Einzeltreffer — DigiBib

Biological interpretation of metabolomic datasets often ends at a pathway analysis step to find the over-represented metabolic pathways in the list of statistically significant metabolites. However, definitions of biochemical pathways and metabolite coverage vary among different curated databases, leading to missed interpretations. For the lists of genes, transcripts and proteins, Gene Ontology (GO) terms over-presentation analysis has become a standardized approach for biological interpretation. But, GO analysis has not been achieved for metabolomic datasets. We present a new knowledgebase (KB) and the online tool, Gene Ontology Analysis by the Integrated Data Science Laboratory for Metabolomics and Exposomics (IDSL.GOA) to conduct GO over-representation analysis for a metabolite list. The IDSL.GOA KB covers 2393 metabolic GO terms and associated 3144 genes, 1,492 EC annotations, and 2621 metabolites. IDSL.GOA analysis of a case study of older versus young female brain cortex metabolome highlighted 82 GO terms being significantly overrepresented (FDR < 0.05). We showed how IDSL.GOA identified key and relevant GO metabolic processes that were not yet covered in other pathway databases. Overall, we suggest that interpretation of metabolite lists should not be limited to only pathway maps and can also leverage GO terms as well. IDSL.GOA provides a useful tool for this purpose, allowing for a more comprehensive and accurate analysis of metabolite pathway data. IDSL.GOA tool can be accessed at https://goa.idsl.me/ .

Titel:	IDSL.GOA: gene ontology analysis for interpreting metabolomic datasets.
Autor/in / Beteiligte Person:	Mahajan, P ; Fiehn, O ; Barupal, D
Link:	Full Text Finder (Volltext)
Zeitschrift:	Scientific reports, Jg. 14 (2024-01-14), Heft 1, S. 1299
Veröffentlichung:	London : Nature Publishing Group, copyright 2011-, 2024
Medientyp:	academicJournal
ISSN:	2045-2322 (electronic)
DOI:	10.1038/s41598-024-51992-x
Schlagwort:	Female Humans Gene Ontology Databases, Factual Computational Biology Metabolomics Proteins genetics
Sonstiges:	Nachgewiesen in: MEDLINE Sprachen: English Publication Type: Journal Article Language: English [Sci Rep] 2024 Jan 14; Vol. 14 (1), pp. 1299. <i>Date of Electronic Publication: </i>2024 Jan 14. MeSH Terms: Metabolomics* ; Proteins* / genetics ; Female ; Humans ; Gene Ontology ; Databases, Factual ; Computational Biology Comments: Update of: bioRxiv. 2024 Jan 05;:. (PMID: 37034715) References: Ding, J. et al. A metabolome atlas of the aging mouse brain. Nat. Commun. 12(1), 6021. https://doi.org/10.1038/s41467-021-26310-y (2021). (PMID: 10.1038/s41467-021-26310-y346548188519999) ; Byeon, S. K. et al. Development of a multiomics model for identification of predictive biomarkers for COVID-19 severity: A retrospective cohort study. Lancet Digit. Health 4(9), e632–e645. https://doi.org/10.1016/S2589-7500(22)00112-1 (2022). (PMID: 10.1016/S2589-7500(22)00112-1358357129273185) ; Wieder, C. et al. 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Rep. 41(13), 111891. https://doi.org/10.1016/j.celrep.2022.111891 (2022). (PMID: 10.1016/j.celrep.2022.11189136577384) Grant Information: U24 ES035386 United States ES NIEHS NIH HHS; UL1 TR004419 United States TR NCATS NIH HHS; UL1TR004419 United States GF NIH HHS; U24ES035386 United States ES NIEHS NIH HHS Substance Nomenclature: 0 (Proteins) Entry Date(s): Date Created: 20240114 Date Completed: 20240116 Latest Revision: 20240210 Update Code: 20240210 PubMed Central ID: PMC10788336

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IDSL.GOA: gene ontology analysis for interpreting metabolomic datasets.