Critical Assessment of Small Molecule Identification 2016: automated methods

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dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorSchymanski, Emma L.en_US
dc.contributor.authorRuttkies, Christophen_US
dc.contributor.authorKrauss, Martinen_US
dc.contributor.authorBrouard, Célineen_US
dc.contributor.authorKind, Tobiasen_US
dc.contributor.authorDührkop, Kaien_US
dc.contributor.authorAllen, Felicityen_US
dc.contributor.authorVaniya, Arpanaen_US
dc.contributor.authorVerdegem, Driesen_US
dc.contributor.authorBöcker, Sebastianen_US
dc.contributor.authorRousu, Juhoen_US
dc.contributor.authorShen, Huibinen_US
dc.contributor.authorTsugawa, Hiroshien_US
dc.contributor.authorSajed, Tanviren_US
dc.contributor.authorFiehn, Oliveren_US
dc.contributor.authorGhesquière, Barten_US
dc.contributor.authorNeumann, Steffenen_US
dc.contributor.departmentDepartment of Computer Scienceen
dc.contributor.groupauthorProfessorship Rousu Juhoen
dc.contributor.groupauthorHelsinki Institute for Information Technology (HIIT)en
dc.contributor.organizationSwiss Federal Institute of Aquatic Science and Technologyen_US
dc.contributor.organizationLeibniz Institute of Plant Biochemistryen_US
dc.contributor.organizationHelmholtz Centre for Environmental Researchen_US
dc.contributor.organizationUniversity of California, Davisen_US
dc.contributor.organizationFriedrich Schiller University Jenaen_US
dc.contributor.organizationUniversity of Albertaen_US
dc.contributor.organizationKU Leuvenen_US
dc.contributor.organizationRIKENen_US
dc.contributor.organizationKing Abdulaziz Universityen_US
dc.date.accessioned2017-05-11T09:04:39Z
dc.date.available2017-05-11T09:04:39Z
dc.date.issued2017-03-27en_US
dc.description.abstractBackground: The fourth round of the Critical Assessment of Small Molecule Identification (CASMI) Contest ( www.casmi-contest.org ) was held in 2016, with two new categories for automated methods. This article covers the 208 challenges in Categories 2 and 3, without and with metadata, from organization, participation, results and post-contest evaluation of CASMI 2016 through to perspectives for future contests and small molecule annotation/identification. Results: The Input Output Kernel Regression (CSI:IOKR) machine learning approach performed best in "Category 2: Best Automatic Structural Identification - In Silico Fragmentation Only", won by Team Brouard with 41% challenge wins. The winner of "Category 3: Best Automatic Structural Identification - Full Information" was Team Kind (MS-FINDER), with 76% challenge wins. The best methods were able to achieve over 30% Top 1 ranks in Category 2, with all methods ranking the correct candidate in the Top 10 in around 50% of challenges. This success rate rose to 70% Top 1 ranks in Category 3, with candidates in the Top 10 in over 80% of the challenges. The machine learning and chemistry-based approaches are shown to perform in complementary ways. Conclusions: The improvement in (semi-)automated fragmentation methods for small molecule identification has been substantial. The achieved high rates of correct candidates in the Top 1 and Top 10, despite large candidate numbers, open up great possibilities for high-throughput annotation of untargeted analysis for "known unknowns". As more high quality training data becomes available, the improvements in machine learning methods will likely continue, but the alternative approaches still provide valuable complementary information. Improved integration of experimental context will also improve identification success further for "real life" annotations. The true "unknown unknowns" remain to be evaluated in future CASMI contests. Graphical abstract .en
dc.description.versionPeer revieweden
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationSchymanski, E L, Ruttkies, C, Krauss, M, Brouard, C, Kind, T, Dührkop, K, Allen, F, Vaniya, A, Verdegem, D, Böcker, S, Rousu, J, Shen, H, Tsugawa, H, Sajed, T, Fiehn, O, Ghesquière, B & Neumann, S 2017, ' Critical Assessment of Small Molecule Identification 2016 : automated methods ', JOURNAL OF CHEMINFORMATICS, vol. 9, no. 1, 22 . https://doi.org/10.1186/s13321-017-0207-1en
dc.identifier.doi10.1186/s13321-017-0207-1en_US
dc.identifier.issn1758-2946
dc.identifier.otherPURE UUID: b23d4885-bc48-4482-a6ad-944b3db60413en_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/b23d4885-bc48-4482-a6ad-944b3db60413en_US
dc.identifier.otherPURE LINK: http://www.scopus.com/inward/record.url?scp=85016274882&partnerID=8YFLogxKen_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/11609835/art_10.1186_s13321_017_0207_1.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/25801
dc.identifier.urnURN:NBN:fi:aalto-201705114176
dc.language.isoenen
dc.relation.ispartofseriesJOURNAL OF CHEMINFORMATICSen
dc.relation.ispartofseriesVolume 9, issue 1en
dc.rightsopenAccessen
dc.subject.keywordCompound identificationen_US
dc.subject.keywordHigh resolution mass spectrometryen_US
dc.subject.keywordIn silico fragmentationen_US
dc.subject.keywordMetabolomicsen_US
dc.subject.keywordStructure elucidationen_US
dc.titleCritical Assessment of Small Molecule Identification 2016: automated methodsen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi
dc.type.versionpublishedVersion
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