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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...

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Detalles Bibliográficos
Autores principales: Thornton, Alexis M., Tumu, Manoj, Brooks, Angela N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
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).
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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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