Expansion of the BioCyc collection of pathway/genome databases to 160 genomes

By Peter D. Karp, Christos A. Ouzounis, C. Moore-Kochlacs, Leon Goldovsky, Pallavi Kaipa, D. Ahrén, Sophia Tsoka, Nikos Darzentas, Victor Kunin, Núria López-Bigas. Nucleic Acids Research, 2005

665 citations recorded by the source corpus.

The BioCyc database collection is a set of 160 pathway/genome databases (PGDBs) for most eukaryotic and prokaryotic species whose genomes have been completely sequenced to date. Each PGDB in the BioCyc collection describes the genome and predicted metabolic network of a single organism, inferred from the MetaCyc database, which is a reference source on metabolic pathways from multiple organisms. In addition, each bacterial PGDB includes predicted operons for the corresponding species. The BioCyc collection provides a unique resource for computational systems biology, namely global and comparative analyses of genomes and metabolic networks, and a supplement to the BioCyc resource of curated PGDBs. The Omics viewer available through the BioCyc website allows scientists to visualize combinations of gene expression, proteomics and metabolomics data on the metabolic maps of these organisms. This paper discusses the computational methodology by which the BioCyc collection has been expanded, and presents an aggregate analysis of the collection that includes the range of number of pathways present in these organisms, and the most frequently observed pathways. We seek scientists to adopt and curate individual PGDBs within the BioCyc collection. Only by harnessing the expertise of many scientists we can hope to produce biological databases, which accurately reflect the depth and breadth of knowledge that the biomedical research community is producing.

Subfields: Molecular Biology, Pharmacology

View Paper: https://www.semanticscholar.org/paper/f2007cb8eccb12f7f835ae66f3e4b001756151cc

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Citations in the corpus

Teleost fish with specific genome duplication as unique models of vertebrate evolution (2010, background): “There are increasing database resources include information on metabolic, signal transduction, and other biological pathways which would be an important basis for studying phenotypic evolution; e.g., KEGG (Kanehisa et al. 2004), BioCyc (Karp et al. 2005), and CellCircuits (Mak et al. 2007).”
Biomedical ontologies: a functional perspective (2007, methodology): “Currently, leading pathway resources such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) [32], BioCyc [33] and Reactome [34] make their data available in BioPAX format and BioPAX viewers are available as additional modules in pathway analysis tools such as PATIKA [35] and Cytoscape [36].”
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MSEA: a web-based tool to identify biologically meaningful patterns in quantitative metabolomic data (2010, methodology): “Information contained in the HMDB was used to extract common names, synonyms, as well as ID used in nine major metabolomic databases [HMDB, PubChem (19), ChEBI (20), KEGG (21), BiGG (22), METLIN (23), BioCyc (24), Reactome (25), and Wikipedia].”
The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored (2010, background): “Annotated pathway knowledge is mostly kept in a separate set of resources (22–24) and yet other interactions can be found in various organism-speciﬁc databases (25,26) or text-mining resources (27,28).”
The NCBI BioSystems database (2009, background): “Fortunately, a considerable number of resources have been created to address this issue: the Pathguide (1) resource lists nearly 300 pathway resources alone, including KEGG (2), Reactome (3), PID (4), PharmGKB (5), GenMAPP (6), Biocyc (7) and many others.”
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