Soneson C, Bendel A, Diss G, Stadler M (2023). “mutscan-a flexible R package for efficient end-to-end analysis of multiplexed assays of variant effect data.” Genome Biology, 24, 132. ISSN 1474-760X. doi:10.1186/s13059-023-02967-0. https://doi.org/10.1186/s13059-023-02967-0.

Corresponding BibTeX entry:

  @Article{,
    title = {mutscan-a flexible R package for efficient end-to-end
      analysis of multiplexed assays of variant effect data},
    author = {Charlotte Soneson and Alexandra M. Bendel and Guillaume
      Diss and Michael B. Stadler},
    publisher = {Springer Nature},
    journal = {Genome Biology},
    year = {2023},
    month = {Jun},
    volume = {24},
    pages = {132},
    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.},
  }