MetaCyc: a multiorganism database of metabolic pathways and enzymes

By Ron Caspi, Hartmut Foerster, Carol A. Fulcher, R. Hopkinson, J. Ingraham, Pallavi Kaipa, Markus Krummenacker, Suzanne M. Paley, J. Pick, Seung Yon Rhee and 3 more. Nucleic Acids Res., 2005

495 citations recorded by the source corpus.

MetaCyc is a database of metabolic pathways and enzymes located at . Its goal is to serve as a metabolic encyclopedia, containing a collection of non-redundant pathways central to small molecule metabolism, which have been reported in the experimental literature. Most of the pathways in MetaCyc occur in microorganisms and plants, although animal pathways are also represented. MetaCyc contains metabolic pathways, enzymatic reactions, enzymes, chemical compounds, genes and review-level comments. Enzyme information includes substrate specificity, kinetic properties, activators, inhibitors, cofactor requirements and links to sequence and structure databases. Data are curated from the primary literature by curators with expertise in biochemistry and molecular biology. MetaCyc serves as a readily accessible comprehensive resource on microbial and plant pathways for genome analysis, basic research, education, metabolic engineering and systems biology. Querying, visualization and curation of the database is supported by SRI's Pathway Tools software. The PathoLogic component of Pathway Tools is used in conjunction with MetaCyc to predict the metabolic network of an organism from its annotated genome. SRI and the European Bioinformatics Institute employed this tool to create pathway/genome databases (PGDBs) for 165 organisms, available at the website. These PGDBs also include predicted operons and pathway hole fillers.

Subfields: Biomedical Engineering, Molecular Biology, Pharmacology

View Paper: https://www.semanticscholar.org/paper/6084ffa1c52e5bc4b7265e3bf692e07bcf4b39b3

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Citations in the corpus

PANTHER version 6: protein sequence and function evolution data with expanded representation of biological pathways (2006, background): “KEGG and MetaCyc have collected mostly metabolic pathways, while STKE and Reactome contain well curated, publicly available data on signaling pathways.”
TransportDB: a comprehensive database resource for cytoplasmic membrane transport systems and outer membrane channels (2006, methodology): “As a starting point, we plan to use the hierarchical compound lists that are defined in the MetaCyc database (25).”
Biosynthetic pathways, gene replacement and the antiquity of life (2004, background): “The BioCyc database, a curated, literature-derived repository of nearly 5000 biochemical reactions from 160 species (Krieger et al., 2004), can be used to extend these arguments beyond exemplary anecdotes.”
STITCH: interaction networks of chemicals and proteins (2007, background): “Finally, there are many pathway databases most prominently KEGG (12), MetaCyc (13) and Reactome (14).”
Centering, scaling, and transformations: improving the biological information content of metabolomics data (2006, background): “As cellular metabolism is strongly interlinked (e.g. see [26,27]), it is expected that the concentrations of many metabolites are simultaneously affected when an organism is grown on a different carbon source.”
The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases (2009, background): “Generation of precursor metabolites and energy (124) Fermentation (34) Respiration (25) Chemoautotrophic Energy Metabolism (14) Methanogenesis (12) Electron Transfer (11) TCA cycle (9) Glycolysis (6) Photosynthesis (6) Pentose Phosphate Pathways (4) Other (3) Detoxification (16) Methylglyoxal Detoxi”
The Human Urine Metabolome (2013, background): “Each MetaboCard entry contains up to 120 data fields many of which are hyperlinked to other databases (KEGG [45], PubChem [46], MetaCyc [47], ChEBI [48], PDB [49], UniProt [50], and GenBank [51] as well as to GeneCard IDs [52], GeneAtlas IDs [53] and HGNC IDs [54] for each of the corresponding enzym”
EcoCyc: a comprehensive database resource for Escherichia coli (2004, background): “The exception is MetaCyc, which describes experimentally elucidated metabolic pathways from more than 240 organisms ( 2 ) and is the reference database for predicting pathways in other BioCyc PGDBs.”
ChEBI: a database and ontology for chemical entities of biological interest (2007, background): “Most biologists conveniently divide all biochemical compounds into ‘biopolymers’, which consist of macromolecules, and the rest, which consist of ‘small molecules’ (see, for instance, MetaCyc Taxonomy of Compounds (1) (http://biocyc.org/META/class-tree? object=Compounds).”
The genome of the blood fluke Schistosoma mansoni (2009, unknown): “An S. mansoni metabolic pathways database, SchistoCyc (http://schistocyc.schistodb.net/ptools), was created using the Pathway Tools software 83 , which contains algorithms to predict an organism’s metabolic pathways from its genome by comparison to MetaCyc, a reference pathways database 84 .”
OptStrain: a computational framework for redesign of microbial production systems. (2004, background): “At the same time, individual reactions are deposited in databases such as KEGG, EMP, MetaCyc, UM-BBD, and many more (Selkov Jr. et al. 1998; Overbeek et al. 2000; Karp et al. 2002; Ellis et al. 2003; Kanehisa et al. 2004; Krieger et al. 2004), forming encompassing and growing collections of the biot”
Recent Developments in Parameter Estimation and Structure Identification of Biochemical and Genomic Systems (2009, methodology): “This type of model construction has been benefiting greatly from databases like KEGG [87,88], MetaCyc [89], and Brenda [90], which contain large collections of pathway topologies and kinetic parameters retrieved from literature sources.”
A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information (2007, methodology): “(v) The content of iAF1260 and the EcoCyc (Keseler et al, 2005), release 10.6 and MetaCyc (Caspi et al, 2006) databases were compared to obtain a more accurate and comprehensive reconstruction.”
Genome-scale models of bacterial metabolism: reconstruction and applications (2008, methodology): “(2006) studied the unknown proton translocation stoichiometry of such a reaction in Methano-sarcina barkeri by determining for each media the model maintenance parameters that provided the best growth yield This model is an update of two earlier models for E. coli (Edwards & Palsson, 2000; Reed et a”
GMD@CSB.DB: the Golm Metabolome Database (2005, methodology): “For instance, databases like BRENDA (Schomburg et al., 2004), KEGG (Kanehisa et al., 2004) or Meta- Cyc (Krieger et al., 2004) harbour information concerning metabolic pathways, chemical reactions including inventories of the genes and enzymes involved.”
A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology (2008, unknown): “net/inchi" metaid="M_172_inchi"> InChI=1/C10H16N5O13P3/c11-8-5-9(13-2-12-8)15(3-14-5)10-7(17)6 (16)4(26-10)1-25-30(21,22)28-31(23,24)27-29(18,19)20/h2-4,6-7,10,16-17H,1H2, (H,21,22)(H,23,24)(H2,11,12,13)(H2,18,19,20)/t4-,6-,7-,10-/m1/s1/f/h18-19,21 ,23H,11H2 </in:inchi> <rdf:RDF xmlns:rdf="http://ww”