In silico methods for predicting functional synonymous variants

Single nucleotide variants (SNVs) contribute to human genomic diversity. Synonymous SNVs are previously considered to be “silent,” but mounting evidence has revealed that these variants can cause RNA and protein changes and are implicated in over 85 human diseases and cancers. Recent improvements in...

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Autores principales: Lin, Brian C., Katneni, Upendra, Jankowska, Katarzyna I., Meyer, Douglas, Kimchi-Sarfaty, Chava
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Review
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10204308/
https://www.ncbi.nlm.nih.gov/pubmed/37217943
http://dx.doi.org/10.1186/s13059-023-02966-1
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author Lin, Brian C.
Katneni, Upendra
Jankowska, Katarzyna I.
Meyer, Douglas
Kimchi-Sarfaty, Chava
author_facet Lin, Brian C.
Katneni, Upendra
Jankowska, Katarzyna I.
Meyer, Douglas
Kimchi-Sarfaty, Chava
author_sort Lin, Brian C.
collection PubMed
description Single nucleotide variants (SNVs) contribute to human genomic diversity. Synonymous SNVs are previously considered to be “silent,” but mounting evidence has revealed that these variants can cause RNA and protein changes and are implicated in over 85 human diseases and cancers. Recent improvements in computational platforms have led to the development of numerous machine-learning tools, which can be used to advance synonymous SNV research. In this review, we discuss tools that should be used to investigate synonymous variants. We provide supportive examples from seminal studies that demonstrate how these tools have driven new discoveries of functional synonymous SNVs. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-023-02966-1.
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spelling pubmed-102043082023-05-24 In silico methods for predicting functional synonymous variants Lin, Brian C. Katneni, Upendra Jankowska, Katarzyna I. Meyer, Douglas Kimchi-Sarfaty, Chava Genome Biol Review Single nucleotide variants (SNVs) contribute to human genomic diversity. Synonymous SNVs are previously considered to be “silent,” but mounting evidence has revealed that these variants can cause RNA and protein changes and are implicated in over 85 human diseases and cancers. Recent improvements in computational platforms have led to the development of numerous machine-learning tools, which can be used to advance synonymous SNV research. In this review, we discuss tools that should be used to investigate synonymous variants. We provide supportive examples from seminal studies that demonstrate how these tools have driven new discoveries of functional synonymous SNVs. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-023-02966-1. BioMed Central 2023-05-22 /pmc/articles/PMC10204308/ /pubmed/37217943 http://dx.doi.org/10.1186/s13059-023-02966-1 Text en © This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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 Review
Lin, Brian C.
Katneni, Upendra
Jankowska, Katarzyna I.
Meyer, Douglas
Kimchi-Sarfaty, Chava
In silico methods for predicting functional synonymous variants
title In silico methods for predicting functional synonymous variants
title_full In silico methods for predicting functional synonymous variants
title_fullStr In silico methods for predicting functional synonymous variants
title_full_unstemmed In silico methods for predicting functional synonymous variants
title_short In silico methods for predicting functional synonymous variants
title_sort in silico methods for predicting functional synonymous variants
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10204308/
https://www.ncbi.nlm.nih.gov/pubmed/37217943
http://dx.doi.org/10.1186/s13059-023-02966-1
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