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. 2016 Jan 4;44(D1):D471-80.
doi: 10.1093/nar/gkv1164. Epub 2015 Nov 2.

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

Ron Caspi  1 , Richard Billington  1 , Luciana Ferrer  2 , Hartmut Foerster  3 , Carol A Fulcher  1 , Ingrid M Keseler  1 , Anamika Kothari  1 , Markus Krummenacker  1 , Mario Latendresse  1 , Lukas A Mueller  3 , Quang Ong  1 , Suzanne Paley  1 , Pallavi Subhraveti  1 , Daniel S Weaver  1 , Peter D Karp  4
Affiliations

The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases (V体育平台登录)

"V体育2025版" Ron Caspi et al. Nucleic Acids Res. .

Abstract

The MetaCyc database (MetaCyc VSports手机版. 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. .

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Figures

Figure 1.
Figure 1.
New electron transfer pathway diagrams enable connecting multiple reactions via any electron carrier. This type of diagram provides more information for electron transfer chains than does the typical metabolic pathway diagram, and clearly depicts the direction of the electron flow, the cell-compartment locations where the substrates are transformed, and the optional translocation of protons across membranes.
Figure 2.
Figure 2.
The new layout of the gene/protein page adds tabs, enabling the user to switch quickly among different data fields, including a summary, GO terms, essentiality data, protein features, operons, and references. A Show All tab places all the information on a single scrollable page.
Figure 3.
Figure 3.
Omics datasets containing multiple time points can be presented in the form of omics popups on individual full-detail pathway diagrams. The user can select from several layouts and drag the popups to modify their location.
Figure 4.
Figure 4.
The new pathway collage diagram combines multiple pathway diagrams for a selected set of pathways. Users can edit pathway collages within their web browser, such as by adding additional pathways; zooming in or out; manually moving pathways or metabolite nodes; showing or hiding connections; highlighting objects of interest; and displaying omics data (transcriptomics data are shown in this image).
Figure 5.
Figure 5.
Adding MIGS [Minimal Information about a (Meta)Genome Sequence] data to PGDBs enables searching the organism list for organisms that match particular phenotypes. In this example, BioCyc was queried for PGDBs for organisms that are facultative in respect to oxygen, retrieving 327 matches.
Figure 6.
Figure 6.
The new BioCyc app, which works on iOS devices, enables users to access any database in BioCyc, and any database in any other Pathway-Tools-based website that is running version 18.5 or later of Pathway Tools. The app lets users select a PGDB, query it for genes, and display related information, such as gene/protein data, catalyzed reactions, and metabolic pathways that the gene products participates in.
See this image and copyright information in PMC

VSports在线直播 - References

    1. Caspi R., Altman T., Billington R., Dreher K., Foerster H., Fulcher C.A., Holland T.A., Keseler I.M., Kothari A., Kubo A., et al. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases. Nucleic Acids Res. 2014;42:D459–471. - PMC (V体育安卓版) - PubMed
    1. Karp P.D., Paley S.M., Krummenacker M., Latendresse M., Dale J.M., Lee T.J., Kaipa P., Gilham F., Spaulding A., Popescu L., et al. Pathway Tools version 13.0: integrated software for pathway/genome informatics and systems biology. Brief Bioinform. 2010;11:40–79. - PMC - PubMed
    1. Dale J.M., Popescu L., Karp P.D. Machine learning methods for metabolic pathway prediction. BMC Bioinformatics. 2010;11:15. - PMC (V体育官网入口) - PubMed
    1. Jankowski M.D., Henry C.S., Broadbelt L.J., Hatzimanikatis V. Group contribution method for thermodynamic analysis of complex metabolic networks. Biophys. J. 2008;95:1487–1499. - VSports注册入口 - PMC - PubMed
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