MMSplice: modular modeling improves the predictions of genetic variant effects on splicing

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ID: 35417
2019
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Abstract
Abstract 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.
Reference Key
cheng2019mmsplicegenome Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Cheng, Jun;Nguyen, Thi Yen Duong;Cygan, Kamil J.;Çelik, Muhammed Hasan;Fairbrother, William G.;Avsec, žiga;Gagneur, Julien;
Journal Genome biology
Year 2019
DOI
DOI not found
URL
http://link.springer.com/article/10.1186/s13059-019-1653-z
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