{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T18:53:56Z","timestamp":1775501636246,"version":"3.50.1"},"reference-count":13,"publisher":"Oxford University Press (OUP)","issue":"7","license":[{"start":{"date-parts":[[2023,7,25]],"date-time":"2023-07-25T00:00:00Z","timestamp":1690243200000},"content-version":"vor","delay-in-days":24,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100014013","name":"UK Research and Innovation","doi-asserted-by":"publisher","award":["NE\/T010959\/1"],"award-info":[{"award-number":["NE\/T010959\/1"]}],"id":[{"id":"10.13039\/100014013","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,7,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Summary<\/jats:title>\n                  <jats:p>The Integrated Probabilistic Annotation (IPA) is an automated annotation method for LC\u2013MS-based untargeted metabolomics experiments that provides statistically rigorous estimates of the probabilities associated with each annotation. Here, we introduce ipaPy2, a substantially improved and completely refactored Python implementation of the IPA method. The revised method is now able to integrate tandem MS fragmentation data, which increases the accuracy of the identifications. Moreover, ipaPy2 provides a much more user-friendly interface, and isotope peaks are no longer treated as individual features but integrated into isotope fingerprints, greatly speeding up the calculations. The method has also been fully integrated with the mzMatch pipeline, so that the results of the annotation can be explored through the newly developed PeakMLViewerPy tool available at https:\/\/github.com\/UoMMIB\/PeakMLViewerPy.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The source code, extensive documentation, and tutorials are freely available on GitHub at https:\/\/github.com\/francescodc87\/ipaPy2<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btad455","type":"journal-article","created":{"date-parts":[[2023,7,25]],"date-time":"2023-07-25T17:29:43Z","timestamp":1690306183000},"source":"Crossref","is-referenced-by-count":6,"title":["ipaPy2: Integrated Probabilistic Annotation (IPA) 2.0\u2014an improved Bayesian-based method for the annotation of LC\u2013MS\/MS untargeted metabolomics data"],"prefix":"10.1093","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1647-7818","authenticated-orcid":false,"given":"Francesco","family":"Del Carratore","sequence":"first","affiliation":[{"name":"Manchester Institute of Biotechnology, Faculty of Science and Engineering, University of Manchester , Manchester M1 7DN, United Kingdom"},{"name":"Department of Biochemistry and Systems Biology, Institute of Integrative, Systems and Molecular Biology, University of Liverpool , Liverpool L69 3BX, United Kingdom"}]},{"given":"William","family":"Eagles","sequence":"additional","affiliation":[{"name":"Manchester Institute of Biotechnology, Faculty of Science and Engineering, University of Manchester , Manchester M1 7DN, United Kingdom"}]},{"given":"Juraj","family":"Borka","sequence":"additional","affiliation":[{"name":"Manchester Institute of Biotechnology, Faculty of Science and Engineering, University of Manchester , Manchester M1 7DN, United Kingdom"}]},{"given":"Rainer","family":"Breitling","sequence":"additional","affiliation":[{"name":"Manchester Institute of Biotechnology, Faculty of Science and Engineering, University of Manchester , Manchester M1 7DN, United Kingdom"}]}],"member":"286","published-online":{"date-parts":[[2023,7,25]]},"reference":[{"key":"2023072902254709100_btad455-B1","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1038\/s41592-021-01197-1","article-title":"Mass spectrometry-based metabolomics: a guide for annotation, quantification and best reporting practices","volume":"18","author":"Alseekh","year":"2021","journal-title":"Nat Methods"},{"key":"2023072902254709100_btad455-B2","doi-asserted-by":"crossref","first-page":"841373","DOI":"10.3389\/fmolb.2022.841373","article-title":"Networks and graphs discovery in metabolomics data analysis and interpretation","volume":"9","author":"Amara","year":"2022","journal-title":"Front Mol 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