{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:34:16Z","timestamp":1772138056439,"version":"3.50.1"},"reference-count":11,"publisher":"Oxford University Press (OUP)","issue":"20","license":[{"start":{"date-parts":[[2022,8,25]],"date-time":"2022-08-25T00:00:00Z","timestamp":1661385600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Carlos III Health Institute of the Spanish Ministry of Economy and Competitiveness","award":["FI18\/00224"],"award-info":[{"award-number":["FI18\/00224"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,10,14]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>LipidMS was initially envisioned to use fragmentation rules and data-independent acquisition (DIA) for lipid annotation. However, data-dependent acquisition (DDA) remains the most widespread acquisition mode for untargeted LC-MS\/MS-based lipidomics. Here, we present LipidMS 3.0, an R package that not only adds DDA and new lipid classes to its pipeline but also the required functionalities to cover the whole data analysis workflow from pre-processing (i.e. peak-peaking, alignment and grouping) to lipid annotation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We applied the new workflow in the data analysis of a commercial human serum pool spiked with 68 representative lipid standards acquired in full scan, DDA and DIA modes. When focusing on the detected lipid standard features and total identified lipids, LipidMS 3.0 data pre-processing performance is similar to XCMS, whereas it complements the annotations returned by MS-DIAL, providing a higher level of structural information and a lower number of incorrect annotations. To extend and facilitate LipidMS 3.0 usage among less experienced R-programming users, the workflow is also implemented as a web-based application.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The LipidMS R-package is freely available at https:\/\/CRAN.R-project.org\/package=LipidMS and as a website at http:\/\/www.lipidms.com.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information<\/jats:title>\n                    <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btac581","type":"journal-article","created":{"date-parts":[[2022,8,24]],"date-time":"2022-08-24T20:17:55Z","timestamp":1661372275000},"page":"4826-4828","source":"Crossref","is-referenced-by-count":11,"title":["LipidMS 3.0: an R-package and a web-based tool for LC-MS\/MS data processing and lipid annotation"],"prefix":"10.1093","volume":"38","author":[{"given":"Mar\u00eda Isabel","family":"Alcoriza-Balaguer","sequence":"first","affiliation":[{"name":"Biomarkers and Precision Medicine Unit, Health Research Institute-Hospital La Fe , Valencia 46026, Spain"}]},{"given":"Juan Carlos","family":"Garc\u00eda-Ca\u00f1averas","sequence":"additional","affiliation":[{"name":"Biomarkers and Precision Medicine Unit, Health Research Institute-Hospital La Fe , Valencia 46026, Spain"}]},{"given":"Francisco Javier","family":"Ripoll-Esteve","sequence":"additional","affiliation":[{"name":"Department of Informatics, Health Research Institute-Hospital La Fe , Valencia 46026, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0708-6364","authenticated-orcid":false,"given":"Marta","family":"Roca","sequence":"additional","affiliation":[{"name":"Analytical Unit, Health Research Institute-Hospital La Fe , Valencia 46026, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7232-0626","authenticated-orcid":false,"given":"Agust\u00edn","family":"Lahoz","sequence":"additional","affiliation":[{"name":"Biomarkers and Precision Medicine Unit, Health Research Institute-Hospital La Fe , Valencia 46026, Spain"},{"name":"Analytical Unit, Health Research Institute-Hospital La Fe , Valencia 46026, Spain"}]}],"member":"286","published-online":{"date-parts":[[2022,8,25]]},"reference":[{"key":"2022101415185766600_btac581-B1","doi-asserted-by":"crossref","first-page":"836","DOI":"10.1021\/acs.analchem.8b03409","article-title":"LipidMS: an R package for lipid annotation in untargeted liquid chromatography-data independent acquisition-mass spectrometry lipidomics","volume":"91","author":"Alcoriza-Balaguer","year":"2019","journal-title":"Anal. Chem"},{"key":"2022101415185766600_btac581-B2","doi-asserted-by":"crossref","first-page":"918","DOI":"10.1038\/nbt.2377","article-title":"A cross-platform toolkit for mass spectrometry and proteomics","volume":"30","author":"Chambers","year":"2012","journal-title":"Nat. Biotechnol"},{"key":"2022101415185766600_btac581-B3","doi-asserted-by":"crossref","first-page":"8072","DOI":"10.1021\/acs.analchem.9b05135","article-title":"Comparison of full-scan, data-dependent, and data-independent acquisition modes in liquid chromatography\u2013mass spectrometry based untargeted metabolomics","volume":"92","author":"Guo","year":"2020","journal-title":"Anal. Chem"},{"key":"2022101415185766600_btac581-B4","doi-asserted-by":"crossref","first-page":"1171","DOI":"10.1038\/nmeth.4470","article-title":"Deciphering lipid structures based on platform-independent decision rules","volume":"14","author":"Hartler","year":"2017","journal-title":"Nat. Methods"},{"key":"2022101415185766600_btac581-B5","doi-asserted-by":"crossref","first-page":"908","DOI":"10.1007\/s13361-017-1608-0","article-title":"Expanding lipidome coverage using LC-MS\/MS data-dependent acquisition with automated exclusion list generation","volume":"28","author":"Koelmel","year":"2017","journal-title":"J. Am. Soc. Mass Spectrom"},{"key":"2022101415185766600_btac581-B6","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1186\/s12859-017-1744-3","article-title":"LipidMatch: an automated workflow for rule-based lipid identification using untargeted high-resolution tandem mass spectrometry data","volume":"18","author":"Koelmel","year":"2017","journal-title":"BMC Bioinformatics"},{"key":"2022101415185766600_btac581-B7","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.aca.2020.12.025","article-title":"Reviewing the metabolome coverage provided by LC-MS: focus on sample preparation and chromatography-A tutorial","volume":"1147","author":"Roca","year":"2021","journal-title":"Anal. Chim. Acta"},{"key":"2022101415185766600_btac581-B8","doi-asserted-by":"crossref","first-page":"779","DOI":"10.1021\/ac051437y","article-title":"XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification","volume":"78","author":"Smith","year":"2006","journal-title":"Anal. Chem"},{"key":"2022101415185766600_btac581-B9","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1038\/nmeth.3393","article-title":"MS-DIAL: data-independent MS\/MS deconvolution for comprehensive metabolome analysis","volume":"12","author":"Tsugawa","year":"2015","journal-title":"Nat. Methods"},{"key":"2022101415185766600_btac581-B10","doi-asserted-by":"crossref","first-page":"594","DOI":"10.1038\/nrd1776","article-title":"The emerging field of lipidomics","volume":"4","author":"Wenk","year":"2005","journal-title":"Nat. Rev. Drug Discov"},{"key":"2022101415185766600_btac581-B11","doi-asserted-by":"crossref","first-page":"2191","DOI":"10.1007\/s00216-019-02241-y","article-title":"Lipidomics from sample preparation to data analysis: a primer","volume":"412","author":"Z\u00fcllig","year":"2020","journal-title":"Anal. Bioanal. 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