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ABSTRACT: Background The MetaCyc and KEGG projects have developed large metabolic pathway databases that are usedfor a variety of applications including genome analysis and metabolic engineering. We present acomparison of the compound, reaction, and pathway content of MetaCyc version 16.0 and a KEGGversion downloaded on Feb-27-2012 to increase understanding of their relative sizes, their degree ofoverlap, and their scope. To assess their overlap, we must know the correspondences between compounds,reactions, and pathways in MetaCyc, and those in KEGG. We devoted significant effort tocomputational and manual matching of these entities, and we evaluated the accuracy of the correspondences.Results KEGG contains 179 module pathways versus 1,846 base pathways in MetaCyc; KEGG contains 237map pathways versus 296 super pathways in MetaCyc. KEGG pathways contain 3.3 times as manyreactions on average as do MetaCyc pathways, and the databases employ different conceptualizationsof metabolic pathways. KEGG contains 8,692 reactions versus 10,262 for MetaCyc. 6,174 KEGGreactions are components of KEGG pathways versus 6,348 for MetaCyc. KEGG contains 16,586 compoundsversus 11,991 for MetaCyc. 6,912 KEGG compounds act as substrates in KEGG reactionsversus 8,891 for MetaCyc. MetaCyc contains a broader set of database attributes than does KEGG,such as relationships from a compound to enzymes that it regulates, identification of spontaneousreactions, and the expected taxonomic range of metabolic pathways. MetaCyc contains many pathwaysnot found in KEGG, from plants, fungi, metazoa, and actinobacteria; KEGG contains pathways not found in MetaCyc, for xenobiotic degradation, glycan metabolism, and metabolism of terpenoidsand polyketides. MetaCyc contains fewer unbalanced reactions, which facilitates metabolic modelingsuch as using flux-balance analysis. MetaCyc includes generic reactions that may be instantiatedcomputationally.Conclusions KEGG contains significantly more compounds than does MetaCyc, whereas MetaCyc contains significantlymore reactions and pathways than does KEGG, in particular KEGG modules are quiteincomplete. The number of reactions occurring in pathways in the two DBs are quite similar.
BMC Bioinformatics 03/2013; 14(1):112. · 2.75 Impact Factor
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ABSTRACT: Pathway databases collect the bioreactions and molecular interactions that define the processes of life. The MetaCyc family of pathway databases consists of thousands of databases that were derived through computational inference of metabolic pathways from the MetaCyc pathway/genome database (PGDB). In some cases, these DBs underwent subsequent manual curation. Curated pathway DBs are now available for most of the major model organisms. Databases in the MetaCyc family are managed using the Pathway Tools software. This chapter presents methods for performing data mining on the MetaCyc family of pathway DBs. We discuss the major data access mechanisms for the family, which include data files in multiple formats; application programming interfaces (APIs) for the Lisp, Java, and Perl languages; and web services. We present an overview of the Pathway Tools schema, an understanding of which is needed to query the DBs. The chapter also presents several interactive data mining tools within Pathway Tools for performing omics data analysis.
Methods in molecular biology (Clifton, N.J.) 01/2013; 939:183-200.
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Ron Caspi, Tomer Altman,
Kate Dreher,
Carol A Fulcher,
Pallavi Subhraveti,
Ingrid M Keseler,
Anamika Kothari,
Markus Krummenacker,
Mario Latendresse,
Lukas A Mueller,
Quang Ong,
Suzanne Paley,
Anuradha Pujar,
Alexander G Shearer,
Michael Travers,
Deepika Weerasinghe,
Peifen Zhang,
Peter D Karp
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ABSTRACT: The MetaCyc database (http://metacyc.org/) provides a comprehensive and freely accessible resource for metabolic pathways and enzymes from all domains of life. The pathways in MetaCyc are experimentally determined, small-molecule metabolic pathways and are curated from the primary scientific literature. MetaCyc contains more than 1800 pathways derived from more than 30,000 publications, and is the largest curated collection of metabolic pathways currently available. Most reactions in MetaCyc pathways are linked to one or more well-characterized enzymes, and both pathways and enzymes are annotated with reviews, evidence codes and literature citations. BioCyc (http://biocyc.org/) is a collection of more than 1700 organism-specific Pathway/Genome Databases (PGDBs). Each BioCyc PGDB contains the full genome and predicted metabolic network of one organism. The network, which is predicted by the Pathway Tools software using MetaCyc as a reference database, consists of metabolites, enzymes, reactions and metabolic pathways. BioCyc PGDBs contain additional features, including predicted operons, transport systems and pathway-hole fillers. The BioCyc website and Pathway Tools software offer many tools for querying and analysis of PGDBs, including Omics Viewers and comparative analysis. New developments include a zoomable web interface for diagrams; flux-balance analysis model generation from PGDBs; web services; and a new tool called Web Groups.
Nucleic Acids Research 11/2011; 40(Database issue):D742-53. · 8.03 Impact Factor
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Ingrid M. Keseler,
Julio Collado-Vides,
Alberto Santos-Zavaleta,
Martín Peralta-Gil,
Socorro Gama-Castro,
Luis Muñiz-Rascado,
César Bonavides-Martínez,
Suzanne M. Paley,
Markus Krummenacker, Tomer Altman, [......],
Aaron Spaulding,
John Pacheco,
Mario Latendresse,
Carol A. Fulcher,
Malabika Sarker,
Alexander Glennon Shearer,
Amanda Mackie,
Ian T. Paulsen,
Robert P. Gunsalus,
Peter D. Karp
Nucleic Acids Research. 01/2011; 39:583-590.
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Ingrid M Keseler,
Julio Collado-Vides,
Alberto Santos-Zavaleta,
Martin Peralta-Gil,
Socorro Gama-Castro,
Luis Muñiz-Rascado,
César Bonavides-Martinez,
Suzanne Paley,
Markus Krummenacker, Tomer Altman, [......],
Aaron Spaulding,
John Pacheco,
Mario Latendresse,
Carol Fulcher,
Malabika Sarker,
Alexander G Shearer,
Amanda Mackie,
Ian Paulsen,
Robert P Gunsalus,
Peter D Karp
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ABSTRACT: EcoCyc (http://EcoCyc.org) is a comprehensive model organism database for Escherichia coli K-12 MG1655. From the scientific literature, EcoCyc captures the functions of individual E. coli gene products; their regulation at the transcriptional, post-transcriptional and protein level; and their organization into operons, complexes and pathways. EcoCyc users can search and browse the information in multiple ways. Recent improvements to the EcoCyc Web interface include combined gene/protein pages and a Regulation Summary Diagram displaying a graphical overview of all known regulatory inputs to gene expression and protein activity. The graphical representation of signal transduction pathways has been updated, and the cellular and regulatory overviews were enhanced with new functionality. A specialized undergraduate teaching resource using EcoCyc is being developed.
Nucleic Acids Research 01/2011; 39(Database issue):D583-90. · 8.03 Impact Factor
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Peter D Karp,
Suzanne M Paley,
Markus Krummenacker,
Mario Latendresse,
Joseph M Dale,
Thomas J Lee,
Pallavi Kaipa,
Fred Gilham,
Aaron Spaulding,
Liviu Popescu, Tomer Altman,
Ian Paulsen,
Ingrid M Keseler,
Ron Caspi
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ABSTRACT: Pathway Tools is a production-quality software environment for creating a type of model-organism database called a Pathway/Genome Database (PGDB). A PGDB such as EcoCyc integrates the evolving understanding of the genes, proteins, metabolic network and regulatory network of an organism. This article provides an overview of Pathway Tools capabilities. The software performs multiple computational inferences including prediction of metabolic pathways, prediction of metabolic pathway hole fillers and prediction of operons. It enables interactive editing of PGDBs by DB curators. It supports web publishing of PGDBs, and provides a large number of query and visualization tools. The software also supports comparative analyses of PGDBs, and provides several systems biology analyses of PGDBs including reachability analysis of metabolic networks, and interactive tracing of metabolites through a metabolic network. More than 800 PGDBs have been created using Pathway Tools by scientists around the world, many of which are curated DBs for important model organisms. Those PGDBs can be exchanged using a peer-to-peer DB sharing system called the PGDB Registry.
Briefings in Bioinformatics 12/2009; 11(1):40-79. · 5.20 Impact Factor
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Ron Caspi, Tomer Altman,
Joseph M Dale,
Kate Dreher,
Carol A Fulcher,
Fred Gilham,
Pallavi Kaipa,
Athikkattuvalasu S Karthikeyan,
Anamika Kothari,
Markus Krummenacker,
Mario Latendresse,
Lukas A Mueller,
Suzanne Paley,
Liviu Popescu,
Anuradha Pujar,
Alexander G Shearer,
Peifen Zhang,
Peter D Karp
[show abstract]
[hide abstract]
ABSTRACT: The MetaCyc database (MetaCyc.org) is a comprehensive and freely accessible resource for metabolic pathways and enzymes from all domains of life. The pathways in MetaCyc are experimentally determined, small-molecule metabolic pathways and are curated from the primary scientific literature. With more than 1400 pathways, MetaCyc is the largest collection of metabolic pathways currently available. Pathways reactions are linked to one or more well-characterized enzymes, and both pathways and enzymes are annotated with reviews, evidence codes, and literature citations. BioCyc (BioCyc.org) is a collection of more than 500 organism-specific Pathway/Genome Databases (PGDBs). Each BioCyc PGDB contains the full genome and predicted metabolic network of one organism. The network, which is predicted by the Pathway Tools software using MetaCyc as a reference, consists of metabolites, enzymes, reactions and metabolic pathways. BioCyc PGDBs also contain additional features, such as predicted operons, transport systems, and pathway hole-fillers. The BioCyc Web site offers several tools for the analysis of the PGDBs, including Omics Viewers that enable visualization of omics datasets on two different genome-scale diagrams and tools for comparative analysis. The BioCyc PGDBs generated by SRI are offered for adoption by any party interested in curation of metabolic, regulatory, and genome-related information about an organism.
Nucleic Acids Research 10/2009; 38(Database issue):D473-9. · 8.03 Impact Factor
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