{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T20:48:27Z","timestamp":1770065307917,"version":"3.49.0"},"reference-count":6,"publisher":"Oxford University Press (OUP)","issue":"16","license":[{"start":{"date-parts":[[2019,1,2]],"date-time":"2019-01-02T00:00:00Z","timestamp":1546387200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["21575151"],"award-info":[{"award-number":["21575151"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002367","name":"Chinese Academy of Sciences","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002367","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018635","name":"Thousand Youth Talents Program","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100018635","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,8,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Summary<\/jats:title>\n                  <jats:p>Mass spectrometry-based metabolomics aims to profile the metabolic changes in biological systems and identify differential metabolites related to physiological phenotypes and aberrant activities. However, many confounding factors during data acquisition complicate metabolomics data, which is characterized by high dimensionality, uncertain degrees of missing and zero values, nonlinearity, unwanted variations and non-normality. Therefore, prior to differential metabolite discovery analysis, various types of data cleaning such as batch alignment, missing value imputation, data normalization and scaling are essentially required for data post-processing. Here, we developed an interactive web server, namely, MetFlow, to provide an integrated and comprehensive workflow for metabolomics data cleaning and differential metabolite discovery.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The MetFlow is freely available on http:\/\/metflow.zhulab.cn\/.<\/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\/bty1066","type":"journal-article","created":{"date-parts":[[2018,12,27]],"date-time":"2018-12-27T12:08:40Z","timestamp":1545912520000},"page":"2870-2872","source":"Crossref","is-referenced-by-count":35,"title":["MetFlow: an interactive and integrated workflow for metabolomics data cleaning and differential metabolite discovery"],"prefix":"10.1093","volume":"35","author":[{"given":"Xiaotao","family":"Shen","sequence":"first","affiliation":[{"name":"Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China"},{"name":"Univeristy of Chinese Academy of Sciences, Beijing, China"}]},{"given":"Zheng-Jiang","family":"Zhu","sequence":"additional","affiliation":[{"name":"Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China"}]}],"member":"286","published-online":{"date-parts":[[2019,1,2]]},"reference":[{"key":"2023062708580591200_bty1066-B1","doi-asserted-by":"crossref","first-page":"1060","DOI":"10.1038\/nprot.2011.335","article-title":"Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry","volume":"6","author":"Dunn","year":"2011","journal-title":"Nat. Protoc"},{"key":"2023062708580591200_bty1066-B2","doi-asserted-by":"crossref","first-page":"93.","DOI":"10.1007\/s11306-016-1030-9","article-title":"Non-targeted UHPLC-MS metabolomic data processing methods: a comparative investigation of normalisation, missing value imputation, transformation and scaling","volume":"12","author":"Guida","year":"2016","journal-title":"Metabolomics"},{"key":"2023062708580591200_bty1066-B3","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1007\/s11306-011-0366-4","article-title":"Missing values in mass spectrometry based metabolomics: an undervalued step in the data processing pipeline","volume":"8","author":"Hrydziuszko","year":"2012","journal-title":"Metabolomics"},{"key":"2023062708580591200_bty1066-B4","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1038\/nrm.2016.25","article-title":"Metabolomics: beyond biomarkers and towards mechanisms","volume":"17","author":"Johnson","year":"2016","journal-title":"Nat. Rev. Mol. Cell Biol"},{"key":"2023062708580591200_bty1066-B5","doi-asserted-by":"crossref","first-page":"89.","DOI":"10.1007\/s11306-016-1026-5","article-title":"Normalization and integration of large-scale metabolomics data using support vector regression","volume":"12","author":"Shen","year":"2016","journal-title":"Metabolomics"},{"key":"2023062708580591200_bty1066-B6","doi-asserted-by":"crossref","first-page":"3417","DOI":"10.1093\/bioinformatics\/bty344","article-title":"MetaboDiff: an R package for differential metabolomic analysis","volume":"34","author":"Mock","year":"2018","journal-title":"Bioinformatics"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/35\/16\/2870\/50719247\/bty1066.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/35\/16\/2870\/50719247\/bty1066.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,27]],"date-time":"2023-06-27T08:58:27Z","timestamp":1687856307000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/35\/16\/2870\/5270667"}},"subtitle":[],"editor":[{"given":"Jonathan","family":"Wren","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2019,1,2]]},"references-count":6,"journal-issue":{"issue":"16","published-print":{"date-parts":[[2019,8,15]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/bty1066","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2019,8,15]]},"published":{"date-parts":[[2019,1,2]]}}}