The MetaCyc Database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases

By Ron Caspi, Tomer Altman, Kate Dreher, Carol A. Fulcher, Pallavi Subhraveti, I. Keseler, Anamika Kothari, Markus Krummenacker, Mario Latendresse, Lukas A. Mueller and 8 more. Nucleic Acids Res., 2007

777 citations recorded by the source corpus.

MetaCyc (MetaCyc.org) is a universal database of metabolic pathways and enzymes from all domains of life. The pathways in MetaCyc are curated from the primary scientific literature, and are experimentally determined small-molecule metabolic pathways. Each reaction in a MetaCyc pathway is annotated with one or more well-characterized enzymes. Because MetaCyc contains only experimentally elucidated knowledge, it provides a uniquely high-quality resource for metabolic pathways and enzymes. BioCyc (BioCyc.org) is a collection of more than 350 organism-specific Pathway/Genome Databases (PGDBs). Each BioCyc PGDB contains the predicted metabolic network of one organism, including metabolic pathways, enzymes, metabolites and reactions predicted by the Pathway Tools software using MetaCyc as a reference database. BioCyc PGDBs also contain predicted operons and predicted pathway hole fillers—predictions of which enzymes may catalyze pathway reactions that have not been assigned to an enzyme. The BioCyc website offers many tools for computational analysis of PGDBs, including comparative analysis and analysis of omics data in a pathway context. The BioCyc PGDBs generated by SRI are offered for adoption by any interested party for the ongoing integration of metabolic and genome-related information about an organism.

Subfields: Biomedical Engineering, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/a7ece6b4ad1f1688ba8afe3720e7f6942ec68f58

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

In Molecular Biology

Citations in the corpus

Pathway enrichment analysis and visualization of omics data using g:Profiler, GSEA, Cytoscape and EnrichmentMap (2019, methodology): “This ﬁ le contains pathways downloaded on 1 July 2017 from eight data sources: GO 57 , Reactome 58 , Panther 38 , NetPath 60 , NCI 79 , MSigDB curated gene sets (C2 collection, excluding Reactome and KEGG) 80 , MSigDB Hallmark (H collection) 81 and HumanCyc 59 .”
The neuroactive potential of the human gut microbiota in quality of life and depression (2019, neutral): “A metabolic reconstruction framework specific for translating shotgun metagenomic data into microbial neuroactive metabolic potential was assembled based on extensive literature ( > 300 peer-reviewed papers) and database (MetaCyc 73 ) review.”
Identification of bioactive metabolites using activity metabolomics (2019, positive): “…linked to unique metabolite identifiers, and involves combining previously generated pathway information (e.g. KEGG [G] , Reactome [G], Biocyc pathways [G ], Recon [G] or metabolomic set enrichments (available via MetaboAnalyst platform), and the newly generated ChemRICH platform 97–101 , 102 ).”
Fate of low molecular weight organic substances in an arable soil: From microbial uptake to utilisation and stabilisation (2014, background): “…by the basic glycolysis pathway (Caspi et al., 2012; Keseler et al., 2009), carboxylic acids enter from side branches of the citric acid cycle (Caspi et al., 2012; Keseler et al., 2009), and amino acids enter glycolysis or the citric acid cycle from individual side branches at different steps…”
BioTransformer: a comprehensive computational tool for small molecule metabolism prediction and metabolite identification (2019, methodology): “…was gathered from resources such as the SIB Bioinformatics Resource Portal (ExPASy) [68], the BRENDA enzyme database [69], various Cyc databases [70], the UniProt knowl-edgebase (UniProtKB) [71], the KEGG database [47], and enzyme nomenclature information provided by the International Union of…”
STITCH 4: integration of protein–chemical interactions with user data (2013, background): “The second channel is made up of manually curated drug–target databases: DrugBank (12), GLIDA (13), Matador (14), TTD (15) and CTD (16); and pathway databases: KEGG (17), NCI/Nature Pathway Interaction Database (18), Reactome (19) and BioCyc (20) .”
The human gut microbiome of early onset type 1 diabetes in the TEDDY study (2018, positive): “5c) and less than 10% based on more functionally-specific MetaCyc pathways (Extended Data Fig.”
A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism (2019, methodology): “The reaction reversibility was corrected based on the BioCyc and BiGG databases 58 .”