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An expression-based variant impact phenotyping protocol to predict the impact of gene variants in cell lines
We describe a bioinformatics protocol for eVIP2 (expression-based variant impact phenotyping). eVIP2 can predict a gene variant’s functional impact by comparing gene expression signatures induced by introduction of wild-type versus mutant cDNAs in cell lines. The predicted functional outcomes of the...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9449654/ https://www.ncbi.nlm.nih.gov/pubmed/36092819 http://dx.doi.org/10.1016/j.xpro.2022.101651 |
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author | Thornton, Alexis M. Tumu, Manoj Brooks, Angela N. |
author_facet | Thornton, Alexis M. Tumu, Manoj Brooks, Angela N. |
author_sort | Thornton, Alexis M. |
collection | PubMed |
description | We describe a bioinformatics protocol for eVIP2 (expression-based variant impact phenotyping). eVIP2 can predict a gene variant’s functional impact by comparing gene expression signatures induced by introduction of wild-type versus mutant cDNAs in cell lines. The predicted functional outcomes of the variants include gain-of-function, loss-of-function, change-of-function, or neutral. eVIP2 improves upon eVIP by being applicable to RNA-seq data and providing pathway-level functional predictions for each mutation. Here, we detail how to run eVIP2 on RNA-seq data from two RNF43 variants. For complete details on the use and execution of this protocol, please refer to Thornton et al. (2021). |
format | Online Article Text |
id | pubmed-9449654 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-94496542022-09-08 An expression-based variant impact phenotyping protocol to predict the impact of gene variants in cell lines Thornton, Alexis M. Tumu, Manoj Brooks, Angela N. STAR Protoc Protocol We describe a bioinformatics protocol for eVIP2 (expression-based variant impact phenotyping). eVIP2 can predict a gene variant’s functional impact by comparing gene expression signatures induced by introduction of wild-type versus mutant cDNAs in cell lines. The predicted functional outcomes of the variants include gain-of-function, loss-of-function, change-of-function, or neutral. eVIP2 improves upon eVIP by being applicable to RNA-seq data and providing pathway-level functional predictions for each mutation. Here, we detail how to run eVIP2 on RNA-seq data from two RNF43 variants. For complete details on the use and execution of this protocol, please refer to Thornton et al. (2021). Elsevier 2022-08-27 /pmc/articles/PMC9449654/ /pubmed/36092819 http://dx.doi.org/10.1016/j.xpro.2022.101651 Text en © 2022. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Protocol Thornton, Alexis M. Tumu, Manoj Brooks, Angela N. An expression-based variant impact phenotyping protocol to predict the impact of gene variants in cell lines |
title | An expression-based variant impact phenotyping protocol to predict the impact of gene variants in cell lines |
title_full | An expression-based variant impact phenotyping protocol to predict the impact of gene variants in cell lines |
title_fullStr | An expression-based variant impact phenotyping protocol to predict the impact of gene variants in cell lines |
title_full_unstemmed | An expression-based variant impact phenotyping protocol to predict the impact of gene variants in cell lines |
title_short | An expression-based variant impact phenotyping protocol to predict the impact of gene variants in cell lines |
title_sort | expression-based variant impact phenotyping protocol to predict the impact of gene variants in cell lines |
topic | Protocol |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9449654/ https://www.ncbi.nlm.nih.gov/pubmed/36092819 http://dx.doi.org/10.1016/j.xpro.2022.101651 |
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