MMSplice: modular modeling improves the predictions of genetic variant effects on splicing
MMSplice: modular modeling improves the predictions of genetic variant effects on splicing
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DOI：
10.1186/s13059-019-1653-z
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发表时间：
2019-03-01
期刊：
影响因子：
12.3
通讯作者：
Gagneur, Julien
中科院分区：
文献类型：
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作者：
Cheng, Jun;Thi Yen Duong Nguyen;Gagneur, Julien
预测基因变异对剪接的影响与人类遗传学密切相关。我们描述了框架MMSplice（拼接的模块化建模），我们用它建立了CAGI5外显子跳跃预测挑战的获胜模型。MMSplice模块是对外显子、内含子和剪接位点进行评分的神经网络，在不同的大规模基因组学数据集上进行训练。这些模块结合起来预测变异对外显子跳变、剪接位点选择、剪接效率和致病性的影响，具有与最先进的性能相匹配或更高的性能。我们的模型可以在知识库Kipoi中获得，它直接应用于VCF文件中的变量，包括索引。
Predicting the effects of genetic variants on splicing is highly relevant for human genetics. We describe the framework MMSplice (modular modeling of splicing) with which we built the winning model of the CAGI5 exon skipping prediction challenge. The MMSplice modules are neural networks scoring exon, intron, and splice sites, trained on distinct large-scale genomics datasets. These modules are combined to predict effects of variants on exon skipping, splice site choice, splicing efficiency, and pathogenicity, with matched or higher performance than state-of-the-art. Our models, available in the repository Kipoi, apply to variants including indels directly from VCF files.