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MetaRNN: differentiating rare pathogenic and rare benign missense SNVs and InDels using deep learning

Multiple computational approaches have been developed to improve our understanding of genetic variants. However, their ability to identify rare pathogenic variants from rare benign ones is still lacking. Using context annotations and deep learning methods, we present pathogenicity prediction models,...

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Detalles Bibliográficos
Autores principales: Li, Chang, Zhi, Degui, Wang, Kai, Liu, Xiaoming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9548151/
https://www.ncbi.nlm.nih.gov/pubmed/36209109
http://dx.doi.org/10.1186/s13073-022-01120-z
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author Li, Chang
Zhi, Degui
Wang, Kai
Liu, Xiaoming
author_facet Li, Chang
Zhi, Degui
Wang, Kai
Liu, Xiaoming
author_sort Li, Chang
collection PubMed
description Multiple computational approaches have been developed to improve our understanding of genetic variants. However, their ability to identify rare pathogenic variants from rare benign ones is still lacking. Using context annotations and deep learning methods, we present pathogenicity prediction models, MetaRNN and MetaRNN-indel, to help identify and prioritize rare nonsynonymous single nucleotide variants (nsSNVs) and non-frameshift insertion/deletions (nfINDELs). We use independent test sets to demonstrate that these new models outperform state-of-the-art competitors and achieve a more interpretable score distribution. Importantly, prediction scores from both models are comparable, enabling easy adoption of integrated genotype-phenotype association analysis methods. All pre-computed nsSNV scores are available at http://www.liulab.science/MetaRNN. The stand-alone program is also available at https://github.com/Chang-Li2019/MetaRNN. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13073-022-01120-z.
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spelling pubmed-95481512022-10-10 MetaRNN: differentiating rare pathogenic and rare benign missense SNVs and InDels using deep learning Li, Chang Zhi, Degui Wang, Kai Liu, Xiaoming Genome Med Method Multiple computational approaches have been developed to improve our understanding of genetic variants. However, their ability to identify rare pathogenic variants from rare benign ones is still lacking. Using context annotations and deep learning methods, we present pathogenicity prediction models, MetaRNN and MetaRNN-indel, to help identify and prioritize rare nonsynonymous single nucleotide variants (nsSNVs) and non-frameshift insertion/deletions (nfINDELs). We use independent test sets to demonstrate that these new models outperform state-of-the-art competitors and achieve a more interpretable score distribution. Importantly, prediction scores from both models are comparable, enabling easy adoption of integrated genotype-phenotype association analysis methods. All pre-computed nsSNV scores are available at http://www.liulab.science/MetaRNN. The stand-alone program is also available at https://github.com/Chang-Li2019/MetaRNN. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13073-022-01120-z. BioMed Central 2022-10-08 /pmc/articles/PMC9548151/ /pubmed/36209109 http://dx.doi.org/10.1186/s13073-022-01120-z Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://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 Method
Li, Chang
Zhi, Degui
Wang, Kai
Liu, Xiaoming
MetaRNN: differentiating rare pathogenic and rare benign missense SNVs and InDels using deep learning
title MetaRNN: differentiating rare pathogenic and rare benign missense SNVs and InDels using deep learning
title_full MetaRNN: differentiating rare pathogenic and rare benign missense SNVs and InDels using deep learning
title_fullStr MetaRNN: differentiating rare pathogenic and rare benign missense SNVs and InDels using deep learning
title_full_unstemmed MetaRNN: differentiating rare pathogenic and rare benign missense SNVs and InDels using deep learning
title_short MetaRNN: differentiating rare pathogenic and rare benign missense SNVs and InDels using deep learning
title_sort metarnn: differentiating rare pathogenic and rare benign missense snvs and indels using deep learning
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9548151/
https://www.ncbi.nlm.nih.gov/pubmed/36209109
http://dx.doi.org/10.1186/s13073-022-01120-z
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