Article |
From genomics to chemical genomics: new developments in KEGG
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 INTRODUCTIONTHE KEGG RESOURCEACCESSING KEGGKEGG APIREFERENCES |
|---|
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 |
|---|
|
TOPABSTRACTINTRODUCTIONTHE KEGG RESOURCEACCESSING KEGGKEGG APIREFERENCES |
|---|
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 |
|---|
|
TOPABSTRACTINTRODUCTIONTHE KEGG RESOURCEACCESSING KEGGKEGG APIREFERENCES |
|---|
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.
|
|
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.
|
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 |
|---|
|
TOPABSTRACTINTRODUCTIONTHE KEGG RESOURCEACCESSING KEGGKEGG APIREFERENCES |
|---|
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 |
|---|
|
TOPABSTRACTINTRODUCTIONTHE KEGG RESOURCEACCESSING KEGGKEGG APIREFERENCES |
|---|
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 |
|---|
|
TOPABSTRACTINTRODUCTIONTHE KEGG RESOURCEACCESSING KEGGKEGG APIREFERENCES |
|---|
- Kanehisa, M. (1997) A database for post-genome analysis Trends Genet, . 13, 375–376[CrossRef][ISI][Medline] .
- 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] . - 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] . - Kanehisa, M., Goto, S., Kawashima, S., Nakaya, A. (2002) The KEGG databases at GenomeNet Nucleic Acids Res, . 30, 42–46
[Abstract/Free Full Text] . - 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] . - Goto, S., Nishioka, T., Kanehisa, M. (1998) LIGAND: chemical database for enzyme reactions Bioinformatics, 14, 591–599
[Abstract/Free Full Text] . - 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][ISI][Medline] .
- 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][ISI][Medline] .
- 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:
|
|
 |
|
  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] [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] [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] [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]
|
|
|
|
 |
|
 B.-Y. Kang, S. Tsoi, Shan Zhu, Shenghui Su, and H. H. Kay Differential Gene Expression Profiling in HELLP Syndrome Placentas Reproductive Sciences, March 1, 2008; 15(3): 285 - 294. [Abstract] [PDF]
|
|
|
|
|
|
A. T. Beek, B. J. F. Keijser, A. Boorsma, A. Zakrzewska, R. Orij, G. J. Smits, and S. Brul Transcriptome Analysis of Sorbic Acid-Stressed Bacillus subtilis Reveals a Nutrient Limitation Response and Indicates Plasma Membrane Remodeling J. Bacteriol., March 1, 2008; 190(5): 1751 - 1761. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 B. Setzer, D. Lebrecht, and U. A. Walker Pyrimidine Nucleoside Depletion Sensitizes to the Mitochondrial Hepatotoxicity of the Reverse Transcriptase Inhibitor Stavudine Am. J. Pathol., March 1, 2008; 172(3): 681 - 690. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
P. Adler, J. Reimand, J. Janes, R. Kolde, H. Peterson, and J. Vilo KEGGanim: pathway animations for high-throughput data Bioinformatics, February 15, 2008; 24(4): 588 - 590. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
K. Sivaraman, A. Seshasayee, P. M. Tarwater, and A. M. Cole Codon choice in genes depends on flanking sequence information--implications for theoretical reverse translation Nucleic Acids Res., February 11, 2008; 36(3): e16 - e16. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
P. N. Bertin, C. Medigue, and P. Normand Advances in environmental genomics: towards an integrated view of micro-organisms and ecosystems Microbiology, February 1, 2008; 154(2): 347 - 359. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
D. Salgado, G. Gimenez, F. Coulier, and C. Marcelle COMPARE, a multi-organism system for cross-species data comparison and transfer of information Bioinformatics, February 1, 2008; 24(3): 447 - 449. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J.-L. Faulon, M. Misra, S. Martin, K. Sale, and R. Sapra Genome scale enzyme metabolite and drug target interaction predictions using the signature molecular descriptor Bioinformatics, January 15, 2008; 24(2): 225 - 233. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
I. Takigawa and H. Mamitsuka Probabilistic path ranking based on adjacent pairwise coexpression for metabolic transcripts analysis Bioinformatics, January 15, 2008; 24(2): 250 - 257. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 J.-R. Kim, Y. Yoon, and K.-H. Cho Coupled Feedback Loops Form Dynamic Motifs of Cellular Networks Biophys. J., January 15, 2008; 94(2): 359 - 365. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
T. Obayashi, S. Hayashi, M. Shibaoka, M. Saeki, H. Ohta, and K. Kinoshita COXPRESdb: a database of coexpressed gene networks in mammals Nucleic Acids Res., January 11, 2008; 36(suppl_1): D77 - D82. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
K. Degtyarenko, P. de Matos, M. Ennis, J. Hastings, M. Zbinden, A. McNaught, R. Alcantara, M. Darsow, M. Guedj, and M. Ashburner ChEBI: a database and ontology for chemical entities of biological interest Nucleic Acids Res., January 11, 2008; 36(suppl_1): D344 - D350. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
T. Nakahara, R. Hashimoto, H. Nakagawa, K. Monde, N. Miura, and S.-I. Nishimura Glycoconjugate Data Bank:Structures an annotated glycan structure database and N-glycan primary structure verification service Nucleic Acids Res., January 11, 2008; 36(suppl_1): D368 - D371. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
E. Grafahrend-Belau, S. Weise, D. Koschutzki, U. Scholz, B. H. Junker, and F. Schreiber MetaCrop: a detailed database of crop plant metabolism Nucleic Acids Res., January 11, 2008; 36(suppl_1): D954 - D958. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
A. Papanicolaou, S. Gebauer-Jung, M. L. Blaxter, W. Owen McMillan, and C. D. Jiggins ButterflyBase: a platform for lepidopteran genomics Nucleic Acids Res., January 11, 2008; 36(suppl_1): D582 - D587. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
L. J. Jensen, P. Julien, M. Kuhn, C. von Mering, J. Muller, T. Doerks, and P. Bork eggNOG: automated construction and annotation of orthologous groups of genes Nucleic Acids Res., January 11, 2008; 36(suppl_1): D250 - D254. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
C. Su, J. M. Peregrin-Alvarez, G. Butland, S. Phanse, V. Fong, A. Emili, and J. Parkinson Bacteriome.org an integrated protein interaction database for E. coli Nucleic Acids Res., January 11, 2008; 36(suppl_1): D632 - D636. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. Gunther, M. Kuhn, M. Dunkel, M. Campillos, C. Senger, E. Petsalaki, J. Ahmed, E. G. Urdiales, A. Gewiess, L. J. Jensen, et al. SuperTarget and Matador: resources for exploring drug-target relationships Nucleic Acids Res., January 11, 2008; 36(suppl_1): D919 - D922. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
K. P. Seiler, G. A. George, M. P. Happ, N. E. Bodycombe, H. A. Carrinski, S. Norton, S. Brudz, J. P. Sullivan, J. Muhlich, M. Serrano, et al. ChemBank: a small-molecule screening and cheminformatics resource database Nucleic Acids Res., January 11, 2008; 36(suppl_1): D351 - D359. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
F. Diella, C. M. Gould, C. Chica, A. Via, and T. J. Gibson Phospho.ELM: a database of phosphorylation sites update 2008 Nucleic Acids Res., January 11, 2008; 36(suppl_1): D240 - D244. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
C. Yeats, J. Lees, A. Reid, P. Kellam, N. Martin, X. Liu, and C. Orengo Gene3D: comprehensive structural and functional annotation of genomes Nucleic Acids Res., January 11, 2008; 36(suppl_1): D414 - D418. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. Donitz, B. Goemann, M. Lize, H. Michael, N. Sasse, E. Wingender, and A. P. Potapov EndoNet: an information resource about regulatory networks of cell-to-cell commu |