The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases

The MetaCyc database (MetaCyc.org) is a freely accessible comprehensive database describing metabolic pathways and enzymes from all domains of life. The majority of MetaCyc pathways are small-molecule metabolic pathways that have been experimentally determined. MetaCyc contains more than 2400 pathways derived from >46 000 publications, and is the largest curated collection of metabolic pathways. BioCyc (BioCyc.org) is a collection of 5700 organism-specific Pathway/Genome Databases (PGDBs), each containing the full genome and predicted metabolic network of one organism, including metabolites, enzymes, reactions, metabolic pathways, predicted operons, transport systems, and pathway-hole fillers. The BioCyc website offers a variety of tools for querying and analyzing PGDBs, including Omics Viewers and tools for comparative analysis. This article provides an update of new developments in MetaCyc and BioCyc during the last two years, including addition of Gibbs free energy values for compounds and reactions; redesign of the primary gene/protein page; addition of a tool for creating diagrams containing multiple linked pathways; several new search capabilities, including searching for genes based on sequence patterns, searching for databases based on an organism's phenotypes, and a cross-organism search; and a metabolite identifier translation service.

[1]  Peter D. Karp,et al.  Discovering novel subsystems using comparative genomics , 2011, Bioinform..

[2]  B. Palsson,et al.  Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110 , 1994, Applied and environmental microbiology.

[3]  Peter D. Karp,et al.  A systematic study of genome context methods: calibration, normalization and combination , 2010, BMC Bioinformatics.

[4]  Melissa J. Landrum,et al.  RefSeq: an update on mammalian reference sequences , 2013, Nucleic Acids Res..

[5]  Joshua A. Lerman,et al.  COBRApy: COnstraints-Based Reconstruction and Analysis for Python , 2013, BMC Systems Biology.

[6]  C. Mathews.  Thermodynamics of biochemical reactions , 2004 .

[7]  Alex H. Lang,et al.  Metabolic resource allocation in individual microbes determines ecosystem interactions and spatial dynamics. , 2014, Cell reports.

[8]  Peter D. Karp,et al.  Machine learning methods for metabolic pathway prediction , 2010 .

[9]  Peter D. Karp,et al.  Pathway Tools version 19.0 update: software for pathway/genome informatics and systems biology , 2016, Briefings Bioinform..

[10]  Christoph Steinbeck,et al.  The ChEBI reference database and ontology for biologically relevant chemistry: enhancements for 2013 , 2012, Nucleic Acids Res..

[11]  Matthew D. Jankowski,et al.  Group contribution method for thermodynamic analysis of complex metabolic networks. , 2008, Biophysical journal.

[12]  Radhakrishnan Mahadevan,et al.  Genome-scale dynamic modeling of the competition between Rhodoferax and Geobacter in anoxic subsurface environments , 2011, The ISME Journal.

[13]  Evan Bolton,et al.  Database resources of the National Center for Biotechnology Information , 2017, Nucleic Acids Res..

[14]  Chris F. Taylor,et al.  The minimum information about a genome sequence (MIGS) specification , 2008, Nature Biotechnology.

[15]  Susumu Goto,et al.  Data, information, knowledge and principle: back to metabolism in KEGG , 2013, Nucleic Acids Res..

[16]  Peter D. Karp,et al.  Pathway Tools version 13.0: integrated software for pathway/genome informatics and systems biology , 2015, Briefings Bioinform..

[17]  Benjamin Hofner,et al.  opm: an R package for analysing OmniLog® phenotype microarray data , 2013, Bioinform..