{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T14:35:14Z","timestamp":1776782114234,"version":"3.51.2"},"reference-count":42,"publisher":"Oxford University Press (OUP)","issue":"W1","license":[{"start":{"date-parts":[[2022,5,25]],"date-time":"2022-05-25T00:00:00Z","timestamp":1653436800000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000038","name":"NSERC","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100013373","name":"AMII","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100013373","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000024","name":"CIHR","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000024","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000066","name":"National Institute of Environmental Health Sciences","doi-asserted-by":"publisher","award":["U2CES030170"],"award-info":[{"award-number":["U2CES030170"]}],"id":[{"id":"10.13039\/100000066","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,7,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The CFM-ID 4.0 web server (https:\/\/cfmid.wishartlab.com) is an online tool for predicting, annotating and interpreting tandem mass (MS\/MS) spectra of small molecules. It is specifically designed to assist researchers pursuing studies in metabolomics, exposomics and analytical chemistry. More specifically, CFM-ID 4.0 supports the: 1) prediction of electrospray ionization quadrupole time-of-flight tandem mass spectra (ESI-QTOF-MS\/MS) for small molecules over multiple collision energies (10 eV, 20 eV, and 40 eV); 2) annotation of ESI-QTOF-MS\/MS spectra given the structure of the compound; and 3) identification of a small molecule that generated a given ESI-QTOF-MS\/MS spectrum at one or more collision energies. The CFM-ID 4.0 web server makes use of a substantially improved MS fragmentation algorithm, a much larger database of experimental and in silico predicted MS\/MS spectra and improved scoring methods to offer more accurate MS\/MS spectral prediction and MS\/MS-based compound identification. Compared to earlier versions of CFM-ID, this new version has an MS\/MS spectral prediction performance that is \u223c22% better and a compound identification accuracy that is \u223c35% better on a standard (CASMI 2016) testing dataset. CFM-ID 4.0 also features a neutral loss function that allows users to identify similar or substituent compounds where no match can be found using CFM-ID\u2019s regular MS\/MS-to-compound identification utility. Finally, the CFM-ID 4.0 web server now offers a much more refined user interface that is easier to use, supports molecular formula identification (from MS\/MS data), provides more interactively viewable data (including proposed fragment ion structures) and displays MS mirror plots for comparing predicted with observed MS\/MS spectra. These improvements should make CFM-ID 4.0 much more useful to the community and should make small molecule identification much easier, faster, and more accurate.<\/jats:p>","DOI":"10.1093\/nar\/gkac383","type":"journal-article","created":{"date-parts":[[2022,5,25]],"date-time":"2022-05-25T01:36:57Z","timestamp":1653442617000},"page":"W165-W174","source":"Crossref","is-referenced-by-count":111,"title":["CFM-ID 4.0 \u2013 a web server for accurate MS-based metabolite identification"],"prefix":"10.1093","volume":"50","author":[{"given":"Fei","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Computing Science, University of Alberta , Edmonton , AB , T6G 2E8 , Canada"}]},{"given":"Dana","family":"Allen","sequence":"additional","affiliation":[{"name":"Department of Biological Sciences, University of Alberta, Edmonton , AB , T6G 2E9 , Canada"}]},{"given":"Siyang","family":"Tian","sequence":"additional","affiliation":[{"name":"Department of Biological Sciences, University of Alberta, Edmonton , AB , T6G 2E9 , Canada"}]},{"given":"Eponine","family":"Oler","sequence":"additional","affiliation":[{"name":"Department of Biological Sciences, University of Alberta, Edmonton , AB , T6G 2E9 , Canada"}]},{"given":"Vasuk","family":"Gautam","sequence":"additional","affiliation":[{"name":"Department of Biological Sciences, University of Alberta, Edmonton , AB , T6G 2E9 , Canada"}]},{"given":"Russell","family":"Greiner","sequence":"additional","affiliation":[{"name":"Department of Computing Science, University of Alberta , Edmonton , AB , T6G 2E8 , Canada"},{"name":"Alberta Machine Intelligence Institute, University of Alberta , Edmonton , AB , T6G 2E8 , Canada"}]},{"given":"Thomas\u00a0O","family":"Metz","sequence":"additional","affiliation":[{"name":"Biological Sciences Division, Pacific Northwest National Laboratory , Richland , WA 99352 , USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3207-2434","authenticated-orcid":false,"given":"David\u00a0S","family":"Wishart","sequence":"additional","affiliation":[{"name":"Department of Computing Science, University of Alberta , Edmonton , AB , T6G 2E8 , Canada"},{"name":"Department of Biological Sciences, University of Alberta, Edmonton , AB , T6G 2E9 , Canada"},{"name":"Department of Laboratory Medicine and Pathology, University of Alberta , Edmonton , AB , T6G 2B7 , Canada"},{"name":"Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta , Edmonton , AB , T6G 2H7 , Canada"},{"name":"Biological Sciences Division, Pacific Northwest National Laboratory , Richland , WA 99352 , USA"}]}],"member":"286","published-online":{"date-parts":[[2022,5,24]]},"reference":[{"key":"2022070500002550800_B1","doi-asserted-by":"crossref","first-page":"23","DOI":"10.3389\/fbioe.2015.00023","article-title":"Analytical methods in untargeted metabolomics: state of the art in 2015","volume":"3","author":"Alonso","year":"2015","journal-title":"Front. Bioeng. Biotechnol."},{"key":"2022070500002550800_B2","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.aca.2019.10.005","article-title":"Simultaneous targeted and untargeted UHPLC-ESI-MS\/MS method with data-independent acquisition for quantification and profiling of (oxidized) fatty acids released upon platelet activation by thrombin","volume":"1094","author":"Cebo","year":"2020","journal-title":"Anal. Chim. Acta"},{"key":"2022070500002550800_B3","doi-asserted-by":"crossref","first-page":"osab003","DOI":"10.1093\/exposome\/osab003","article-title":"Analytical strategies for chemical exposomics: exploring limits and feasibility","volume":"1","author":"Vitale","year":"2021","journal-title":"Exposome"},{"key":"2022070500002550800_B4","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1021\/acsomega.7b01536","article-title":"LC-MS\/MS-based method for the multiplex detection of 24 fentanyl analogues and metabolites in whole blood at sub ng mL\u20131 concentrations","volume":"3","author":"Strayer","year":"2018","journal-title":"ACS Omega"},{"key":"2022070500002550800_B5","doi-asserted-by":"crossref","first-page":"e00122","DOI":"10.1016\/j.teac.2021.e00122","article-title":"Recent advances in analytical methodologies based on mass spectrometry for the environmental analysis of halogenated organic contaminants","volume":"30","author":"Ayala-Cabrera","year":"2021","journal-title":"Trends Environ. Anal. Chem."},{"key":"2022070500002550800_B6","doi-asserted-by":"crossref","first-page":"D622","DOI":"10.1093\/nar\/gkab1062","article-title":"HMDB 5.0: the human metabolome database for 2022","volume":"50","author":"Wishart","year":"2021","journal-title":"Nucleic Acids Res."},{"key":"2022070500002550800_B7","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1186\/s12302-019-0237-6","article-title":"Establish data infrastructure to compile and exchange environmental screening data on a european scale","volume":"31","author":"Slobodnik","year":"2019","journal-title":"Environ. Sci. Eur."},{"key":"2022070500002550800_B8","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1002\/jms.1777","article-title":"MassBank: a public repository for sharing mass spectral data for life sciences","volume":"45","author":"Horai","year":"2010","journal-title":"J. Mass Spectrom."},{"key":"2022070500002550800_B9","doi-asserted-by":"crossref","first-page":"828","DOI":"10.1038\/nbt.3597","article-title":"Sharing and community curation of mass spectrometry data with global natural products social molecular networking","volume":"34","author":"Wang","year":"2016","journal-title":"Nat. Biotechnol."},{"key":"2022070500002550800_B10","article-title":"NIST\/EPA\/NIH mass spectral library with search program data version: NIST v14 mass spectrometry data center national institute of standards and technology","author":"Stephen","year":"2014"},{"key":"2022070500002550800_B11","article-title":"NIST\/EPA\/NIH mass spectral library with search program data version: NIST v17 mass spectrometry data center national institute of standards and technology","author":"Stephen","year":"2017"},{"key":"2022070500002550800_B12","article-title":"NIST\/EPA\/NIH mass spectral library with search program data version: NIST v20 mass spectrometry data center national institute of standards and technology","author":"Stephen","year":"2020"},{"key":"2022070500002550800_B13","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1097\/01.ftd.0000179845.53213.39","article-title":"METLIN\u202f: a metabolite mass spectral database","volume":"27","author":"Smith","year":"2005","journal-title":"Ther. Drug Monit."},{"key":"2022070500002550800_B14","doi-asserted-by":"crossref","first-page":"3156","DOI":"10.1021\/acs.analchem.7b04424","article-title":"METLIN: a technology platform for identifying knowns and unknowns","volume":"90","author":"Guijas","year":"2018","journal-title":"Anal. Chem."},{"key":"2022070500002550800_B15","first-page":"420","article-title":"Natural products as reservoirs of novel therapeutic agents","volume":"17","author":"Mushtaq","year":"2018","journal-title":"EXCLI J."},{"key":"2022070500002550800_B16","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1186\/s13321-020-00478-9","article-title":"COCONUT online: collection of open natural products database","volume":"13","author":"Sorokina","year":"2021","journal-title":"J. Cheminform."},{"key":"2022070500002550800_B17","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/S0027-5107(01)00289-5","article-title":"Distributed structure-searchable toxicity (DSSTox) database","volume":"499","author":"Richard","year":"2022","journal-title":"Mutat. Res."},{"key":"2022070500002550800_B18","doi-asserted-by":"crossref","first-page":"180125","DOI":"10.1038\/sdata.2018.125","article-title":"The chemical and products database, a resource for exposure-relevant data on chemicals in consumer products","volume":"5","author":"Dionisio","year":"2017","journal-title":"Sci. Data."},{"key":"2022070500002550800_B19","doi-asserted-by":"crossref","first-page":"12549","DOI":"10.1073\/pnas.1516878112","article-title":"Illuminating the dark matter in metabolomics","volume":"112","author":"da\u00a0Silva","year":"2015","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"2022070500002550800_B20","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.aca.2017.12.034","article-title":"Dark matter in host-microbiome metabolomics: tackling the unknowns\u2013A review","volume":"1037","author":"Peisl","year":"2018","journal-title":"Anal. Chim. Acta."},{"key":"2022070500002550800_B21","doi-asserted-by":"crossref","first-page":"2333","DOI":"10.1093\/bioinformatics\/bts437","article-title":"Metabolite identification and molecular fingerprint prediction through machine learning","volume":"28","author":"Heinonen","year":"2012","journal-title":"Bioinformatics."},{"key":"2022070500002550800_B22","doi-asserted-by":"crossref","first-page":"484","DOI":"10.3390\/metabo3020484","article-title":"Metabolite identification through machine learning\u2014 tackling casmi challenge using fingerid","volume":"3","author":"Shen","year":"2013","journal-title":"Metabolites."},{"key":"2022070500002550800_B23","doi-asserted-by":"crossref","first-page":"i157","DOI":"10.1093\/bioinformatics\/btu275","article-title":"Metabolite identification through multiple kernel learning on fragmentation trees","volume":"30","author":"Shen","year":"2014","journal-title":"Bioinformatics"},{"key":"2022070500002550800_B24","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1038\/s41592-019-0344-8","article-title":"SIRIUS 4: a rapid tool for turning tandem mass spectra into metabolite structure information","volume":"16","author":"D\u00fchrkop","year":"2019","journal-title":"Nat. Methods"},{"key":"2022070500002550800_B25","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1186\/s13321-016-0115-9","article-title":"MetFrag relaunched: incorporating strategies beyond in silico fragmentation","volume":"8","author":"Ruttkies","year":"2016","journal-title":"J. Cheminform."},{"key":"2022070500002550800_B26","doi-asserted-by":"crossref","first-page":"755","DOI":"10.1038\/nmeth.2551","article-title":"LipidBlast in silico tandem mass spectrometry database for lipid identification","volume":"10","author":"Kind","year":"2013","journal-title":"Nat. Methods"},{"key":"2022070500002550800_B27","doi-asserted-by":"crossref","first-page":"7689","DOI":"10.1021\/acs.analchem.6b01622","article-title":"Computational prediction of electron ionization mass spectra to assist in GC\/MS compound identification","volume":"88","author":"Allen","year":"2016","journal-title":"Anal. Chem."},{"key":"2022070500002550800_B28","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1007\/s11306-014-0676-4","article-title":"Competitive fragmentation modeling of ESI-MS\/MS spectra for putative metabolite identification","volume":"11","author":"Allen","year":"2015","journal-title":"Metabolomics."},{"key":"2022070500002550800_B29","doi-asserted-by":"crossref","first-page":"700","DOI":"10.1021\/acscentsci.9b00085","article-title":"Rapid prediction of electron-ionization mass spectrometry using neural networks","volume":"5","author":"Wei","year":"2019","journal-title":"ACS Cent. Sci."},{"key":"2022070500002550800_B30","doi-asserted-by":"crossref","first-page":"72","DOI":"10.3390\/metabo9040072","article-title":"CFM-ID 3.0: significantly improved ESI-MS\/MS prediction and compound identification","volume":"9","author":"Djoumbou-Feunang","year":"2019","journal-title":"Metabolites."},{"key":"2022070500002550800_B31","doi-asserted-by":"crossref","first-page":"2096","DOI":"10.1093\/bioinformatics\/bty080","article-title":"ChemDistiller: an engine for metabolite annotation in mass spectrometry","volume":"34","author":"Laponogov","year":"2018","journal-title":"Bioinformatics."},{"key":"2022070500002550800_B32","doi-asserted-by":"crossref","first-page":"12580","DOI":"10.1073\/pnas.1509788112","article-title":"Searching molecular structure databases with tandem mass spectra using CSI:FingerID","volume":"112","author":"D\u00fchrkop","year":"2015","journal-title":"Proc. Natl\/Acad. Sci. USA"},{"key":"2022070500002550800_B33","doi-asserted-by":"crossref","first-page":"891","DOI":"10.1016\/j.jasms.2007.01.008","article-title":"Liquid chromatography electron capture dissociation tandem mass spectrometry (LC-ECD-MS\/MS) versus liquid chromatography collision-induced dissociation tandem mass spectrometry (LC-CID-MS\/MS) for the identification of proteins","volume":"18","author":"Creese","year":"2007","journal-title":"J. Am. Soc. Mass Spectrom."},{"key":"2022070500002550800_B34","doi-asserted-by":"crossref","first-page":"W94","DOI":"10.1093\/nar\/gku436","article-title":"CFM-ID: a web server for annotation, spectrum prediction and metabolite identification from tandem mass spectra","volume":"42","author":"Allen","year":"2014","journal-title":"Nucleic Acids Res."},{"key":"2022070500002550800_B35","doi-asserted-by":"crossref","first-page":"11692","DOI":"10.1021\/acs.analchem.1c01465","article-title":"CFM-ID 4.0: more accurate ESI-MS\/MS spectral prediction and compound identification","volume":"93","author":"Wang","year":"2021","journal-title":"Anal. Chem."},{"key":"2022070500002550800_B36","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1021\/jasms.1c00343","article-title":"Neutral loss mass spectral data enhances molecular similarity analysis in METLIN","volume":"33","author":"Aisporna","year":"2022","journal-title":"J. Am. Soc. Mass Spectrom."},{"key":"2022070500002550800_B37","doi-asserted-by":"crossref","first-page":"13261","DOI":"10.1021\/acs.analchem.7b03320","article-title":"Combining fragment-ion and neutral-loss matching during mass spectral library searching: a new general purpose algorithm applicable to illicit drug identification","volume":"89","author":"Moorthy","year":"2017","journal-title":"Anal. Chem."},{"key":"2022070500002550800_B38","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1186\/s13321-016-0174-y","article-title":"ClassyFire: automated chemical classification with a comprehensive, computable taxonomy","volume":"8","author":"Djoumbou\u00a0Feunang","year":"2016","journal-title":"J. Cheminform."},{"key":"2022070500002550800_B39","doi-asserted-by":"crossref","first-page":"D665","DOI":"10.1093\/nar\/gkab1052","article-title":"NP-MRD: the natural products magnetic resonance database","volume":"50","author":"Wishart","year":"2021","journal-title":"Nucleic Acids Res."},{"key":"2022070500002550800_B40","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/j.phytol.2016.12.008","article-title":"Using MS-FINDER for identifying 19 natural products in the CASMI 2016 contest","volume":"21","author":"Vaniya","year":"2017","journal-title":"Phytochem. Lett."},{"key":"2022070500002550800_B41","doi-asserted-by":"crossref","first-page":"e4693","DOI":"10.1002\/jms.4693","article-title":"Collision energies on QTof and orbitrap instruments: how to make proteomics measurements comparable","volume":"56","author":"Szab\u00f3","year":"2021","journal-title":"J. Mass Spectrom."},{"key":"2022070500002550800_B42","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/S1574-1400(08)00012-1","article-title":"PubChem: integrated platform of small molecules and biological activities","volume":"4","author":"Bolton","year":"2008","journal-title":"Annu. Rep. Comput. Chem."}],"container-title":["Nucleic Acids Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/nar\/article-pdf\/50\/W1\/W165\/44379339\/gkac383.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/nar\/article-pdf\/50\/W1\/W165\/44379339\/gkac383.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,5]],"date-time":"2022-07-05T00:05:05Z","timestamp":1656979505000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/nar\/article\/50\/W1\/W165\/6591530"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,24]]},"references-count":42,"journal-issue":{"issue":"W1","published-online":{"date-parts":[[2022,5,24]]},"published-print":{"date-parts":[[2022,7,5]]}},"URL":"https:\/\/doi.org\/10.1093\/nar\/gkac383","relation":{},"ISSN":["0305-1048","1362-4962"],"issn-type":[{"value":"0305-1048","type":"print"},{"value":"1362-4962","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2022,7,5]]},"published":{"date-parts":[[2022,5,24]]}}}