{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T16:56:15Z","timestamp":1767372975825,"version":"3.41.2"},"reference-count":9,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2023,6,2]],"date-time":"2023-06-02T00:00:00Z","timestamp":1685664000000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"German Ministry for Science and Education","award":["FKZ 161L0214E"],"award-info":[{"award-number":["FKZ 161L0214E"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Summary<\/jats:title>\n                  <jats:p>mpwR is an R package for a standardized comparison of mass spectrometry (MS)-based proteomic label-free workflows recorded by data-dependent or data-independent spectral acquisition. The user-friendly design allows easy access to compare the influence of sample preparation procedures, combinations of liquid chromatography (LC)-MS setups, as well as intra- and inter-software differences on critical performance measures across an unlimited number of analyses. mpwR supports outputs of commonly used software for bottom-up proteomics, such as ProteomeDiscoverer, Spectronaut, MaxQuant, and DIA-NN.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>mpwR is available as an open-source R package. Release versions can be accessed on CRAN (https:\/\/CRAN.R-project.org\/package=mpwR) for all major operating systems. The development version is maintained on GitHub (https:\/\/github.com\/okdll\/mpwR) and full documentation with examples and workflow templates is provided via the package website (https:\/\/okdll.github.io\/mpwR\/).<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btad358","type":"journal-article","created":{"date-parts":[[2023,6,2]],"date-time":"2023-06-02T23:33:52Z","timestamp":1685748832000},"source":"Crossref","is-referenced-by-count":6,"title":["mpwR: an R package for comparing performance of mass spectrometry-based proteomic workflows"],"prefix":"10.1093","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6703-7997","authenticated-orcid":false,"given":"Oliver","family":"Kardell","sequence":"first","affiliation":[{"name":"Metabolomics and Proteomics Core (MPC), Helmholtz Zentrum M\u00fcnchen, German Research Center for Environmental Health (GmbH) , 80939 M\u00fcnchen, Germany"}]},{"given":"Stephan","family":"Breimann","sequence":"additional","affiliation":[{"name":"German Center for Neurodegenerative Diseases (DZNE) Munich, DZNE , 81377 M\u00fcnchen, Germany"},{"name":"Biomedical Center, Division of Metabolic Biochemistry, LMU Munich , 81377 M\u00fcnchen, Germany"},{"name":"Department of Genome Oriented Bioinformatics, Technical University Munich, Wissenschaftszentrum Weihenstephan , 85354 Freising, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1630-6827","authenticated-orcid":false,"given":"Stefanie M","family":"Hauck","sequence":"additional","affiliation":[{"name":"Metabolomics and Proteomics Core (MPC), Helmholtz Zentrum M\u00fcnchen, German Research Center for Environmental Health (GmbH) , 80939 M\u00fcnchen, Germany"}]}],"member":"286","published-online":{"date-parts":[[2023,6,2]]},"reference":[{"key":"2023061408391253700_btad358-B1","doi-asserted-by":"crossref","first-page":"1400","DOI":"10.1074\/mcp.M114.044305","article-title":"Extending the limits of quantitative proteome profiling with data-independent acquisition and application to acetaminophen-treated three-dimensional liver microtissues","volume":"14","author":"Bruderer","year":"2015","journal-title":"Mol Cell Proteomics"},{"author":"Childs","key":"2023061408391253700_btad358-B2","doi-asserted-by":"publisher","DOI":"10.18129\/B9.bioc.TPP"},{"key":"2023061408391253700_btad358-B3","doi-asserted-by":"crossref","first-page":"2524","DOI":"10.1093\/bioinformatics\/btu305","article-title":"MSstats: an R package for statistical analysis of quantitative mass spectrometry-based proteomic 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