{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Quinlan ZA"],"funding":["Blasker Environmental Grant of the San Diego Foundation","NIEHS NIH HHS","Gordon and Betty Moore Foundation","National Science Foundation"],"pagination":["1275"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9786801"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["12(12)"],"pubmed_abstract":["Recent developments in molecular networking have expanded our ability to characterize the metabolome of diverse samples that contain a significant proportion of ion features with no mass spectral match to known compounds. Manual and tool-assisted natural annotation propagation is readily used to classify molecular networks; however, currently no annotation propagation tools leverage consensus confidence strategies enabled by hierarchical chemical ontologies or enable the use of new <i>in silico</i> tools without significant modification. Herein we present ConCISE (Consensus Classifications of <i>In Silico</i> Elucidations) which is the first tool to fuse molecular networking, spectral library matching and <i>in silico</i> class predictions to establish accurate putative classifications for"],"journal":["Metabolites"],"pubmed_title":["ConCISE: Consensus Annotation Propagation of Ion Features in Untargeted Tandem Mass Spectrometry Combining Molecular Networking and <i>In Silico</i> Metabolite Structure Prediction."],"pmcid":["PMC9786801"],"funding_grant_id":["MMI 6920","P01-ES021921","BLSK201676272","2023298","OCE-1155269","2019252845","OCE-1313747"],"pubmed_authors":["Petras D","Aluwihare LI","Quinlan ZA","Nelson CE","Dorrestein PC","Koester I","Wegley Kelly L","Aron AT"],"additional_accession":[]},"is_claimable":false,"name":"ConCISE: Consensus Annotation Propagation of Ion Features in Untargeted Tandem Mass Spectrometry Combining Molecular Networking and <i>In Silico</i> Metabolite Structure Prediction.","description":"Recent developments in molecular networking have expanded our ability to characterize the metabolome of diverse samples that contain a significant proportion of ion features with no mass spectral match to known compounds. Manual and tool-assisted natural annotation propagation is readily used to classify molecular networks; however, currently no annotation propagation tools leverage consensus confidence strategies enabled by hierarchical chemical ontologies or enable the use of new <i>in silico</i> tools without significant modification. Herein we present ConCISE (Consensus Classifications of <i>In Silico</i> Elucidations) which is the first tool to fuse molecular networking, spectral library matching and <i>in silico</i> class predictions to establish accurate putative classifications for","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Dec","modification":"2025-04-04T07:59:52.212Z","creation":"2025-04-04T07:59:52.212Z"},"accession":"S-EPMC9786801","cross_references":{"pubmed":["36557313"],"doi":["10.3390/metabo12121275"]}}