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

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...

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Autores principales: Cheng, Jun, Nguyen, Thi Yen Duong, Cygan, Kamil J., Çelik, Muhammed Hasan, Fairbrother, William G., Avsec, žiga, Gagneur, Julien
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6396468/
https://www.ncbi.nlm.nih.gov/pubmed/30823901
http://dx.doi.org/10.1186/s13059-019-1653-z
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author Cheng, Jun
Nguyen, Thi Yen Duong
Cygan, Kamil J.
Çelik, Muhammed Hasan
Fairbrother, William G.
Avsec, žiga
Gagneur, Julien
author_facet Cheng, Jun
Nguyen, Thi Yen Duong
Cygan, Kamil J.
Çelik, Muhammed Hasan
Fairbrother, William G.
Avsec, žiga
Gagneur, Julien
author_sort Cheng, Jun
collection PubMed
description 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13059-019-1653-z) contains supplementary material, which is available to authorized users.
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spelling pubmed-63964682019-03-13 MMSplice: modular modeling improves the predictions of genetic variant effects on splicing Cheng, Jun Nguyen, Thi Yen Duong Cygan, Kamil J. Çelik, Muhammed Hasan Fairbrother, William G. Avsec, žiga Gagneur, Julien Genome Biol Method 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13059-019-1653-z) contains supplementary material, which is available to authorized users. BioMed Central 2019-03-01 /pmc/articles/PMC6396468/ /pubmed/30823901 http://dx.doi.org/10.1186/s13059-019-1653-z Text en © The Author(s) 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Method
Cheng, Jun
Nguyen, Thi Yen Duong
Cygan, Kamil J.
Çelik, Muhammed Hasan
Fairbrother, William G.
Avsec, žiga
Gagneur, Julien
MMSplice: modular modeling improves the predictions of genetic variant effects on splicing
title MMSplice: modular modeling improves the predictions of genetic variant effects on splicing
title_full MMSplice: modular modeling improves the predictions of genetic variant effects on splicing
title_fullStr MMSplice: modular modeling improves the predictions of genetic variant effects on splicing
title_full_unstemmed MMSplice: modular modeling improves the predictions of genetic variant effects on splicing
title_short MMSplice: modular modeling improves the predictions of genetic variant effects on splicing
title_sort mmsplice: modular modeling improves the predictions of genetic variant effects on splicing
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6396468/
https://www.ncbi.nlm.nih.gov/pubmed/30823901
http://dx.doi.org/10.1186/s13059-019-1653-z
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