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CADD-Splice—improving genome-wide variant effect prediction using deep learning-derived splice scores
BACKGROUND: Splicing of genomic exons into mRNAs is a critical prerequisite for the accurate synthesis of human proteins. Genetic variants impacting splicing underlie a substantial proportion of genetic disease, but are challenging to identify beyond those occurring at donor and acceptor dinucleotid...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7901104/ https://www.ncbi.nlm.nih.gov/pubmed/33618777 http://dx.doi.org/10.1186/s13073-021-00835-9 |
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author | Rentzsch, Philipp Schubach, Max Shendure, Jay Kircher, Martin |
author_facet | Rentzsch, Philipp Schubach, Max Shendure, Jay Kircher, Martin |
author_sort | Rentzsch, Philipp |
collection | PubMed |
description | BACKGROUND: Splicing of genomic exons into mRNAs is a critical prerequisite for the accurate synthesis of human proteins. Genetic variants impacting splicing underlie a substantial proportion of genetic disease, but are challenging to identify beyond those occurring at donor and acceptor dinucleotides. To address this, various methods aim to predict variant effects on splicing. Recently, deep neural networks (DNNs) have been shown to achieve better results in predicting splice variants than other strategies. METHODS: It has been unclear how best to integrate such process-specific scores into genome-wide variant effect predictors. Here, we use a recently published experimental data set to compare several machine learning methods that score variant effects on splicing. We integrate the best of those approaches into general variant effect prediction models and observe the effect on classification of known pathogenic variants. RESULTS: We integrate two specialized splicing scores into CADD (Combined Annotation Dependent Depletion; cadd.gs.washington.edu), a widely used tool for genome-wide variant effect prediction that we previously developed to weight and integrate diverse collections of genomic annotations. With this new model, CADD-Splice, we show that inclusion of splicing DNN effect scores substantially improves predictions across multiple variant categories, without compromising overall performance. CONCLUSIONS: While splice effect scores show superior performance on splice variants, specialized predictors cannot compete with other variant scores in general variant interpretation, as the latter account for nonsense and missense effects that do not alter splicing. Although only shown here for splice scores, we believe that the applied approach will generalize to other specific molecular processes, providing a path for the further improvement of genome-wide variant effect prediction. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13073-021-00835-9. |
format | Online Article Text |
id | pubmed-7901104 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-79011042021-02-23 CADD-Splice—improving genome-wide variant effect prediction using deep learning-derived splice scores Rentzsch, Philipp Schubach, Max Shendure, Jay Kircher, Martin Genome Med Research BACKGROUND: Splicing of genomic exons into mRNAs is a critical prerequisite for the accurate synthesis of human proteins. Genetic variants impacting splicing underlie a substantial proportion of genetic disease, but are challenging to identify beyond those occurring at donor and acceptor dinucleotides. To address this, various methods aim to predict variant effects on splicing. Recently, deep neural networks (DNNs) have been shown to achieve better results in predicting splice variants than other strategies. METHODS: It has been unclear how best to integrate such process-specific scores into genome-wide variant effect predictors. Here, we use a recently published experimental data set to compare several machine learning methods that score variant effects on splicing. We integrate the best of those approaches into general variant effect prediction models and observe the effect on classification of known pathogenic variants. RESULTS: We integrate two specialized splicing scores into CADD (Combined Annotation Dependent Depletion; cadd.gs.washington.edu), a widely used tool for genome-wide variant effect prediction that we previously developed to weight and integrate diverse collections of genomic annotations. With this new model, CADD-Splice, we show that inclusion of splicing DNN effect scores substantially improves predictions across multiple variant categories, without compromising overall performance. CONCLUSIONS: While splice effect scores show superior performance on splice variants, specialized predictors cannot compete with other variant scores in general variant interpretation, as the latter account for nonsense and missense effects that do not alter splicing. Although only shown here for splice scores, we believe that the applied approach will generalize to other specific molecular processes, providing a path for the further improvement of genome-wide variant effect prediction. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13073-021-00835-9. BioMed Central 2021-02-22 /pmc/articles/PMC7901104/ /pubmed/33618777 http://dx.doi.org/10.1186/s13073-021-00835-9 Text en © The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data. |
spellingShingle | Research Rentzsch, Philipp Schubach, Max Shendure, Jay Kircher, Martin CADD-Splice—improving genome-wide variant effect prediction using deep learning-derived splice scores |
title | CADD-Splice—improving genome-wide variant effect prediction using deep learning-derived splice scores |
title_full | CADD-Splice—improving genome-wide variant effect prediction using deep learning-derived splice scores |
title_fullStr | CADD-Splice—improving genome-wide variant effect prediction using deep learning-derived splice scores |
title_full_unstemmed | CADD-Splice—improving genome-wide variant effect prediction using deep learning-derived splice scores |
title_short | CADD-Splice—improving genome-wide variant effect prediction using deep learning-derived splice scores |
title_sort | cadd-splice—improving genome-wide variant effect prediction using deep learning-derived splice scores |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7901104/ https://www.ncbi.nlm.nih.gov/pubmed/33618777 http://dx.doi.org/10.1186/s13073-021-00835-9 |
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