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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "mutscan" in publications use:'
type: software
license: MIT
title: 'mutscan: Preprocessing and Analysis of Deep Mutational Scanning Data'
version: 1.3.0
doi: 10.1186/s13059-023-02967-0
identifiers:
- type: doi
  value: 10.32614/CRAN.package.mutscan
abstract: Provides functionality for processing and statistical analysis of multiplexed
  assays of variant effect (MAVE) and similar data. The package contains functions
  covering the full workflow from raw FASTQ files to publication-ready visualizations.
  A broad range of library designs can be processed with a single, unified interface.
authors:
- family-names: Soneson
  given-names: Charlotte
  email: charlottesoneson@gmail.com
  orcid: https://orcid.org/0000-0003-3833-2169
- family-names: Stadler
  given-names: Michael
  email: michael.stadler@fmi.ch
  orcid: https://orcid.org/0000-0002-2269-4934
preferred-citation:
  type: article
  title: mutscan-a flexible R package for efficient end-to-end analysis of multiplexed
    assays of variant effect data
  authors:
  - family-names: Soneson
    given-names: Charlotte
    email: charlottesoneson@gmail.com
    orcid: https://orcid.org/0000-0003-3833-2169
  - family-names: Bendel
    given-names: Alexandra M.
  - family-names: Diss
    given-names: Guillaume
  - family-names: Stadler
    given-names: Michael B.
  publisher:
    name: Springer Nature
  journal: Genome Biology
  year: '2023'
  month: '6'
  volume: '24'
  doi: 10.1186/s13059-023-02967-0
  issn: 1474-760X
  url: https://doi.org/10.1186/s13059-023-02967-0
  abstract: 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.
  start: '132'
repository: https://bioc.r-universe.dev
repository-code: https://github.com/fmicompbio/mutscan
commit: d0c6226d03c0ed700cd507ded9f4a52e32ed80a2
url: https://github.com/fmicompbio/mutscan
date-released: '2026-04-28'
contact:
- family-names: Soneson
  given-names: Charlotte
  email: charlottesoneson@gmail.com
  orcid: https://orcid.org/0000-0003-3833-2169