Abstract
Genomics has revolutionized research in biological sciences. The reductionist approach of single-molecule analysis is being slowly, but steadily, replaced by global views of the entire cellular machinery. This started with genetics, which turned to determining complete DNA sequences of organisms and is resulting in global comparisons of these sequences to infer their evolutionary past. With the availability of the first complete genome sequences, however, it became obvious that we know very little about how cells work. The problem is that a large number of genes in any genome have functions that are yet unknown, as judged by the lack of obvious phenotype of the corresponding mutants. These “orphan” genes are also not similar at the DNA or protein sequence levels to other genes of known function. The first complete eukaryotic genome, of the yeast Saccharomyces cerevisiae (Goffeau et al., 1996), perhaps the species for which we know most biochemistry, revealed a staggering 40% of genes to which no function could be assigned. Based on this, Oliver called for a systematic approach to the discovery of gene function (Oliver, 1996), which since became known as functional genomics.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Achard F, Vaysseix G, Barillot E. XML, bioinformatics and data integration. Bioinformatics 17: 115–125 (2001).
Asimov D, The grand tour: a tool for viewing multidimensional data. SIAMJ Sci Stat Compute 128–143 (1985).
Baker PG, Goble CA, Bechhofer S et al. An ontology for bioinformatics applications. Bioinformatics 15: 510–520. (1999).
Barillot E, Achard F. XML: A lingua franca for science? Trends Biotechnol 18: 331–333 (2000).
Bell CJ, Dixon RA, Farmer AD et al. The Medicago Genome Initiative: a model legume database. Nucleic Acids Res 29: 114–117 (2001).
Bestel-Corre G, Dumas-Gaudot E et al. Proteome analysis and identification of symbiosis-related proteins from Medicago truncatula Gaertn by two-dimensional electrophoresis and mass spectrometry. Electrophoresis 23: 122–137. (2002).
Brazhnik P, de la Fuente A, Mendes P. Gene networks: how to put the function in genomics. Trends Biotechnol in pressspi (2002).
Casari G, De Daruvar A, Sander C, Schneider R. Bioinformatics and the discovery of gene function. Trends Genet 12: 244–245 (1996).
Chance B, Garfinkel D, Higgins J, Hess B. Metabolic control mechanisms. V. A solution for the equations representing interaction between glycolysis and respiration in ascites tumor cells. J Biol Chem 235: 2426–2439 (1960).
Cherry JM, Adler C, Ball C et al. SGD: Saccharomyces Genome Database. Nucleic Acids Res 26: 73–79 (1998).
Consortium, The Gene Ontology. Creating the gene ontology resource: design and implementation. Genome Res 11: 1425–1433 (2001).
Cornish-Bowden A, Cardenas ML. Functional genomics. Silent genes given voice. Nature 409: 571–572 (2001).
Eisen MB, Spellman PT, Brown PO, Botstein D. Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA. 95: 14863–14868 (1998).
Ellis LB, Hershberger CD, Bryan EM, Wackett LP. The University of Minnesota Biocatalysis /Biodegradation Database: emphasizing enzymes. Nucleic Acids Res 29: 340–343 (2001).
Fell DA, Sauro HM. Metabolic control analysis by computer — progress and prospects. Biomed Biochim Acta 49: 811–816 (1990).
Fiehn O, Kopka J, Dbrmann P et al. Metabolite profiling for plant functional genomics. Nature Biotechnol 18: 1157–1161 (2000).
Fiehn O. Metabolomics — the link between genotypes and phenotypes. Plant Mol Biol 48: 155–171 (2002).
Friedman JH. Exploratory projection pursuit. J Am Stat Assoc 82: 249–266 (1987).
Gansner ER, North SC. An open graph visualization system and its applications to software engineering. Software Practice Experience 30: 1203–1233 (1999).
Garfinkel D, Garfinkel L, Pring M et al. Computer applications to biochemical kinetics. Ann Rev Biochem 39: 473–498 (1970).
Garfinkel D. Computer modeling of metabolic pathways. Trends Biochem Sci 6: 69–71 (1981).
Goffeau A, Barrell BG, Bussey H et al. Life with 6000 genes. Science 274: 546–567 (1996).
Goto S, Nishioka T, Kanehisa M. LIGAND: chemical database for enzyme reactions. Bioinformatics 14: 591–599 (1998).
Hofmeyr JHS. Steady-state modeling of metabolic pathways. A guide for the prospective simulator. Computer Appl Bio sci 2: 5–11 (1986).
Huala E, Dickerman AW, Garcia-Hernandez M et al. The Arabidopsis Information Resource (TAIR): a comprehensive database and web-based information retrieval, analysis, and visualization system for a model plant. Nucleic Acids Res 29: 102–105 (2001).
Hucka M, Finney A, Sauro HM et al. The Systems Biology Markup Language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics: submitted (2002).
Huhman DV, Sumner LW. Metabolic profiling of saponins in Medicago sativa and Medicago truncatula using HPLC coupled to an electrospray ion-trap mass spectrometer. Phytochemistry 59: 347–360 (2002).
Hulme EC. Simulation of biochemical systems. J Theoret Biol 31: 131–137 (1971).
Jeong H, Tombor B, Albert R et al. The large-scale organization of metabolic networks. Nature 407: 651–654 (2000).
Juty NS, Spence HD, Hotz HR et al. Simultaneous modelling of metabolic, genetic and product-interaction networks. Briefings Bioinformat 2: 223–232 (2001).
Kanehisa M, Goto S, Kawashima S, Nakaya A. The KEGG databases at Genome Net. Nucleic Acids Res 30: 42–46 (2002).
Karp PD, Riley M, Paley SM, Pelligrinitoole A. Ecocyc - An encyclopedia of Escherichia coli genes and metabolism. Nucleic Acids Res 24: 32–39 (1996).
Karp PD. An ontology for biological function based on molecular interactions. Bioinformatics 16: 269–285 (2000).
Kell DB, King RD. On the optimization of classes for the assignment of unidentified reading frames in functional genomics programmes: the need for machine learning. Trends Biotechnol 18: 93–98 (2000).
Kibby MR. Stochastic method for the simulation of biochemical systems on a digital computer. Nature 222: 298–299 (1969).
Kose F, Weckwerth W, Linke T, Fiehn O. Visualizing plant metabolomic correlation networks using clique-metabolite matrices. Bioinformatics 17: 1198–1208 (2001).
Mathesius U, Keijzers G, Natera SH et al. Establishment of a root proteome reference map for the model legume Medicago truncatula using the expressed sequence tag database for peptide mass fingerprinting. Proteomics 1: 1424–1440 (2001).
Mendes P. Biochemistry by numbers: simulation of biochemical pathways with Gepasi 3. Trends Biochem Sci 22: 361–363 (1997).
Mendes P, Bulmore DL, Farmer AD et al. PathDB: a second generation metabolic database. In Animating the Cellular Map. Hofmeyr JHS, Rohwer JM, Snoep JL (Ed) pp. 207–212, Stellenbosch University Press, Stellenbosch (2000).
Mendes P. Emerging bioinformatics for the metabolome. Briefings Bioinformat in press (2002).
Meyer RD, Cook D. Visualization of data. Curr Opin Biotechnol 11: 89–96 (2000).
Nakao M, Bono H, Kawashima S et al. Genome-scale gene expression analysis and pathway reconstruction in KEGG. Genome Informat 10: 94–103 (1999).
Oliver SG. From DNA sequence to biological function. Nature 379: 597–600 (1996).
Oliver SG, Winson MK, Kell DB, Baganz F. Systematic functional analysis of the yeast genome. Trends Biotechnol 16: 373–378 (1998).
Park DJ, Wright BE. METASIM, a general purpose metabolic stimulator for studying cellular transformations. Computer Prog Biomed 3: 10–26 (1973).
Raamsdonk LM, Teusink B, Broadhurst D et al. A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations. Nature Biotechnol 19: 45–50 (2001).
Riley M. Functions of the gene products of Escherichia coli. Microbiol Rev 57: 862–952 (1993).
Roessner U, Luedemann A, Brust D et al. Metabolic profiling allows comprehensive phenotyping of genetically or environmentally modified plant systems. Plant Cell 13: 11–29 (2001).
Schomburg I, Chang A, Schomburg D. BRENDA, enzyme data and metabolic information. Nucleic Acids Res 30: 47–49 (2002).
Schulze-Kremer S. Adding semantics to genome databases: towards an ontology for molecular biology. Intel Sys Mol Biol 5: 272–275 (1997).
Selkov E, Basmanova S, Gaasterland T et al. The metabolic pathway collection from EMP: the enzymes and metabolic pathways database. Nucleic Acids Res 24: 26–28 (1996).
Trethewey RN, Krotzky AJ, Willmitzer L. Metabolic profiling: a Rosetta stone for genomics? Curr Opin Plant Biol 2: 83–85 (1999).
Wagner A, Fell DA. The small world inside large metabolic networks. Proc Royal Soc London B 268: 1803–1810 (2001).
Wittig U, De Beuckelaer A. Analysis and comparison of metabolic pathway databases. Briefings Bioinformat 2: 126–142 (2001).
Wixon J. Website review: pathway databases. Compar Fund Genom 2: 391–397 (2001).
Wolf D, Gray CP, de Saizieu A. Visualising gene expression in its metabolic context. Briefings Bioinformat 1: 297–304 (2000).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer Science+Business Media New York
About this chapter
Cite this chapter
Li, X.J. et al. (2003). Databases and Visualization for Metabolomics. In: Harrigan, G.G., Goodacre, R. (eds) Metabolic Profiling: Its Role in Biomarker Discovery and Gene Function Analysis. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-0333-0_16
Download citation
DOI: https://doi.org/10.1007/978-1-4615-0333-0_16
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4613-5025-5
Online ISBN: 978-1-4615-0333-0
eBook Packages: Springer Book Archive