Nucleic Acids Research

Nucleic Acids Research 2006 34(Database Issue):D354-D357; doi:10.1093/nar/gkj102

This Article Abstract Print PDF (340K) Screen PDF (106K) Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Add to My Personal Archive Download to citation manager Commercial Re-use Guidelines for Open Access NAR Content Google Scholar Articles by Kanehisa, M. Articles by Hirakawa, M. Search for Related Content PubMed PubMed Citation Articles by Kanehisa, M. Articles by Hirakawa, M. Social Bookmarking          What's this?

Nucleic Acids Research, 2006, Vol. 34, Database issue D354-D357
© The Author 2006. Published by Oxford University Press. All rights reserved
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions{at}oxfordjournals.org

Article

From genomics to chemical genomics: new developments in KEGG

Minoru Kanehisa^1,2,*, Susumu Goto¹, Masahiro Hattori¹, Kiyoko F. Aoki-Kinoshita¹, Masumi Itoh¹, Shuichi Kawashima², Toshiaki Katayama², Michihiro Araki² and Mika Hirakawa^1,3

¹Bioinformatics Center, Institute for Chemical Research, Kyoto University Uji, Kyoto 611-0011, Japan ²Human Genome Center, Institute of Medical Science, University of Tokyo Minato-ku, Tokyo 108-8639, Japan ³Institute for Bioinformatics Research and Development, Japan Science and Technology Agency Chiyoda-ku, Tokyo 102-8666, Japan

^*To whom correspondence should be addressed. Tel: +81 774 38 3270; Fax: +81 774 38 3269; Email: kanehisa{at}kuicr.kyoto-u.ac.jp

Received September 14, 2005. Revised October 17, 2005. Accepted October 17, 2005.

	ABSTRACT

TOP ABSTRACT INTRODUCTION THE KEGG RESOURCE ACCESSING KEGG KEGG API REFERENCES

 
The increasing amount of genomic and molecular information is the basis for understanding higher-order biological systems, such as the cell and the organism, and their interactions with the environment, as well as for medical, industrial and other practical applications. The KEGG resource (http://www.genome.jp/kegg/) provides a reference knowledge base for linking genomes to biological systems, categorized as building blocks in the genomic space (KEGG GENES) and the chemical space (KEGG LIGAND), and wiring diagrams of interaction networks and reaction networks (KEGG PATHWAY). A fourth component, KEGG BRITE, has been formally added to the KEGG suite of databases. This reflects our attempt to computerize functional interpretations as part of the pathway reconstruction process based on the hierarchically structured knowledge about the genomic, chemical and network spaces. In accordance with the new chemical genomics initiatives, the scope of KEGG LIGAND has been significantly expanded to cover both endogenous and exogenous molecules. Specifically, RPAIR contains curated chemical structure transformation patterns extracted from known enzymatic reactions, which would enable analysis of genome-environment interactions, such as the prediction of new reactions and new enzyme genes that would degrade new environmental compounds. Additionally, drug information is now stored separately and linked to new KEGG DRUG structure maps.

	INTRODUCTION

TOP ABSTRACT INTRODUCTION THE KEGG RESOURCE ACCESSING KEGG KEGG API REFERENCES

 
While traditional genomics and other types of omics approaches have contributed to our knowledge on the genomic space of possible genes and proteins that make up the biological system, the new chemical genomics initiatives will give us a glimpse of the chemical space of possible chemical substances that exist as an interface between the biological world and the natural world. The KEGG database project was initiated in 1995, the last year of the first 5-year phase of the Japanese Human Genome Programme (1). After 10 years of development in parallel with the growing number of completely sequenced genomes and increased activities in post-genomic research, the KEGG project has entered a new phase in accordance with the chemical genomics initiatives.

KEGG is a database resource for understanding higher-order functions and utilities of the biological system, such as the cell or the organism, from genomic and molecular information. In fact, we consider KEGG as a computer representation of the biological system, consisting of building blocks and wiring diagrams, which can be used for modeling and simulation as well as for browsing and retrieval (2). Originally, the wiring diagrams involved endogenous molecules, both those that are directly encoded in the genome (proteins and RNAs) and those that are indirectly encoded through biosynthetic/biodegradation pathways (metabolites, glycans and so on). Now we are extending these wiring diagrams to include exogenous molecules. This will help understand interactions between the biological system and the natural environment, and would eventually lead to representation and reconstruction of another higher-level biological system, the biological world. Here we report new developments in KEGG towards this direction.

	THE KEGG RESOURCE

TOP ABSTRACT INTRODUCTION THE KEGG RESOURCE ACCESSING KEGG KEGG API REFERENCES

 
Overview
KEGG consists of four main databases. As illustrated in Figure 1 they are categorized as building blocks in the genomic space (GENES databases) and the chemical space (LIGAND database), wiring diagrams in the network space (PATHWAY database) and ontologies for pathway reconstruction (BRITE database). BRITE had been a separate database for many years, but it was formally included in KEGG in release 34.0 (April 2005) to establish a logical foundation for the KEGG Project. The URLs for accessing KEGG are summarized in Table 1.

View larger version (20K): [in this window] [in a new window]   Figure 1 The overall architecture of KEGG now consisting of four main components. KEGG BRITE has been formally added to establish a logical foundation for inference of higher-order functions.

 

View this table: [in this window] [in a new window]   Table 1 URLs for the KEGG resource

 
Biological systems are represented in KEGG by two types of graphs, called nested graphs and line graphs in theoretical computer science. The nested graph is a graph whose nodes can themselves be graphs. It is used for representing KEGG network hierarchy and for pathway reconstruction and functional inference. The line graph is a graph derived by interchanging nodes and edges of another graph. It represents the inherent complementarity of the metabolic pathway, which can be viewed either as a network of genes (enzymes) or as a network of compounds, meaning that one can be generated from the other by the line graph transformation. Thus, the line graph is the basis for integrated analysis of genomic and chemical information.

BRITE database
KEGG BRITE is a collection of hierarchies and binary relations with two inter-related objectives corresponding to the two types of graphs: to automate functional interpretations associated with the KEGG pathway reconstruction and to assist discovery of empirical rules involving genome-environment interactions. Currently, we focus on hierarchical structuring of our knowledge on functional aspects of the genomic and chemical spaces (Table 2), including the KEGG orthology (KO) system for ortholog/paralog gene groups, the reaction classification (RC) system for biochemical reactions, and other classifications for compounds and drugs tentatively called chemical ontology as shown in Figure 1. We plan to extend the KO system to include the definition of functional modules in the KEGG pathways and to develop ontologies for computational inference of higher-order functions.

View this table: [in this window] [in a new window]   Table 2 Functional hierarchies in KEGG BRITE

 
PATHWAY database
The KEGG PATHWAY database is a collection of manually drawn pathway maps for metabolism, genetic information processing, environmental information processing such as signal transduction, various other cellular processes and human diseases. During the past 2 years we have significantly increased the number of pathway maps for regulatory pathways including signal transduction, ligand–receptor interaction and cell communication, all based on extensive survey of published literature. For metabolic pathways we created two new sections, ‘Glycan Biosynthesis and Metabolism’ and ‘Biosynthesis of Polyketides and Nonribosomal Peptides’. The XML version of the pathway maps is available for both metabolic and regulatory pathways. These KEGG Markup Language (KGML) files provide graph information that can be used to computationally reproduce and manipulate KEGG pathway maps.

GENES database
The KEGG GENES database is a collection of gene catalogs for all complete genomes and some partial genomes (31 eukaryotes, 235 bacteriaand 23 archaea as of September 12, 2005), generated from publicly available resources, mostly NCBI RefSeq (3). All genomes in KEGG GENES are subject to SSDB computation and given manual KO assignments as described below. There are auxiliary collections of gene catalogs: DGENES for draft genomes (21 eukaryotes) and EGENES for expressed sequence tag consensus contigs (25 plants). These are meant to supplement the repertoire of KEGG organisms, and all are given automatic KO assignments using GENES as a reference dataset. Each GENES entry contains cross-reference information to outside databases, including NCBI gi numbers, Entrez Gene IDs and UniProt accession numbers. Starting with KEGG release 37.0 (January 2006) automatic ID conversion is implemented enabling use of such outside identifiers to access KEGG GENES and then the other KEGG databases.

KEGG orthology
There is a total of over one million genes in KEGG GENES, representing a tiny, but well-characterized part of the genomic space that makes up the biological world. From this part we organize knowledge about orthologous genes and paralogous genes, which, we hope, can be generalized for understanding the entire genomic space. This knowledge is stored in the KO system, a pathway-based classification of orthologous genes, including orthologous relationships of paralogous gene groups. The KO identifier, or the K number, is a common identifier for linking genomic information in the GENES database with network information in the PATHWAY database. The pathway nodes represented by rectangles in the KEGG reference pathway maps are given KO identifiers, so that organism-specific pathways can be computationally generated once each genome is annotated with KO's. This annotation or the KO assignment is done manually for KEGG GENES with the help of the GFIT tool using best-hit relations in pairwise genome comparisons stored in the SSDB database (4).

Because the number of ortholog groups that can be linked to pathways is limited, we have introduced two additional ways to define KO's. One is to use COG (5) to cover a broad-range of possible ortholog groups. The other is to rely on experts' classifications of protein families, which tend to be more functionally oriented resulting in narrowly defined KO's. A growing number of protein families are being added to the KO system, and they are shown in separate hierarchies different from the KEGG network hierarchy. The KO system can be best viewed from the KEGG BRITE database (Table 2).

LIGAND database
Originally, the LIGAND database consisted of just two components: ENZYME for enzyme nomenclature and COMPOUND for chemical compound structures (6). It later successively included additional components: REACTION for chemical reaction formulas, GLYCAN for glycan structures, RPAIR for reactant pair transformation patterns and DRUG for drug information. This expansion of the LIGAND collection represents our expanded efforts for understanding the chemical space that is part of the biological world.

The KEGG DRUG database is a new addition from KEGG release 36.1 (December 2005). It contains chemical structures and additional information such as therapeutic categories and target molecules. A most unique feature of KEGG DRUG is a collection of drug structure maps, which graphically illustrate, in a manner similar to KEGG pathway maps, our knowledge on groups of chemical structural patterns, therapeutic categories, their relationships and the chronology of drug development if known.

Reaction classification
The RC system in the chemical space is a counterpart of the KO system in the genomic space (Figure 1). It represents our attempt to organize knowledge on chemical reactions by categorizing chemical structure transformation patterns. The REACTION database contains individual reaction formulas taken from the ENZYME database. Each reaction formula is split into a set of substrate-product pairs, and the chemical structure comparison program SIMCOMP is applied to obtain an optimal alignment. This comparison is based on atom typing, which is the conversion of regular atomic (C, N, O, S, P and so on) representation to what we call KCF representation that consists of 68 atom types distinguishing functional groups and atomic environments (7). The chemical structure alignment generated by SIMCOMP is used to define the R atom for the reaction center, the D atom(s) for adjacent atom(s) in the mismatched region and the M atom(s) for adjacent atom(s) in the matched region (8). This is first done computationally and is followed by extensive manual curation.

The RPAIR database is still under development, but it is the basis for the RC system categorizing curated RDM patterns. Since an enzymatic reaction usually involves multiple substrates and products, one EC number corresponds to a combination of RDM patterns. The RC system has enabled automatic assignment of EC numbers from a set of substrate and product structures (8) and will further enable exploration of unknown reactions by generating plausible combinations of RDM patterns, which may then be related to possible paralogs of enzyme genes.

Glycosyltransferase reactions
Functional glycomics has been a most successful area for integrated analysis of genomic and chemical information (9). The carbohydrate sequence of glycans is determined by a specific set of biosynthetic reactions catalyzed by different types of glycosyltransferases. Thus, once we know the repertoire of glycosyltransferases in the genome or in the transcriptome, it should in principle be possible to predict the repertoire of glycan structures. Conversely, the knowledge about glycan structures can be used to search and annotate new glycosyltransferases. Composite Structure Map in KEGG GLYCAN is a tool for converting genomic or transcriptomic data to glycan structure variations based on a curated set of known glycosyltransferase reactions.

	ACCESSING KEGG

TOP ABSTRACT INTRODUCTION THE KEGG RESOURCE ACCESSING KEGG KEGG API REFERENCES

 
Web and FTP
KEGG is the major component of the Japanese GenomeNet, which is served by the Kyoto University Bioinformatics Center. The other GenomeNet services including DBGET and BLAST/FASTA searches are now primarily developed and used to support KEGG. The official URL for GenomeNet has been modified to http://www.genome.jp/, but the former URL http://www.genome.ad.jp/ will still be made available (Table 1). To download the KEGG data, academic users may use the GenomeNet FTP site.

	KEGG API

TOP ABSTRACT INTRODUCTION THE KEGG RESOURCE ACCESSING KEGG KEGG API REFERENCES

 
The KEGG API service has become an increasingly popular mode of access. It is the SOAP/WSDL interface to KEGG, enabling users to write their own programs to access, customize and utilize KEGG.

KegArray and KegDraw
KegArray and KegDraw are standalone Java applications that make use of the KEGG resources. KegArray is for microarray data analysis in conjunction with KEGG pathways and genomes. KegDraw is for drawing glycan structures and chemical compound structures, which can then be used to query against KEGG and PubChem databases. Both are freely available to academic and non-academic users.

	ACKNOWLEDGEMENTS

 
The KEGG project is supported by the Institute for Bioinformatics Research and Development of the Japan Science and Technology Agency, the 21st Century COE program ‘Genome Science’, and a grant-in-aid for scientific research on the priority area from the Ministry of Education, Culture, Sports, Science and Technology of Japan. The computational resources were provided by the Bioinformatics Center, Institute for Chemical Research, Kyoto University. Funding to pay the Open Access publication charges for this article was provided by the grant-in-aid for scientific research.

Conflict of interest statement. None declared.

	REFERENCES

TOP ABSTRACT INTRODUCTION THE KEGG RESOURCE ACCESSING KEGG KEGG API REFERENCES

 

1. Kanehisa, M. (1997) A database for post-genome analysis Trends Genet, . 13, 375–376[CrossRef][Web of Science][Medline] .

2. Kanehisa, M., Goto, S., Kawashima, S., Okuno, Y., Hattori, M. (2004) The KEGG resource for deciphering the genome Nucleic Acids Res, . 32, D277–D280[Abstract/Free Full Text] .

3. Pruitt, K.D., Tatusova, T., Maglott, D.R. (2005) NCBI Reference Sequence (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins Nucleic Acids Res, . 33, D501–D504[Abstract/Free Full Text] .

4. Kanehisa, M., Goto, S., Kawashima, S., Nakaya, A. (2002) The KEGG databases at GenomeNet Nucleic Acids Res, . 30, 42–46[Abstract/Free Full Text] .

5. Tatusov, R.L., Natale, D.A., Garkavtsev, I.V., Tatusova, T.A., Shankavaram, U.T., Rao, B.S., Kiryutin, B., Galperin, M.Y., Fedorova, N.D., Koonin, E.V. (2001) The COG database: new developments in phylogenetic classification of proteins from complete genomes Nucleic Acids Res, . 29, 22–28[Abstract/Free Full Text] .

6. Goto, S., Nishioka, T., Kanehisa, M. (1998) LIGAND: chemical database for enzyme reactions Bioinformatics, 14, 591–599[Abstract/Free Full Text] .

7. Hattori, M., Okuno, Y., Goto, S., Kanehisa, M. (2003) Development of a chemical structure comparison method for integrated analysis of chemical and genomic information in the metabolic pathways J. Am. Chem. Soc, . 125, 11853–11865[CrossRef][Web of Science][Medline] .

8. Kotera, M., Okuno, Y., Hattori, M., Goto, S., Kanehisa, M. (2004) Computational assignment of the EC numbers for genomic-scale analysis of enzymatic reactions J. Am. Chem. Soc, . 126, 16487–16498[CrossRef][Web of Science][Medline] .

9. Hashimoto, K., Goto, S., Kawano, S., Aoki-Kinoshita, K.F., Ueda, N., Hamajima, M., Kawasaki, T., Kanehisa, M. (2005) KEGG as a glycome informatics resource Glycobiology, , in press .

CiteULike    Connotea    Del.icio.us    What's this?

This article has been cited by other articles:

				J. Du, M A. Meledeo, Z. Wang, H. S Khanna, V. D P Paruchuri, and K. J Yarema Metabolic glycoengineering: Sialic acid and beyond Glycobiology, December 1, 2009; 19(12): 1382 - 1401. [Abstract] [Full Text] [PDF]

				S. Dong, O. N. Chesnokova, C. L. Turnbough Jr., and D. G. Pritchard Identification of the UDP-N-Acetylglucosamine 4-Epimerase Involved in Exosporium Protein Glycosylation in Bacillus anthracis J. Bacteriol., November 15, 2009; 191(22): 7094 - 7101. [Abstract] [Full Text] [PDF]

				M. Moisi, C. Jenul, S. M. Butler, A. New, S. Tutz, J. Reidl, K. E. Klose, A. Camilli, and S. Schild A Novel Regulatory Protein Involved in Motility of Vibrio cholerae J. Bacteriol., November 15, 2009; 191(22): 7027 - 7038. [Abstract] [Full Text] [PDF]

				E. W. Sayers, T. Barrett, D. A. Benson, E. Bolton, S. H. Bryant, K. Canese, V. Chetvernin, D. M. Church, M. DiCuccio, S. Federhen, et al. Database resources of the National Center for Biotechnology Information Nucleic Acids Res., November 12, 2009; (2009) gkp967v1. [Abstract] [Full Text] [PDF]

				M. Kuhn, D. Szklarczyk, A. Franceschini, M. Campillos, C. von Mering, L. J. Jensen, A. Beyer, and P. Bork STITCH 2: an interaction network database for small molecules and proteins Nucleic Acids Res., November 6, 2009; (2009) gkp937v1. [Abstract] [Full Text] [PDF]

				D. D. Sears, G. Hsiao, A. Hsiao, J. G. Yu, C. H. Courtney, J. M. Ofrecio, J. Chapman, and S. Subramaniam Mechanisms of human insulin resistance and thiazolidinedione-mediated insulin sensitization PNAS, November 3, 2009; 106(44): 18745 - 18750. [Abstract] [Full Text] [PDF]

				A. Keller, C. Backes, A. Gerasch, M. Kaufmann, O. Kohlbacher, E. Meese, and H.-P. Lenhof A novel algorithm for detecting differentially regulated paths based on gene set enrichment analysis Bioinformatics, November 1, 2009; 25(21): 2787 - 2794. [Abstract] [Full Text] [PDF]

				M. Kanehisa, S. Goto, M. Furumichi, M. Tanabe, and M. Hirakawa KEGG for representation and analysis of molecular networks involving diseases and drugs Nucleic Acids Res., October 30, 2009; (2009) gkp896v1. [Abstract] [Full Text] [PDF]

				S. Keerthikumar, S. Bhadra, K. Kandasamy, R. Raju, Y. L. Ramachandra, C. Bhattacharyya, K. Imai, O. Ohara, S. Mohan, and A. Pandey Prediction of Candidate Primary Immunodeficiency Disease Genes Using a Support Vector Machine Learning Approach DNA Res, October 3, 2009; (2009) dsp019v1. [Abstract] [Full Text] [PDF]

				C. Li, X. Li, Y. Miao, Q. Wang, W. Jiang, C. Xu, J. Li, J. Han, F. Zhang, B. Gong, et al. SubpathwayMiner: a software package for flexible identification of pathways Nucleic Acids Res., October 1, 2009; 37(19): e131 - e131. [Abstract] [Full Text] [PDF]

				M. Clement-Ziza, C. Malabat, C. Weber, I. Moszer, T. Aittokallio, C. Letondal, and S. Rousseau Genoscape: a Cytoscape plug-in to automate the retrieval and integration of gene expression data and molecular networks Bioinformatics, October 1, 2009; 25(19): 2617 - 2618. [Abstract] [Full Text] [PDF]

				K. Bleakley and Y. Yamanishi Supervised prediction of drug-target interactions using bipartite local models Bioinformatics, September 15, 2009; 25(18): 2397 - 2403. [Abstract] [Full Text] [PDF]

				D. Zamar, B. Tripp, G. Ellis, and D. Daley Path: a tool to facilitate pathway-based genetic association analysis Bioinformatics, September 15, 2009; 25(18): 2444 - 2446. [Abstract] [Full Text] [PDF]

				L. Zhang, M. Hammell, B. A. Kudlow, V. Ambros, and M. Han Systematic analysis of dynamic miRNA-target interactions during C. elegans development Development, September 15, 2009; 136(18): 3043 - 3055. [Abstract] [Full Text] [PDF]

				D. G. Morris, S. M. Waters, S. D. McCarthy, J. Patton, B. Earley, R. Fitzpatrick, J. J. Murphy, M. G. Diskin, D. A. Kenny, A. Brass, et al. Pleiotropic effects of negative energy balance in the postpartum dairy cow on splenic gene expression: repercussions for innate and adaptive immunity Physiol Genomics, September 1, 2009; 39(1): 28 - 37. [Abstract] [Full Text] [PDF]

				D. J. Pappas, G. Coppola, P. A. Gabatto, F. Gao, D. H. Geschwind, J. R. Oksenberg, and S. E. Baranzini Longitudinal system-based analysis of transcriptional responses to type I interferons Physiol Genomics, August 7, 2009; 38(3): 362 - 371. [Abstract] [Full Text] [PDF]

				J. Krawczyk, T. A. Kohl, A. Goesmann, J. Kalinowski, and J. Baumbach From Corynebacterium glutamicum to Mycobacterium tuberculosis--towards transfers of gene regulatory networks and integrated data analyses with MycoRegNet Nucleic Acids Res., August 1, 2009; 37(14): e97 - e97. [Abstract] [Full Text] [PDF]

				A. Mithani, G. M. Preston, and J. Hein Rahnuma: hypergraph-based tool for metabolic pathway prediction and network comparison Bioinformatics, July 15, 2009; 25(14): 1831 - 1832. [Abstract] [Full Text] [PDF]

				S.-H. Nam, D.-W. Kim, T.-S. Jung, Y.-S. Choi, D.-W. Kim, H.-S. Choi, S.-H. Choi, and H.-S. Park PESTAS: a web server for EST analysis and sequence mining Bioinformatics, July 15, 2009; 25(14): 1846 - 1848. [Abstract] [Full Text] [PDF]

				E. K. Piper, N. N. Jonsson, C. Gondro, A. E. Lew-Tabor, P. Moolhuijzen, M. E. Vance, and L. A. Jackson Immunological Profiles of Bos taurus and Bos indicus Cattle Infested with the Cattle Tick, Rhipicephalus (Boophilus) microplus Clin. Vaccine Immunol., July 1, 2009; 16(7): 1074 - 1086. [Abstract] [Full Text] [PDF]

				J. Elhai, A. Taton, J. Massar, J. K. Myers, M. Travers, J. Casey, M. Slupesky, and J. Shrager BioBIKE: A Web-based, programmable, integrated biological knowledge base Nucleic Acids Res., July 1, 2009; 37(suppl_2): W28 - W32. [Abstract] [Full Text] [PDF]

				C.-H. Chou, W.-C. Chang, C.-M. Chiu, C.-C. Huang, and H.-D. Huang FMM: a web server for metabolic pathway reconstruction and comparative analysis Nucleic Acids Res., July 1, 2009; 37(suppl_2): W129 - W134. [Abstract] [Full Text] [PDF]

				C.-H. Sun, M.-S. Kim, Y. Han, and G.-S. Yi COFECO: composite function annotation enriched by protein complex data Nucleic Acids Res., July 1, 2009; 37(suppl_2): W350 - W355. [Abstract] [Full Text] [PDF]

				A. Mithani, G. M. Preston, and J. Hein A stochastic model for the evolution of metabolic networks with neighbor dependence Bioinformatics, June 15, 2009; 25(12): 1528 - 1535. [Abstract] [Full Text] [PDF]

				Y. Yamanishi, M. Hattori, M. Kotera, S. Goto, and M. Kanehisa E-zyme: predicting potential EC numbers from the chemical transformation pattern of substrate-product pairs Bioinformatics, June 15, 2009; 25(12): i179 - i186. [Abstract] [Full Text] [PDF]

				I. P. Laumanns, L. Fink, J. Wilhelm, J.-C. Wolff, R. Mitnacht-Kraus, S. Graef-Hoechst, M. M. Stein, R. M. Bohle, W. Klepetko, M. A. R. Hoda, et al. The Noncanonical WNT Pathway Is Operative in Idiopathic Pulmonary Arterial Hypertension Am. J. Respir. Cell Mol. Biol., June 1, 2009; 40(6): 683 - 691. [Abstract] [Full Text] [PDF]

				A. C. Smith and A. J. Robinson MitoMiner, an Integrated Database for the Storage and Analysis of Mitochondrial Proteomics Data Mol. Cell. Proteomics, June 1, 2009; 8(6): 1324 - 1337. [Abstract] [Full Text] [PDF]

				Z. Tu, C. Argmann, K. K. Wong, L. J. Mitnaul, S. Edwards, I. C. Sach, J. Zhu, and E. E. Schadt Integrating siRNA and protein-protein interaction data to identify an expanded insulin signaling network Genome Res., June 1, 2009; 19(6): 1057 - 1067. [Abstract] [Full Text] [PDF]

				W. A. Messersmith, N.V. Rajeshkumar, A. C. Tan, X. F. Wang, V. Diesl, S. E. Choe, M. Follettie, C. Coughlin, F. Boschelli, E. Garcia-Garcia, et al. Efficacy and pharmacodynamic effects of bosutinib (SKI-606), a Src/Abl inhibitor, in freshly generated human pancreas cancer xenografts Mol. Cancer Ther., June 1, 2009; 8(6): 1484 - 1493. [Abstract] [Full Text] [PDF]

				C. J. Hunter, M. Williams, M. Petrosyan, Y. Guner, R. Mittal, D. Mock, J. S. Upperman, H. R. Ford, and N. V. Prasadarao Lactobacillus bulgaricus Prevents Intestinal Epithelial Cell Injury Caused by Enterobacter sakazakii-Induced Nitric Oxide both In Vitro and in the Newborn Rat Model of Necrotizing Enterocolitis Infect. Immun., March 1, 2009; 77(3): 1031 - 1043. [Abstract] [Full Text] [PDF]

				S. C. Murray, W. L. Rooney, M. T. Hamblin, S. E. Mitchell, and S. Kresovich Sweet Sorghum Genetic Diversity and Association Mapping for Brix and Height The Plant Genome, March 1, 2009; 2(1): 48 - 62. [Abstract] [Full Text] [PDF]

				O. N. Chesnokova, S. A. McPherson, C. T. Steichen, and C. L. Turnbough Jr. The Spore-Specific Alanine Racemase of Bacillus anthracis and Its Role in Suppressing Germination during Spore Development J. Bacteriol., February 15, 2009; 191(4): 1303 - 1310. [Abstract] [Full Text] [PDF]

				T. Baba, K. Kuwahara-Arai, I. Uchiyama, F. Takeuchi, T. Ito, and K. Hiramatsu Complete Genome Sequence of Macrococcus caseolyticus Strain JSCS5402, Reflecting the Ancestral Genome of the Human-Pathogenic Staphylococci J. Bacteriol., February 15, 2009; 191(4): 1180 - 1190. [Abstract] [Full Text] [PDF]

				G. M. Seibold, M. Wurst, and B. J. Eikmanns Roles of maltodextrin and glycogen phosphorylases in maltose utilization and glycogen metabolism in Corynebacterium glutamicum Microbiology, February 1, 2009; 155(2): 347 - 358. [Abstract] [Full Text] [PDF]

				S. Bocker, M. C. Letzel, Z. Liptak, and A. Pervukhin SIRIUS: decomposing isotope patterns for metabolite identification Bioinformatics, January 15, 2009; 25(2): 218 - 224. [Abstract] [Full Text] [PDF]

				M. A. Sartor, G. D. Leikauf, and M. Medvedovic LRpath: a logistic regression approach for identifying enriched biological groups in gene expression data Bioinformatics, January 15, 2009; 25(2): 211 - 217. [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]

				A. G. McDonald, S. Boyce, and K. F. Tipton ExplorEnz: the primary source of the IUBMB enzyme list Nucleic Acids Res., January 1, 2009; 37(suppl_1): D593 - D597. [Abstract] [Full Text] [PDF]

				D. S. Wishart, C. Knox, A. C. Guo, R. Eisner, N. Young, B. Gautam, D. D. Hau, N. Psychogios, E. Dong, S. Bouatra, et al. HMDB: a knowledgebase for the human metabolome Nucleic Acids Res., January 1, 2009; 37(suppl_1): D603 - D610. [Abstract] [Full Text] [PDF]

				J. W. Whitaker, I. Letunic, G. A. McConkey, and D. R. Westhead metaTIGER: a metabolic evolution resource Nucleic Acids Res., January 1, 2009; 37(suppl_1): D531 - D538. [Abstract] [Full Text] [PDF]

				Z.-y. Peng, X. Zhou, L. Li, X. Yu, H. Li, Z. Jiang, G. Cao, M. Bai, X. Wang, C. Jiang, et al. Arabidopsis Hormone Database: a comprehensive genetic and phenotypic information database for plant hormone research in Arabidopsis Nucleic Acids Res., January 1, 2009; 37(suppl_1): D975 - D982. [Abstract] [Full Text] [PDF]

				U. Schmidt, S. Struck, B. Gruening, J. Hossbach, I. S. Jaeger, R. Parol, U. Lindequist, E. Teuscher, and R. Preissner SuperToxic: a comprehensive database of toxic compounds Nucleic Acids Res., January 1, 2009; 37(suppl_1): D295 - D299. [Abstract] [Full Text] [PDF]

				C. Goble, R. Stevens, D. Hull, K. Wolstencroft, and R. Lopez Data curation + process curation=data integration + science Brief Bioinform, December 6, 2008; (2008) bbn034v1. [Abstract] [Full Text] [PDF]

				C. F. kuper, W. H. M. Heijne, M. Dansen, K. C. M. Verhoeckx, A. Boorsma, M. Radonjic, J. Bruijntjes, R. Stierum, H. Muijser, and J. H. E. Arts Molecular Characterization of Trimellitic Anhydride-induced Respiratory Allergy in Brown Norway Rats Toxicol Pathol, December 1, 2008; 36(7): 985 - 998. [Abstract] [Full Text] [PDF]

				S. Yagi, K. Hirabayashi, S. Sato, W. Li, Y. Takahashi, T. Hirakawa, G. Wu, N. Hattori, N. Hattori, J. Ohgane, et al. DNA methylation profile of tissue-dependent and differentially methylated regions (T-DMRs) in mouse promoter regions demonstrating tissue-specific gene expression Genome Res., December 1, 2008; 18(12): 1969 - 1978. [Abstract] [Full Text] [PDF]

				H. Neuweger, S. P. Albaum, M. Dondrup, M. Persicke, T. Watt, K. Niehaus, J. Stoye, and A. Goesmann MeltDB: a software platform for the analysis and integration of metabolomics experiment data Bioinformatics, December 1, 2008; 24(23): 2726 - 2732. [Abstract] [Full Text] [PDF]

				A. Mazurie, D. Bonchev, B. Schwikowski, and G. A. Buck Phylogenetic distances are encoded in networks of interacting pathways Bioinformatics, November 15, 2008; 24(22): 2579 - 2585. [Abstract] [Full Text] [PDF]

				U. D. Akavia and D. Benayahu Meta-analysis and profiling of cardiac expression modules Physiol Genomics, November 12, 2008; 35(3): 305 - 315. [Abstract] [Full Text] [PDF]

				J.-j. Kim and D. Rebholz-Schuhmann Categorization of services for seeking information in biomedical literature: a typology for improvement of practice Brief Bioinform, November 1, 2008; 9(6): 452 - 465. [Abstract] [Full Text] [PDF]

				M. A. A. Castro, R. J. S. Dalmolin, J. C. F. Moreira, J. C. M. Mombach, and R. M. C. de Almeida Evolutionary origins of human apoptosis and genome-stability gene networks Nucleic Acids Res., November 1, 2008; 36(19): 6269 - 6283. [Abstract] [Full Text] [PDF]

				Y. Li, P. Hao, S. Zheng, K. Tu, H. Fan, R. Zhu, G. Ding, C. Dong, C. Wang, X. Li, et al. Gene expression module-based chemical function similarity search Nucleic Acids Res., November 1, 2008; 36(20): e137 - e137. [Abstract] [Full Text] [PDF]

				B. Elliott, M. Kirac, A. Cakmak, G. Yavas, S. Mayes, E. Cheng, Y. Wang, C. Gupta, G. Ozsoyoglu, and Z. Meral Ozsoyoglu PathCase: pathways database system Bioinformatics, November 1, 2008; 24(21): 2526 - 2533. [Abstract] [Full Text] [PDF]

				S. Jancek, S. Gourbiere, H. Moreau, and G. Piganeau Clues about the Genetic Basis of Adaptation Emerge from Comparing the Proteomes of Two Ostreococcus Ecotypes (Chlorophyta, Prasinophyceae) Mol. Biol. Evol., November 1, 2008; 25(11): 2293 - 2300. [Abstract] [Full Text] [PDF]

				N. S. Rajasekaran, M. A. Firpo, B. A. Milash, R. B. Weiss, and I. J. Benjamin Global expression profiling identifies a novel biosignature for protein aggregation R120GCryAB cardiomyopathy in mice Physiol Genomics, October 8, 2008; 35(2): 165 - 172. [Abstract] [Full Text] [PDF]

				E. A. Welsh, M. Liberton, J. Stockel, T. Loh, T. Elvitigala, C. Wang, A. Wollam, R. S. Fulton, S. W. Clifton, J. M. Jacobs, et al. The genome of Cyanothece 51142, a unicellular diazotrophic cyanobacterium important in the marine nitrogen cycle PNAS, September 30, 2008; 105(39): 15094 - 15099. [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]

				S. Bocker and F. Rasche Towards de novo identification of metabolites by analyzing tandem mass spectra Bioinformatics, August 15, 2008; 24(16): i49 - i55. [Abstract] [Full Text] [PDF]

				G. Kastenmuller, J. Gasteiger, and H.-W. Mewes An environmental perspective on large-scale genome clustering based on metabolic capabilities Bioinformatics, August 15, 2008; 24(16): i56 - i62. [Abstract] [Full Text] [PDF]

				F. Santos-Beneit, A. Rodriguez-Garcia, E. Franco-Dominguez, and J. F. Martin Phosphate-dependent regulation of the low- and high-affinity transport systems in the model actinomycete Streptomyces coelicolor Microbiology, August 1, 2008; 154(8): 2356 - 2370. [Abstract] [Full Text] [PDF]

				K. Helbig, C. Grosse, and D. H. Nies Cadmium Toxicity in Glutathione Mutants of Escherichia coli J. Bacteriol., August 1, 2008; 190(15): 5439 - 5454. [Abstract] [Full Text] [PDF]

				D.-S. Lee, J. Park, K. A. Kay, N. A. Christakis, Z. N. Oltvai, and A.-L. Barabasi Cozzarelli Prize Winner@;DELIM@;From the Cover: The implications of human metabolic network topology for disease comorbidity PNAS, July 22, 2008; 105(29): 9880 - 9885. [Abstract] [Full Text] [PDF]

				I. G. Costa, S. Roepcke, C. Hafemeister, and A. Schliep Inferring differentiation pathways from gene expression Bioinformatics, July 1, 2008; 24(13): i156 - i164. [Abstract] [Full Text] [PDF]

				F. Lemoine, B. Labedan, and C. Froidevaux GenoQuery: a new querying module for functional annotation in a genomic warehouse Bioinformatics, July 1, 2008; 24(13): i322 - i329. [Abstract] [Full Text] [PDF]

				Y. Yamanishi, M. Araki, A. Gutteridge, W. Honda, and M. Kanehisa Prediction of drug-target interaction networks from the integration of chemical and genomic spaces Bioinformatics, July 1, 2008; 24(13): i232 - i240. [Abstract] [Full Text] [PDF]

				J. Klein, S. Leupold, R. Munch, C. Pommerenke, T. Johl, U. Karst, L. Jansch, D. Jahn, and I. Retter ProdoNet: identification and visualization of prokaryotic gene regulatory and metabolic networks Nucleic Acids Res., July 1, 2008; 36(suppl_2): W460 - W464. [Abstract] [Full Text] [PDF]

				K. Suhre and P. Schmitt-Kopplin MassTRIX: mass translator into pathways Nucleic Acids Res., July 1, 2008; 36(suppl_2): W481 - W484. [Abstract] [Full Text] [PDF]

				A. V. Nairn, W. S. York, K. Harris, E. M. Hall, J. M. Pierce, and K. W. Moremen Regulation of Glycan Structures in Animal Tissues: TRANSCRIPT PROFILING OF GLYCAN-RELATED GENES J. Biol. Chem., June 20, 2008; 283(25): 17298 - 17313. [Abstract] [Full Text] [PDF]

				J. S. Chang, R.-F. Yeh, J. K. Wiencke, J. L. Wiemels, I. Smirnov, A. R. Pico, T. Tihan, J. Patoka, R. Miike, J. D. Sison, et al. Pathway Analysis of Single-Nucleotide Polymorphisms Potentially Associated with Glioblastoma Multiforme Susceptibility Using Random Forests Cancer Epidemiol. Biomarkers Prev., June 1, 2008; 17(6): 1368 - 1373. [Abstract] [Full Text] [PDF]

				S. Gotz, J. M. Garcia-Gomez, J. Terol, T. D. Williams, S. H. Nagaraj, M. J. Nueda, M. Robles, M. Talon, J. Dopazo, and A. Conesa High-throughput functional annotation and data mining with the Blast2GO suite Nucleic Acids Res., June 1, 2008; 36(10): 3420 - 3435. [Abstract] [Full Text] [PDF]

				L. Smeenk, S. J. van Heeringen, M. Koeppel, M. A. van Driel, S. J. J. Bartels, R. C. Akkers, S. Denissov, H. G. Stunnenberg, and M. Lohrum Characterization of genome-wide p53-binding sites upon stress response Nucleic Acids Res., June 1, 2008; 36(11): 3639 - 3654. [Abstract] [Full Text] [PDF]

				D. Aguilar, L. Skrabanek, S. S. Gross, B. Oliva, and F. Campagne Beyond tissueInfo: functional prediction using tissue expression profile similarity searches Nucleic Acids Res., June 1, 2008; 36(11): 3728 - 3737. [Abstract] [Full Text] [PDF]

				A. S. Chen-Plotkin, F. Geser, J. B. Plotkin, C. M. Clark, L. K. Kwong, W. Yuan, M. Grossman, V. M. Van Deerlin, J. Q. Trojanowski, and V. M.-Y. Lee Variations in the progranulin gene affect global gene expression in frontotemporal lobar degeneration Hum. Mol. Genet., May 15, 2008; 17(10): 1349 - 1362. [Abstract] [Full Text] [PDF]

				F. Galka, S. N. Wai, H. Kusch, S. Engelmann, M. Hecker, B. Schmeck, S. Hippenstiel, B. E. Uhlin, and M. Steinert Proteomic Characterization of the Whole Secretome of Legionella pneumophila and Functional Analysis of Outer Membrane Vesicles Infect. Immun., May 1, 2008; 76(5): 1825 - 1836. [Abstract] [Full Text] [PDF]

				P. May, S. Wienkoop, S. Kempa, B. Usadel, N. Christian, J. Rupprecht, J. Weiss, L. Recuenco-Munoz, O. Ebenhoh, W. Weckwerth, et al. Metabolomics- and Proteomics-Assisted Genome Annotation and Analysis of the Draft Metabolic Network of Chlamydomonas reinhardtii Genetics, May 1, 2008; 179(1): 157 - 166. [Abstract] [Full Text] [PDF]

				K. Horan, C. Jang, J. Bailey-Serres, R. Mittler, C. Shelton, J. F. Harper, J.-K. Zhu, J. C. Cushman, M. Gollery, and T. Girke Annotating Genes of Known and Unknown Function by Large-Scale Coexpression Analysis Plant Physiology, May 1, 2008; 147(1): 41 - 57. [Abstract] [Full Text] [PDF]

				A. Jimeno, A. C. Tan, J. Coffa, N.V. Rajeshkumar, P. Kulesza, B. Rubio-Viqueira, J. Wheelhouse, B. Diosdado, W. A. Messersmith, C. Iacobuzio-Donahue, et al. Coordinated Epidermal Growth Factor Receptor Pathway Gene Overexpression Predicts Epidermal Growth Factor Receptor Inhibitor Sensitivity in Pancreatic Cancer Cancer Res., April 15, 2008; 68(8): 2841 - 2849. [Abstract] [Full Text] [PDF]

				M. A. Oberhardt, J. Puchalka, K. E. Fryer, V. A. P. Martins dos Santos, and J. A. Papin Genome-Scale Metabolic Network Analysis of the Opportunistic Pathogen Pseudomonas aeruginosa PAO1 J. Bacteriol., April 15, 2008; 190(8): 2790 - 2803. [Abstract] [Full Text] [PDF]

				J. Hegermann, S. Halbedel, R. Dumke, J. Regula, R. R. Gabdoulline, F. Mayer, J. Stulke, and R. Herrmann The acidic, glutamine-rich Mpn474 protein of Mycoplasma pneumoniae is surface exposed and covers the complete cell Microbiology, April 1, 2008; 154(4): 1185 - 1192. [Abstract] [Full Text] [PDF]

				S. Dong, S. A. McPherson, L. Tan, O. N. Chesnokova, C. L. Turnbough Jr., and D. G. Pritchard Anthrose Biosynthetic Operon of Bacillus anthracis J. Bacteriol., April 1, 2008; 190(7): 2350 - 2359. [Abstract] [Full Text] [PDF]

This Article Abstract Print PDF (340K) Screen PDF (106K) Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Add to My Personal Archive Download to citation manager Commercial Re-use Guidelines for Open Access NAR Content Google Scholar Articles by Kanehisa, M. Articles by Hirakawa, M. Search for Related Content PubMed PubMed Citation Articles by Kanehisa, M. Articles by Hirakawa, M. Social Bookmarking          What's this?

Online ISSN 1362-4962 - Print ISSN 0305-1048

Copyright © 2009 Oxford University Press

Oxford Journals Oxford University Press

• Site Map
• Privacy Policy
• Frequently Asked Questions

Other Oxford University Press sites: Oxford University Press Oxford Journals China Oxford Journals Japan American National Biography Booksellers' Information Service Children's Fiction and Poetry Children's Reference Corporate & Special Sales Dictionaries Dictionary of National Biography Digital Reference English Language Teaching Higher Education Textbooks Humanities International Education Unit Law Medicine Music Online Products Oxford English Dictionary Reference Rights and Permissions Science School Books Social Sciences Very Short Introductions World's Classics