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Using deep mutational scanning to benchmark variant effect predictors and identify disease mutations
To deal with the huge number of novel protein‐coding variants identified by genome and exome sequencing studies, many computational variant effect predictors (VEPs) have been developed. Such predictors are often trained and evaluated using different variant data sets, making a direct comparison betw...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7336272/ https://www.ncbi.nlm.nih.gov/pubmed/32627955 http://dx.doi.org/10.15252/msb.20199380 |
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author | Livesey, Benjamin J Marsh, Joseph A |
author_facet | Livesey, Benjamin J Marsh, Joseph A |
author_sort | Livesey, Benjamin J |
collection | PubMed |
description | To deal with the huge number of novel protein‐coding variants identified by genome and exome sequencing studies, many computational variant effect predictors (VEPs) have been developed. Such predictors are often trained and evaluated using different variant data sets, making a direct comparison between VEPs difficult. In this study, we use 31 previously published deep mutational scanning (DMS) experiments, which provide quantitative, independent phenotypic measurements for large numbers of single amino acid substitutions, in order to benchmark and compare 46 different VEPs. We also evaluate the ability of DMS measurements and VEPs to discriminate between pathogenic and benign missense variants. We find that DMS experiments tend to be superior to the top‐ranking predictors, demonstrating the tremendous potential of DMS for identifying novel human disease mutations. Among the VEPs, DeepSequence clearly stood out, showing both the strongest correlations with DMS data and having the best ability to predict pathogenic mutations, which is especially remarkable given that it is an unsupervised method. We further recommend SNAP2, DEOGEN2, SNPs&GO, SuSPect and REVEL based upon their performance in these analyses. |
format | Online Article Text |
id | pubmed-7336272 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-73362722020-07-08 Using deep mutational scanning to benchmark variant effect predictors and identify disease mutations Livesey, Benjamin J Marsh, Joseph A Mol Syst Biol Articles To deal with the huge number of novel protein‐coding variants identified by genome and exome sequencing studies, many computational variant effect predictors (VEPs) have been developed. Such predictors are often trained and evaluated using different variant data sets, making a direct comparison between VEPs difficult. In this study, we use 31 previously published deep mutational scanning (DMS) experiments, which provide quantitative, independent phenotypic measurements for large numbers of single amino acid substitutions, in order to benchmark and compare 46 different VEPs. We also evaluate the ability of DMS measurements and VEPs to discriminate between pathogenic and benign missense variants. We find that DMS experiments tend to be superior to the top‐ranking predictors, demonstrating the tremendous potential of DMS for identifying novel human disease mutations. Among the VEPs, DeepSequence clearly stood out, showing both the strongest correlations with DMS data and having the best ability to predict pathogenic mutations, which is especially remarkable given that it is an unsupervised method. We further recommend SNAP2, DEOGEN2, SNPs&GO, SuSPect and REVEL based upon their performance in these analyses. John Wiley and Sons Inc. 2020-07-06 /pmc/articles/PMC7336272/ /pubmed/32627955 http://dx.doi.org/10.15252/msb.20199380 Text en © 2020 The Authors. Published under the terms of the CC BY 4.0 license This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Articles Livesey, Benjamin J Marsh, Joseph A Using deep mutational scanning to benchmark variant effect predictors and identify disease mutations |
title | Using deep mutational scanning to benchmark variant effect predictors and identify disease mutations |
title_full | Using deep mutational scanning to benchmark variant effect predictors and identify disease mutations |
title_fullStr | Using deep mutational scanning to benchmark variant effect predictors and identify disease mutations |
title_full_unstemmed | Using deep mutational scanning to benchmark variant effect predictors and identify disease mutations |
title_short | Using deep mutational scanning to benchmark variant effect predictors and identify disease mutations |
title_sort | using deep mutational scanning to benchmark variant effect predictors and identify disease mutations |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7336272/ https://www.ncbi.nlm.nih.gov/pubmed/32627955 http://dx.doi.org/10.15252/msb.20199380 |
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