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  • Poster presentation
  • P-III-0772

Linking the human metabolome and proteome in UniProtKB through Rhea

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Data Integration: With Bioinformatics to Biological Knowledge

Poster

Linking the human metabolome and proteome in UniProtKB through Rhea

Topic

  • Data Integration: With Bioinformatics to Biological Knowledge

Authors

Lionel Breuza (Geneva / CH), Lucila Aimo (Geneva / CH), Ghislaine Argoud-Puy (Geneva / CH), Kristian B. Axelsen (Geneva / CH), Cristina Casals-Casas (Geneva / CH), Elisabeth Coudert (Geneva / CH), Nadine Gruaz-Gumowski (Geneva / CH), Anne Morgat (Geneva / CH), Nevila Hyka-Nouspikel (Geneva / CH), Lucille Pourcel (Geneva / CH), Shyamala Sundaram (Geneva / CH), Alan Bridge (Geneva / CH), the UniProt Consortium (Geneva / CH; Cambridge / GB; Washington, DC / US)

Abstract

The UniProt Knowledgebase (UniProtKB, at www.uniprot.org) is a reference resource of protein sequences and functional annotation that is commonly used for proteomics, transcriptomics, and genomics analyses. Here we describe work that extends the domain of applications of UniProtKB to include integrated analyses of metabolomics and other chemical data, through the reannotation of all human enzyme and transporter functions using Rhea, an expert curated knowledgebase of biochemical reactions (www.rhea-db.org) based on the ChEBI ontology of small molecules (www.ebi.ac.uk/chebi/). With over 3,600 human enzymes and transporters now linked to Rhea (of 29 million sequences in UniProtKB linked to Rhea), this work is part of a wider effort to improve small molecule data in UniProtKB for all species that also includes annotation of ligand binding sites, and PTMs, using ChEBI. It improves interoperability with other data and knowledge resources dealing with small molecules, such as the metabolomics data repository MetaboLights, the protein structure repository PDB, the pathway knowledgebase Reactome, and the Gene Ontology, and provides enhanced support for metabolic modeling, multi-omics data integration and analysis, and the use of advanced machine learning approaches to predict enzyme function and biosynthetic and bioremediation pathways.

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