version of this article (10.1186/s12859-018-2337-5) contains supplementary material, which is available to authorized users.
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影响因子：
12.3
作者：
McLaren W;Gil L;Hunt SE;Riat HS;Ritchie GR;Thormann A;Flicek P;Cunningham F
通讯作者：
Cunningham F
影响因子：
8.8
作者：
Mather, Cheryl A.;Mooney, Sean D.;Shirts, Brian H.
通讯作者：
Shirts, Brian H.
影响因子：
9.8
作者：
Church DM;Goodstadt L;Hillier LW;Zody MC;Goldstein S;She X;Bult CJ;Agarwala R;Cherry JL;DiCuccio M;Hlavina W;Kapustin Y;Meric P;Maglott D;Birtle Z;Marques AC;Graves T;Zhou S;Teague B;Potamousis K;Churas C;Place M;Herschleb J;Runnheim R;Forrest D;Amos-Landgraf J;Schwartz DC;Cheng Z;Lindblad-Toh K;Eichler EE;Ponting CP;Mouse Genome Sequencing Consortium
通讯作者：
Mouse Genome Sequencing Consortium
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
影响因子：
7
作者：
Ng, PC;Henikoff, S
通讯作者：
Henikoff, S