Published online 7 August 2006
Nucleic Acids Research, 2006, Vol. 34, No. 13 3687-3697
© 2006 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commerical use, distribution, and reproduction in any medium, provided the original work is properly cited.
Article |
The outcomes of pathway database computations depend on pathway ontology
Bioinformatics Research Group, Artificial Intelligence Center, SRI International Menlo Park, CA 94025, USA
*To whom correspondence should be addressed. Tel: +1 650 859 4358; Fax: +1 650 859 3735; Email: pkarp{at}ai.sri.com
*Correspondence may also be addressed to M. L. Green. Tel: +1 650 859 5669; Fax: +1 650 859 3735; Email: green{at}ai.sri.com
Received March 10, 2006. Revised June 3, 2006. Accepted June 5, 2006.
Different biological notions of pathways are used in different pathway databases. Those pathway ontologies significantly impact pathway computations. Computational users of pathway databases will obtain different results depending on the pathway ontology used by the databases they employ, and different pathway ontologies are preferable for different end uses. We explore differences in pathway ontologies by comparing the BioCyc and KEGG ontologies. The BioCyc ontology defines a pathway as a conserved, atomic module of the metabolic network of a single organism, i.e. often regulated as a unit, whose boundaries are defined at high-connectivity stable metabolites. KEGG pathways are on average 4.2 times larger than BioCyc pathways, and combine multiple biological processes from different organisms to produce a substrate-centered reaction mosaic. We compared KEGG and BioCyc pathways using genome context methods, which determine the functional relatedness of pairs of genes. For each method we employed, a pair of genes randomly selected from a BioCyc pathway is more likely to be related by that method than is a pair of genes randomly selected from a KEGG pathway, supporting the conclusion that the BioCyc pathway conceptualization is closer to a single conserved biological process than is that of KEGG.
![]()
CiteULike
Connotea
Del.icio.us What's this?
This article has been cited by other articles:
![]() |
A. Lysenko, M. M. Hindle, J. Taubert, M. Saqi, and C. J. Rawlings Data integration for plant genomics--exemplars from the integration of Arabidopsis thaliana databases Brief Bioinform, November 1, 2009; 10(6): 676 - 693. [Abstract] [Full Text] [PDF] |
||||
![]() |
A. S.N. Seshasayee, G. M. Fraser, M. M. Babu, and N. M. Luscombe Principles of transcriptional regulation and evolution of the metabolic system in E. coli Genome Res., January 1, 2009; 19(1): 79 - 91. [Abstract] [Full Text] [PDF] |
||||
![]() |
E. Borenstein, M. Kupiec, M. W. Feldman, and E. Ruppin Large-scale reconstruction and phylogenetic analysis of metabolic environments PNAS, September 23, 2008; 105(38): 14482 - 14487. [Abstract] [Full Text] [PDF] |
||||
![]() |
R. Caspi, H. Foerster, C. A. Fulcher, P. Kaipa, M. Krummenacker, M. Latendresse, S. Paley, S. Y. Rhee, A. G. Shearer, C. Tissier, et al. The MetaCyc Database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases Nucleic Acids Res., January 11, 2008; 36(suppl_1): D623 - D631. [Abstract] [Full Text] [PDF] |
||||
![]() |
M. L. Green and P. D. Karp Using genome-context data to identify specific types of functional associations in pathway/genome databases Bioinformatics, July 1, 2007; 23(13): i205 - i211. [Abstract] [Full Text] [PDF] |
||||




