Updates in Rhea: SPARQLing biochemical reaction data

Abstract Rhea (http://www.rhea-db.org) is a comprehensive and non-redundant resource of over 11 000 expert-curated biochemical reactions that uses chemical entities from the ChEBI ontology to represent reaction participants. Originally designed as an annotation vocabulary for the UniProt Knowledgebase (UniProtKB), Rhea also provides reaction data for a range of other core knowledgebases and data repositories including ChEBI and MetaboLights. Here we describe recent developments in Rhea, focusing on a new resource description framework representation of Rhea reaction data and an SPARQL endpoint (https://sparql.rhea-db.org/sparql) that provides access to it. We demonstrate how federated queries that combine the Rhea SPARQL endpoint and other SPARQL endpoints such as that of UniProt can provide improved metabolite annotation and support integrative analyses that link the metabolome through the proteome to the transcriptome and genome. These developments will significantly boost the utility of Rhea as a means to link chemistry and biology for a more holistic understanding of biological systems and their function in health and disease.

[1]  Anne Morgat,et al.  Updates in Rhea – an expert curated resource of biochemical reactions , 2016, Nucleic Acids Res..

[2]  Robert E. W. Hancock,et al.  MetaBridge: enabling network-based integrative analysis via direct protein interactors of metabolites , 2018, Bioinform..

[3]  T. Kurzchalia,et al.  Stereoselective synthesis and hormonal activity of novel dafachronic acids and naturally occurring steroids isolated from corals. , 2012, Organic & biomolecular chemistry.

[4]  Gabi Kastenmüller,et al.  Biochemical insights from population studies with genetics and metabolomics. , 2016, Archives of biochemistry and biophysics.

[5]  Janet M. Thornton,et al.  Mechanism and Catalytic Site Atlas (M-CSA): a database of enzyme reaction mechanisms and active sites , 2017, Nucleic Acids Res..

[6]  Thawfeek M. Varusai,et al.  The Reactome Pathway Knowledgebase , 2017, Nucleic acids research.

[7]  Matej Oresic,et al.  Metabolomics enables precision medicine: “A White Paper, Community Perspective” , 2016, Metabolomics.

[8]  Christoph Steinbeck,et al.  ChEBI in 2016: Improved services and an expanding collection of metabolites , 2015, Nucleic Acids Res..

[9]  Andrew G. McDonald,et al.  ExplorEnz: the primary source of the IUBMB enzyme list , 2008, Nucleic Acids Res..

[10]  Philip Miller,et al.  BiGG Models: A platform for integrating, standardizing and sharing genome-scale models , 2015, Nucleic Acids Res..

[11]  Alan Bridge,et al.  The SwissLipids knowledgebase for lipid biology , 2015, Bioinform..

[12]  David S. Wishart,et al.  HMDB 4.0: the human metabolome database for 2018 , 2017, Nucleic Acids Res..

[13]  Cathy H. Wu,et al.  UniProt: the Universal Protein knowledgebase , 2004, Nucleic Acids Res..

[14]  Rolf Apweiler,et al.  IntEnz, the integrated relational enzyme database , 2004, Nucleic Acids Res..

[15]  Gary Siuzdak,et al.  Metabolomics activity screening for identifying metabolites that modulate phenotype , 2018, Nature Biotechnology.

[16]  Christoph Steinbeck,et al.  MetaboLights: An Open‐Access Database Repository for Metabolomics Data , 2016, Current protocols in bioinformatics.

[17]  Elisabeth Coudert,et al.  HAMAP in 2015: updates to the protein family classification and annotation system , 2014, Nucleic Acids Res..

[18]  Ioannis Xenarios,et al.  Plasma Dihydroceramides Are Diabetes Susceptibility Biomarker Candidates in Mice and Humans. , 2017, Cell reports.

[19]  Konrad J. Karczewski,et al.  Integrative omics for health and disease , 2018, Nature Reviews Genetics.

[20]  Larry Smarr,et al.  Creating a 3D microbial and chemical snapshot of a human habitat , 2018, Scientific Reports.

[21]  Olivier Martin,et al.  MetaNetX/MNXref – reconciliation of metabolites and biochemical reactions to bring together genome-scale metabolic networks , 2015, Nucleic Acids Res..

[22]  S. Rosanoff,et al.  An update on the Enzyme Portal: an integrative approach for exploring enzyme knowledge , 2017, Protein engineering, design & selection : PEDS.

[23]  Evan Bolton,et al.  ClassyFire: automated chemical classification with a comprehensive, computable taxonomy , 2016, Journal of Cheminformatics.

[24]  Minoru Kanehisa,et al.  KEGG: new perspectives on genomes, pathways, diseases and drugs , 2016, Nucleic Acids Res..

[25]  B. Palsson,et al.  A protocol for generating a high-quality genome-scale metabolic reconstruction , 2010 .

[26]  Markus Krummenacker,et al.  The MetaCyc database of metabolic pathways and enzymes , 2017, Nucleic acids research.