Cargando…
mutscan—a flexible R package for efficient end-to-end analysis of multiplexed assays of variant effect data
Multiplexed assays of variant effect (MAVE) experimentally measure the effect of large numbers of sequence variants by selective enrichment of sequences with desirable properties followed by quantification by sequencing. mutscan is an R package for flexible analysis of such experiments, covering the...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10236832/ https://www.ncbi.nlm.nih.gov/pubmed/37264470 http://dx.doi.org/10.1186/s13059-023-02967-0 |
_version_ | 1785053029418926080 |
---|---|
author | Soneson, Charlotte Bendel, Alexandra M. Diss, Guillaume Stadler, Michael B. |
author_facet | Soneson, Charlotte Bendel, Alexandra M. Diss, Guillaume Stadler, Michael B. |
author_sort | Soneson, Charlotte |
collection | PubMed |
description | Multiplexed assays of variant effect (MAVE) experimentally measure the effect of large numbers of sequence variants by selective enrichment of sequences with desirable properties followed by quantification by sequencing. mutscan is an R package for flexible analysis of such experiments, covering the entire workflow from raw reads up to statistical analysis and visualization. The core components are implemented in C++ for efficiency. Various experimental designs are supported, including single or paired reads with optional unique molecular identifiers. To find variants with changed relative abundance, mutscan employs established statistical models provided in the edgeR and limma packages. mutscan is available from https://github.com/fmicompbio/mutscan. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-023-02967-0. |
format | Online Article Text |
id | pubmed-10236832 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-102368322023-06-03 mutscan—a flexible R package for efficient end-to-end analysis of multiplexed assays of variant effect data Soneson, Charlotte Bendel, Alexandra M. Diss, Guillaume Stadler, Michael B. Genome Biol Software Multiplexed assays of variant effect (MAVE) experimentally measure the effect of large numbers of sequence variants by selective enrichment of sequences with desirable properties followed by quantification by sequencing. mutscan is an R package for flexible analysis of such experiments, covering the entire workflow from raw reads up to statistical analysis and visualization. The core components are implemented in C++ for efficiency. Various experimental designs are supported, including single or paired reads with optional unique molecular identifiers. To find variants with changed relative abundance, mutscan employs established statistical models provided in the edgeR and limma packages. mutscan is available from https://github.com/fmicompbio/mutscan. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-023-02967-0. BioMed Central 2023-06-01 /pmc/articles/PMC10236832/ /pubmed/37264470 http://dx.doi.org/10.1186/s13059-023-02967-0 Text en © The Author(s) 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 | Software Soneson, Charlotte Bendel, Alexandra M. Diss, Guillaume Stadler, Michael B. mutscan—a flexible R package for efficient end-to-end analysis of multiplexed assays of variant effect data |
title | mutscan—a flexible R package for efficient end-to-end analysis of multiplexed assays of variant effect data |
title_full | mutscan—a flexible R package for efficient end-to-end analysis of multiplexed assays of variant effect data |
title_fullStr | mutscan—a flexible R package for efficient end-to-end analysis of multiplexed assays of variant effect data |
title_full_unstemmed | mutscan—a flexible R package for efficient end-to-end analysis of multiplexed assays of variant effect data |
title_short | mutscan—a flexible R package for efficient end-to-end analysis of multiplexed assays of variant effect data |
title_sort | mutscan—a flexible r package for efficient end-to-end analysis of multiplexed assays of variant effect data |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10236832/ https://www.ncbi.nlm.nih.gov/pubmed/37264470 http://dx.doi.org/10.1186/s13059-023-02967-0 |
work_keys_str_mv | AT sonesoncharlotte mutscanaflexiblerpackageforefficientendtoendanalysisofmultiplexedassaysofvarianteffectdata AT bendelalexandram mutscanaflexiblerpackageforefficientendtoendanalysisofmultiplexedassaysofvarianteffectdata AT dissguillaume mutscanaflexiblerpackageforefficientendtoendanalysisofmultiplexedassaysofvarianteffectdata AT stadlermichaelb mutscanaflexiblerpackageforefficientendtoendanalysisofmultiplexedassaysofvarianteffectdata |