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dc.contributor.authorLeidl, Mathias
dc.contributor.authorAvila-Pacheco, Julian
dc.contributor.authorPirhaji, Leila
dc.contributor.authorMilani, Pamela
dc.contributor.authorCurran, Timothy G.
dc.contributor.authorClish, Clary
dc.contributor.authorWhite, Forest M
dc.contributor.authorSaghatelian, Alan
dc.contributor.authorFraenkel, Ernest
dc.date.accessioned2018-09-05T17:26:25Z
dc.date.available2018-09-05T17:26:25Z
dc.date.issued2016-08
dc.identifier.issn1548-7091
dc.identifier.issn1548-7105
dc.identifier.urihttp://hdl.handle.net/1721.1/117642
dc.description.abstractUncovering the molecular context of dysregulated metabolites is crucial to understand pathogenic pathways. However, their system-level analysis has been limited owing to challenges in global metabolite identification. Most metabolite features detected by untargeted metabolomics carried out by liquid-chromatography-mass spectrometry cannot be uniquely identified without additional, time-consuming experiments. We report a network-based approach, prize-collecting Steiner forest algorithm for integrative analysis of untargeted metabolomics (PIUMet), that infers molecular pathways and components via integrative analysis of metabolite features, without requiring their identification. We demonstrated PIUMet by analyzing changes in metabolism of sphingolipids, fatty acids and steroids in a Huntington's disease model. Additionally, PIUMet enabled us to elucidate putative identities of altered metabolite features in diseased cells, and infer experimentally undetected, disease-associated metabolites and dysregulated proteins. Finally, we established PIUMet's ability for integrative analysis of untargeted metabolomics data with proteomics data, demonstrating that this approach elicits disease-associated metabolites and proteins that cannot be inferred by individual analysis of these data.en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (grant R01-GM089903)en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (grant U54-NS091046)en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (grant U01-CA184898)en_US
dc.description.sponsorshipNational Cancer Institute (U.S.) (grant U54 CA112967)en_US
dc.description.sponsorshipNational Cancer Institute (U.S.) (grant P30 CA014051)en_US
dc.description.sponsorshipSearle Scholars Programen_US
dc.publisherSpringer Natureen_US
dc.relation.isversionofhttp://dx.doi.org/10.1038/NMETH.3940en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourcePMCen_US
dc.titleRevealing disease-associated pathways by network integration of untargeted metabolomicsen_US
dc.typeArticleen_US
dc.identifier.citationPirhaji, Leila, Pamela Milani, Mathias Leidl, Timothy Curran, Julian Avila-Pacheco, Clary B Clish, Forest M White, Alan Saghatelian, and Ernest Fraenkel. “Revealing Disease-Associated Pathways by Network Integration of Untargeted Metabolomics.” Nature Methods 13, no. 9 (August 1, 2016): 770–776.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Biological Engineeringen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Biologyen_US
dc.contributor.mitauthorPirhaji, Leila
dc.contributor.mitauthorMilani, Pamela
dc.contributor.mitauthorCurran, Timothy G.
dc.contributor.mitauthorClish, Clary
dc.contributor.mitauthorWhite, Forest M
dc.contributor.mitauthorSaghatelian, Alan
dc.contributor.mitauthorFraenkel, Ernest
dc.relation.journalNature Methodsen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2018-08-30T15:06:54Z
dspace.orderedauthorsPirhaji, Leila; Milani, Pamela; Leidl, Mathias; Curran, Timothy; Avila-Pacheco, Julian; Clish, Clary B; White, Forest M; Saghatelian, Alan; Fraenkel, Ernesten_US
dspace.embargo.termsNen_US
dc.identifier.orcidhttps://orcid.org/0000-0001-6246-276X
dc.identifier.orcidhttps://orcid.org/0000-0003-0250-0474
dc.identifier.orcidhttps://orcid.org/0000-0002-1545-1651
dc.identifier.orcidhttps://orcid.org/0000-0001-9249-8181
mit.licenseOPEN_ACCESS_POLICYen_US


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