1 Evaluation of the Transcriptome and Genome to Inform the Study of Metabolic Control in Plants
Oliver Thimm
CNAP, Department of Biology, University of York, PO Box 373, YO10 5YW York, UK
Search for more papers by this authorOliver E. Bläsing
Max-Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Golm, Germany
Search for more papers by this authorBjörn Usadel
CNAP, Department of Biology, University of York, PO Box 373, YO10 5YW York, UK
Search for more papers by this authorYves Gibon
CNAP, Department of Biology, University of York, PO Box 373, YO10 5YW York, UK
Search for more papers by this authorOliver Thimm
CNAP, Department of Biology, University of York, PO Box 373, YO10 5YW York, UK
Search for more papers by this authorOliver E. Bläsing
Max-Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Golm, Germany
Search for more papers by this authorBjörn Usadel
CNAP, Department of Biology, University of York, PO Box 373, YO10 5YW York, UK
Search for more papers by this authorYves Gibon
CNAP, Department of Biology, University of York, PO Box 373, YO10 5YW York, UK
Search for more papers by this authorAbstract
The sections in this article are
- Introduction
- Transcript Profiling Technologies
- Transcript Profiling Workflow
- What Can We Learn from Transcript Profiles Performed in a Starchless Mutant?
- Conclusion/Perspectives
- Acknowledgements
References
- 1 C. Somerville and S. Somerville (1999) Plant functional genomics. Science 285, 380–383.
- 2 M.B. Eisen, P.T. Spellman, P.O. Brown and D. Botstein (1998) Cluster analysis and display of genome-wide expression patterns. Proceedings of the National Academy of Sciences of the United States of America 95, 14863–14868.
- 3 M.P. Brown, W.N. Grundy, D.Lin et al. (2000) Knowledge-based analysis of microarray gene expression data by using support vector machines. Proceedings of the National Academy of Sciences of the United States of America 97, 262–267.
- 4 J. Donson, Y. Fang, G. Espiritu-Santo et al. (2002) Comprehensive gene expression analysis by transcript profiling. Plant Molecular Biology 48, 75–97.
- 5 B.C. Meyers, T.H. Vu, S.S. Tej et al. (2004) Analysis of the transcriptional complexity of Arabidopsis thaliana by massively parallel signature sequencing. Nature Biotechnology 22, 1006–1011.
- 6 D. Gillespie and S. Spiegelman (1965) A quantitative assay for DNA-RNA hybrids with DNA immobilized on a membrane. Journal of Molecular Biology 12, 829–842.
- 7 J.C. Alwine, D.J. Kemp and G.R. Stark (1977) Method for detection of specific RNAs in agarose gels by transfer to diazobenzyloxymethyl-paper and hybridization with DNA probes. Proceedings of the National Academy of Sciences of the United States of America 74, 5350–5354.
- 8 R.A. Irizarry (2004) Multiple lab comparison of microarray platforms. Department of Biostatistics Working Papers, Johns Hopkins University, Working Paper 71, pp. 1–18.
- 9 C.A. Heid, J. Stevens, K.J. Livak and P.M. Williams (1996) Real time quantitative PCR. Genome Research 6, 986–994.
- 10 T. Czechowski, R.P. Bari, M. Stitt, W.R. Scheible and M.K. Udvardi (2004) Real-time RT-PCR profiling of over 1400 Arabidopsis transcription factors: unprecedented sensitivity reveals novel root- and shoot-specific genes. Plant Journal 38, 366–379.
- 11 N. Raikhel and S. Somerville (2004) Modification of the data release policy for gene expression profiling experiments. Plant Physiology 135, 1149.
- 12 R. Jorgensen (2004) Criteria for publication in The Plant Cell. The Plant Cell 16, 1645–1646.
- 13 A. Brazma, P. Hingamp, J. Quackenbush et al. (2001) Minimum information about a microarray experiment (MIAME) – toward standards for microarray data. Nature Genetics 29, 365–371.
- 14 S.Y. Rhee, W. Beavis, T.Z. Berardini et al. (2003) The Arabidopsis Information Resource (TAIR): a model organism database providing a centralized, curated gateway to Arabidopsis biology, research materials and community. Nucleic Acids Research 31, 224–228.
- 15 M. Stitt and A.R. Fernie (2003) From measurements of metabolites to metabolomics: an ‘on the fly’ perspective illustrated by recent studies of carbon–nitrogen interactions. Current Opinion in Biotechnology 14, 136–144.
- 16 S.L. Harmer, L.B. Hogenesch, M. Straume et al. (2000) Orchestrated transcription of key pathways in Arabidopsis by the circadian clock. Science 290, 2110–2113.
- 17 R. Schaffer, J. Landgraf, M. Accerbi, V. Simon, M. Larson and E. Wisman (2001) Microarray analysis of diurnal and circadian-regulated genes in Arabidopsis. The Plant Cell 13, 113–123.
- 18 S.M. Smith, D.C. Fulton, T. Chia et al. (2004) Diurnal changes in the transcriptome encoding enzymes of starch metabolism provide evidence for both transcriptional and posttranscriptional regulation of starch metabolism in Arabidopsis leaves. Plant Physiology 136, 2687–2699.
- 19 Y. Gibon, O.E. Blaesing, J. Hannemann et al. (2004) A robot-based platform to measure multiple enzyme activities in Arabidopsis using a set of cycling assays: comparison of changes of enzyme activities and transcript levels during diurnal cycles and in prolonged darkness. The Plant Cell 16, 3304–3325.
- 20 R. Alba, Z.J. Fei, P. Payton et al. (2004) ESTs, cDNA microarrays, and gene expression profiling: tools for dissecting plant physiology and development. Plant Journal 39, 697–714.
- 21 N. Leonhardt, J.M. Kwak, N. Robert, D. Waner, G. Leonhardt and J.I. Schroeder (2004) Microarray expression analyses of Arabidopsis guard cells and isolation of a recessive abscisic acid hypersensitive protein phosphatase 2C mutant. Plant Cell 16, 596–615.
- 22 J. Kehr (2003) Single cell technology. Current Opinion in Plant Biology 6, 617–621.
- 23 S. de Folter, J. Busscher, L. Colombo, A. Losa and G.C. Angenent (2004) Transcript profiling of transcription factor genes during silique development in Arabidopsis. Plant Molecular Biology 56, 351–366.
- 24 D. Steinhauser, B. Usadel, A. Luedemann, O. Thimm and J. Kopka (2004) CSB.DB: a comprehensive systems-biology database. Bioinformatics 20, 3647–3651.
- 25 D. Steinhauser, B.H. Junker, A. Luedemann, J. Selbig and J. Kopka (2004) Hypothesis-driven approach to predict transcriptional units from gene expression data. Bioinformatics 20, 1928–1939.
- 26 P. Zimmermann, M. Hirsch-Hoffmann, L. Hennig and W. Gruissem (2004) GENEVESTIGATOR. Arabidopsis microarray database and analysis toolbox. Plant Physiology 136, 2621–2632.
- 27 M. Suistomaa, A. Kari, E. Ruokonen and J. Takala (2000) Sampling rate causes bias in APACHE II and SAPS II scores. Intensive Care Medicine 26, 1773–1778.
- 28 H. Parkinson, U. Sarkans, M. Shojatalab et al. (2005) Array Express – a public repository for microarray gene expression data at the EBI. Nucleic Acids Research 33, D553–D555.
- 29 N. Allet, N. Barrillat, T. Baussant et al. (2004) In vitro and in silico processes to identify differentially expressed proteins. Proteomics 4, 2333–2351.
- 30 C.S. Goh, N. Lan, N. Echols et al. (2003) SPINE 2: a system for collaborative structural proteomics within a federated database framework. Nucleic Acids Research 31(11), 2833–2838.
- 31 R Development Core Team (2004) R: A language and environment for statistical computing.
- 32 R.C. Gentleman, V.J. Carey, D.M. Bates et al. (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biology 5, R80.
- 33 Affymetrix (1999) Microarray Suite User Guide, Version 4. Affymetrix.
- 34 R.A. Irizarry, B. Hobbs, F. Collin et al. (2003) Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4, 249–264.
- 35 F. Naef, C.R. Hacker, N. Patil and M. Magnasco (2002) Empirical characterization of the expression ratio noise structure in high-density oligonucleotide arrays. Genome Biology 3, 18.01–18.11.
- 36 C. Li and W.H. Wong (2001) Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection. Proceedings of the National Academy of Sciences of the United States of America 98, 31–36.
- 37 K. Aggarwal and K.H. Lee (2003) Functional genomics and proteomics as a foundation for systems biology. Brief Functional Genomics and Proteomics 2, 175–184.
- 38 C. Li and W. Wong (2001) Model-based analysis of oligonucleotide arrays: model validation, design issues and standard error application. Genome Biology 2, 1–11.
- 39 L. Zhang, M.F. Miles and K.D. Aldape (2003) A model of molecular interactions on short oligonucleotide microarrays. Nature Biotechnology 21, 818–821.
- 40 Z. Wu, R.A. Irizarry and R. Gentleman (2004) A model based background adjustment for oligonucleotide expression arrays. Department of Biostatistics Working Papers, Johns Hopkins University, Working Paper 1, pp. 1–26.
- 41 F. Naef and M.O. Magnasco (2003) Solving the riddle of the bright mismatches: labeling and effective binding in oligonucleotide arrays. Physical Review E Statistical, Nonlinear, and Soft Matter Physics 68, 1–4.
- 42 Z. Wu and R.A. Irizarry (2004) Preprocessing of oligonucleotide array data. Nature Biotechnology 22, 656–658.
- 43 S. Saviozzi and R.A. Calogero (2003) Microarray probe expression measures, data normalization and statistical validation. Computational Functional Genomics 4, 442–446.
- 44 M. O'Connell (2003) Differential expression, class discovery and class prediction using S-PLUS and S+ArrayAnalyser. Special Interest Group on Knowledge Discovery and Data Mining Explorations 5, 38–47.
- 45 Affymetrix (2003) Microarray Suite User Guide, Version 5. Affymetrix.
- 46 R.A. Irizarry, B.M. Bolstad, F. Collin, L.M. Cope, B. Hobbs and T.P. Speed (2003) Summaries of Affymetrix Gene Chip probe level data. Nucleic Acids Research 31, e15.
- 47 R. Breitling, P. Armengaud, A. Amtmann and P. Herzyk (2004) Rank products: a simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments. Federation of European Biochemical Societies Letters 573, 83–92.
- 48 V.G. Tusher, R. Tibshirani and G. Chu (2001) Significance analysis of microarrays applied to the ionizing radiation response. Proceedings of the National Academy of Sciences of the United States of America 98, 5116–5121.
- 49 R. Wang, K. Guegler, S.T. LaBrie and N.M. Crawford (2000) Genomic analysis of a nutrient response in Arabidopsis reveals diverse expression patterns and novel metabolic and potential regulatory genes induced by nitrate. The Plant Cell 12, 1491–1509.
- 50 R. Schaffer, J. Landgraf, M. Accerbi, V.V. Simon, M. Larson and E. Wisman (2001) Microarray analysis of diurnal and circadian-regulated genes in Arabidopsis. The Plant Cell 13, 113–123.
- 51 M. Seki, M. Narusaka, H. Abe et al. (2001) Monitoring the expression pattern of 1300 Arabidopsis genes under drought and cold stresses by using a full-length cDNA microarray. The Plant Cell 13, 61–72.
- 52 W. Pan (2002) A comparative review of statistical methods for discovering differentially expressed genes in replicated microarray experiments. Bioinformatics 18, 546–554.
- 53 W.J. Frawley, G. Piatetsky-Shapiro and C.J. Matheus (1991) Knowledge discovery in databases: an overview. In: Knowledge Discovery in Databases, (eds G. Piatetsky-Shapiro and W.J. Frawley), AAAI Press/MIT Press, Menlo Park, CA/Cambridge, MA. pp. 1–27.
- 54 X. Wen, S. Fuhrman, G.S. Michaels et al. (1998) Large-scale temporal gene expression mapping of central nervous system development. Proceedings of the National Academy of Sciences of the United States of America 95, 334–339.
- 55 P. D'Haeseleer, S. Liang and R. Somogyi (2000) Genetic network inference: from co-expression clustering to reverse engineering. Bioinformatics 16, 707–726.
- 56 S. Raychaudhuri, J.M. Stuart and R.B. Altman (2000) Principal components analysis to summarize microarray experiments: application to sporulation time series. Pacific Symposium on Biocomputing, 455–466.
- 57 K. Fellenberg, N.C. Hauser, B. Brors, A. Neutzner, J.D. Hoheisel and M. Vingron (2001) Correspondence analysis applied to microarray data. Proceedings of the National Academy of Sciences of the United States of America 98, 10781–10786.
- 58 D.J. Craigon, N. James, J. Okyere, J. Higgins, J. Jotham and S. May (2004) NASCArrays: a repository for microarray data generated by NASC's transcriptomics service. Nucleic Acids Research 32, 575–577.
- 59 L. Shen, J. Gong, R.A. Caldo et al. (2005) BarleyBase – an expression profiling database for plant genomics. Nucleic Acids Research 33, 614–618.
- 60 T. Barrett, T.O. Suzek, D.B. Troup et al. (2005) NCBI GEO: mining millions of expression profiles – database and tools. Nucleic Acids Research 33, 562–566.
- 61 H. Parkinson, U. Sarkans, M. Shojatalab et al. (2005) ArrayExpress – a public repository for microarray gene expression data at the EBI. Nucleic Acids Research 33, 553–555.
- 62 C.A. Ball, I.A. Awad, J. Demeter et al. (2005) The Stanford Microarray Database accommodates additional microarray platforms and data formats. Nucleic Acids Research 33, 580–582.
- 63 C. Ball, A. Brazma, H. Causton et al. (2004) An open letter on microarray data from the MGED Society. Microbiology 150, 3522–3524.
- 64 J. Allemeersch, S. Durinck, R. Vanderhaeghen et al. (2005) Benchmarking the CATMA microarray. A novel tool for Arabidopsis transcriptome analysis. Plant Physiology 137, 588–601.
- 65 G. Bloom, I.V. Yang, D. Boulware et al. (2004) Multi-platform, multi-site, microarray-based human tumor classification. American Journal of Pathology 164, 9–16.
- 66 R. Breitling, A. Amtmann and P. Herzyk (2004) Iterative Group Analysis (iGA): a simple tool to enhance sensitivity and facilitate interpretation of microarray experiments. BMC Bioinformatics 5, 34.
- 67 W. Chen, N.J. Provart, J. Glazebrook et al. (2002) Expression profile matrix of Arabidopsis transcription factor genes suggests their putative functions in response to environmental stresses. The Plant Cell 14, 559–574.
- 68 T. Wang and G.D. Stormo (2003) Combining phylogenetic data with co-regulated genes to identify regulatory motifs. Bioinformatics 19, 2369–2380.
- 69 V.J. Nikiforova, B. Gakiere, S. Kempa et al. (2004) Towards dissecting nutrient metabolism in plants: a systems biology case study on sulphur metabolism. Journal of Experimental Botany 55, 1861–1870.
- 70 M. Ashburner, C.A. Ball, J.A. Blake et al. (2000) Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nature Genetics 25, 25–29.
- 71 T.Z. Berardini, S. Mundodi, L. Reiser et al. (2004) Functional annotation of the Arabidopsis genome using controlled vocabularies. Plant Physiology 135, 745–755.
- 72 A. Ruepp, A. Zollner, D. Maier et al. (2004) The FunCat, a functional annotation scheme for systematic classification of proteins from whole genomes. Nucleic Acids Research 32, 5539–5545.
- 73 O. Thimm, O. Blasing, Y. Gibon et al. (2004) MAPMAN: a user-driven tool to display genomics data sets onto diagrams of metabolic pathways and other biological processes. Plant Journal 37, 914–939.
- 74 D. Martin, C. Brun, E. Remy, P. Mouren, D. Thieffry and B. Jacq (2004) GOToolBox: functional analysis of gene datasets based on Gene Ontology. Genome Biology 5, R101.
- 75 G.F. Berriz, O.D. King, B. Bryant, C. Sander and F.P. Roth (2003) Characterizing gene sets with FuncAssociate. Bioinformatics 19, 2502–2504.
- 76 P. Grosu, J.P. Townsend, D.L. Hartl and D. Cavalieri (2002) Pathway Processor: a tool for integrating whole-genome expression results into metabolic networks. Genome Research 12, 1121–1126.
- 77 D. Pan, N. Sun, K.H. Cheung et al. (2003) BMC PathMAPA: a tool for displaying gene expression and performing statistical tests on metabolic pathways at multiple levels for Arabidopsis. Boinformatics 4, 56.
- 78 S.W. Doniger, N. Salomonis, K.D. Dahlquist, K. Vranizan, S.C. Lawlor and B.R. Conklin (2003) MAPPFinder: using Gene Ontology and GenMAPP to create a global gene-expression profile from microarray data. Genome Biology 4, R7.
- 79 H.J. Chung, M. Kim, C.H. Park, J. Kim and J.H. Kim (2004) ArrayXPath: mapping and visualizing microarray gene-expression data with integrated biological pathway resources using Scalable Vector Graphics. Nucleic Acids Research 32 (Web Server issue), W460–W464.
- 80 L.A. Mueller, P. Zhang and S.Y. Rhee (2003) AraCyc: a biochemical pathway database for Arabidopsis. Plant Physiology 132, 453–460.
- 81 M. Kanehisa, S. Goto, S. Kawashima, Y. Okuno and M. Hattori (2004) The KEGG resource for deciphering the genome. Nucleic Acids Research 32, D277–D280.
- 82 O. Thimm, B. Essigmann, S. Kloska, T. Altmann and T.J. Buckhout (2001) Response of Arabidopsis to iron deficiency stress as revealed by microarray analysis. Plant Physiology 127, 1030–1043.
- 83 N. Moseyko and L.J. Feldman (2002) VIZARD: analysis of Affymetrix Arabidopsis GeneChip data. Bioinformatics 18, 1264–1265.
- 84 Z. Hu, J. Mellor, J. Wu and C. DeLisi (2004) VisANT: an online visualization and analysis tool for biological interaction data. BMC Bioinformatics 5, 17.
- 85 E.S. Wurtele, J. Li, L. Diao et al. (2003) MetNet: software to build and model the biogenetic lattice Arabidopsis. Computational Functional Genomics 4, 239–245.
- 86 R. Breitling, A. Amtmann and P. Herzyk (2004) Graph-based iterative group analysis enhances microarray interpretation. BMC Bioinformatics 5, 100.
- 87 H. Ogata, S. Goto, W. Fujibuchi and M. Kanehisa (1998) Computation with the KEGG pathway database. Biosystems 47, 119–128.
- 88 H. Schoof, R. Ernst, V. Nazarov, L. Pfeifer, H.W. Mewes and K.F. Mayer (2004) MIPS Arabidopsis thaliana Database (MAtDB): an integrated biological knowledge resource for plant genomics. Nucleic Acids Research 32, D373–D376.
- 89 H. Schoof, P. Zaccaria, H. Gundlach et al. (2002) MIPS Arabidopsis thaliana Database (MAtDB): an integrated biological knowledge resource based on the first complete plant genome. Nucleic Acids Research 30, 91–93.
- 90 T. Caspar and B.G. Pickard (1989) Gravitropism in a starchless mutant of Arabidopsis: implications for the starch-statolith theory of gravity sensing. Planta 177, 185–197.
- 91 Y. Gibon, O.E. Blasing, N. Palacios-Rojas et al. (2004) Adjustment of diurnal starch turnover to short days: depletion of sugar during the night leads to a temporary inhibition of carbohydrate utilization, accumulation of sugars and post-translational activation of ADP-glucose pyrophosphorylase in the following light period. Plant Journal 39, 847–862.
- 92 R. Brouquisse, J.P. Gaudillere and P. Raymond (1998) Induction of a carbon-starvation-related proteolysis in whole maize plants submitted to light/dark cycles and to extended darkness. Plant Physiology 117, 1281–1291.
- 93 C. Devaux, P. Baldet, J. Joubes et al. (2003) Physiological, biochemical and molecular analysis of sugar-starvation responses in tomato roots. Journal of Experimental Botany 54, 1143–1151.
- 94 L.J. Sweetlove, R.L. Last and A.R. Fernie (2003) Predictive metabolic engineering: a goal for systems biology. Plant Physiology 132, 420–425.
- 95 K.M. Oksman-Caldentey, D. Inze and M. Oresic (2004) Connecting genes to metabolites by a systems biology approach. Proceedings of the National Academy of Sciences of the United States of America 101, 9949–9950.
- 96 M.Y. Hirai, M. Yano, D.B. Goodenowe et al. (2004) Integration of transcriptomics and metabolomics for understanding of global responses to nutritional stresses in Arabidopsis thaliana . Proceedings of the National Academy of Sciences of the United States of America 101, 10205–10210.
- 97 D. Greenbaum, C. Colangelo, K. Williams and M. Gerstein (2003) Comparing protein abundance and mRNA expression levels on a genomic scale. Genome Biology 4, 117.
- 98 D. Edwards and J. Batley (2004) Plant bioinformatics: from genome to phenome. Trends in Biotechnology 22, 232–237.
- 99 B. Parvin, Q. Yang, G. Fontenay and M.H. Barcellos-Hoff (2002) BioSig: an imaging bioinformatic system for studying phenomics. Computer 35, 65.
- 100 D. Hanisch, J. Fluck, H.T. Mevissen and R. Zimmer (2003) Playing biology's name game: identifying protein names in scientific text. Pacific Symposium on Biocomputing, 403–414.
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