| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
|
|
|
|||||||
|
|||||||||
|
Plant Physiology 138:1289-1300 (2005) © 2005 American Society of Plant Biologists KaPPA-View. A Web-Based Analysis Tool for Integration of Transcript and Metabolite Data on Plant Metabolic Pathway Maps1,[w]Kazusa DNA Research Institute, Kisarazu, Chiba 292–0818, Japan (T.T., N.S., H.S., K.S., D.S.); Graduate School of Bioscience and Biotechnology, Tokyo Institute of Technology, Midori, Yokohama 226–8501, Japan (H.O.); Graduate School of Life Sciences, Tohoku University, Aoba, Sendai 980–8578, Japan (K.N.); Institute of Multidisciplinary Research for Advanced Materials, Tohoku University, Aoba, Sendai 980–8577, Japan (T.K.); Research Institute for Sustainable Humanosphere, Kyoto University, Uji, Kyoto 611–0011, Japan (T.U.); Marine Biotechnology Institute, Kamaishi, Iwate 026–0001, Japan (N.M.); and Graduate School of Pharmaceutical Sciences, Chiba University, Inage, Chiba 263–8522, Japan (K.S.)
The application of DNA array technology and chromatographic separation techniques coupled with mass spectrometry to transcriptomic and metabolomic analyses in plants has resulted in the generation of considerable quantitative data related to transcription and metabolism. The integration of "omic" data is one of the major concerns associated with research into identifying gene function. Thus, we developed a Web-based tool, KaPPA-View, for representing quantitative data for individual transcripts and/or metabolites on plant metabolic pathway maps. We prepared a set of comprehensive metabolic pathway maps for Arabidopsis (Arabidopsis thaliana) and depicted these graphically in Scalable Vector Graphics format. Individual transcripts assigned to a reaction are represented symbolically together with the symbols of the reaction and metabolites on metabolic pathway maps. Using quantitative values for transcripts and/or metabolites submitted by the user as Comma Separated Value-formatted text through the Internet, the KaPPA-View server inserts colored symbols corresponding to a defined metabolic process at that site on the maps and returns them to the user's browser. The server also provides information on transcripts and metabolites in pop-up windows. To demonstrate the process, we describe the dataset obtained for transgenic plants that overexpress the PAP1 gene encoding a MYB transcription factor on metabolic pathway maps. The presentation of data in this manner is useful for viewing metabolic data in a way that facilitates the discussion of gene function.
The total number of metabolites produced in the plant kingdom is estimated to exceed 200,000 (Dixon and Strack, 2003  ). Given the ever-increasing applications of plant metabolites, either directly or indirectly as foods, medicines, and industrial materials, the new research area referred to as "metabolomics," in which metabolites are investigated holistically in conjunction with functional genomics, will have a strong influence on plant biotechnology and breeding. Comprehensive analyses of transcripts can be performed using DNA array technology (Aharoni and Vorst, 2002; Donson et al., 2002), especially for plant species such as Arabidopsis (Arabidopsis thaliana; Arabidopsis Genome Initiative, 2000) and rice (Oryza sativa) in which genome sequencing has been completed (Feng et al., 2002; Goff et al., 2002; Sasaki et al., 2002; Yu et al., 2002) or species for which expressed sequence tag sequences are available, such as soybean (Glycine max), wheat (Triticum aestivum), barley (Hordeum vulgare), and tomato (Solanum esculentum; http://www.ncbi.nlm.nih.gov/dbEST/). Recently, the application of compound separation techniques coupled to mass spectrometry (MS), such as gas chromatography-MS, liquid chromatography (LC)-MS, and capillary electrophoresis-MS, have been applied to the comprehensive analysis of plant metabolites (Sumner et al., 2003; Sato et al., 2004). These analyses have produced large amounts of transcriptomic and metabolomic ("omic") quantitative data, the integration of which is one of the major concerns for researchers identifying genes and for "omic" approaches to systems biology.
Several metabolic pathway databases are available to facilitate our understanding of transcriptome and metabolome data. The Kyoto Encyclopedia of Genes and Genomes (KEGG; http://www.genome.ad.jp/kegg/) has a pathway database (PATHWAY) that contains information of metabolites and genes, as well as graphical representations of metabolic pathways and complexes derived from various biological processes (Goto et al., 2002
A relatively common characteristic of plants is that several homologous gene products are often assigned to a single enzymatic reaction. Multigene families are considerably more prevalent among plant genomes than among animal genomes (Arabidopsis Genome Initiative, 2000
The diagrammatic representation of individual transcript data together with enzymatic reaction information on a metabolic pathway map that also contains quantitative information of the substrate and product might aid in our interpretation of the gene function. One of the tools on the AraCyc database has the ability to paint data values of transcripts onto the metabolic overview diagram. However, when multigene families are thought to be involved in single reactions, only representative data are used for the painting. Individual transcript data are not shown on individual metabolic pathway maps. A recent version of AraCyc can also represent metabolite data but only onto the overview diagram. Recently, a user-driven tool, MAPMAN, was developed for representing transcript data on the pictorial diagrams, in which all Arabidopsis genes were categorized on the basis of biological function (Thimm et al., 2004
Overall Features of the KaPPA-View Tool We designed a Web-based tool for the analysis of plant metabolic pathways, called KaPPA-View, with which users can display the changes of individual transcripts and metabolites on the comprehensive metabolic pathway maps that we prepared. In addition, each metabolic pathway map was designed to serve as a source of information for metabolites and genes involved in the pathway. To meet the computational requirements of such a task, we set up the program KV-Engine to manage the pathway information library that generates metabolic pathway maps in SVG format (see below), the data library, and the Web server on the KaPPA-View server (Fig. 1). Using Internet browsers, users can access and apply the tool to their own datasets formatted as Comma Separated Value (CSV) files. In addition, users can access the transcript and metabolite data libraries that are uploaded from the KaPPA-View administrator. Given that KaPPA-View is a JAVA application, it is platform independent and can be used on a variety of popular operation systems (OS), such as Windows 2000/XP (Microsoft, Redmond, WA) and Macintosh OS 9/X (Apple Computer, Cupertino, CA), all of which have the SVG plug-in SVG Viewer supplied by Adobe Systems (San Jose, CA). However, we recommend use of Windows 2000/XP and the Web browser Internet Explorer 6.0 or higher, which allows users to access the full functions of KaPPA-View. Macintosh users can use the basic functions but cannot access directly information windows from each SVG tag on maps, while information windows are accessible from the element list provided on the screen.
We used symbols to depict the various enzymatic reactions and transcripts involved in the metabolic pathway maps (Fig. 2A). In this way, circles were used to represent substrates and the reaction products, arrows for reactions, and squares for transcripts involved in, or putatively assigned to, the reaction. The changes in individual transcripts and metabolites were represented using squares and circles of different colors, respectively, that were defined by color charts (Fig. 2B). We also designed color pathway indicators for overall changes to transcripts and metabolites in individual pathways, subclasses, and categories (Fig. 2C).
Information on the metabolites and genes in individual pathways can be retrieved for each map in pop-up windows (Fig. 3). An element list listing all of the information of the elements (the reaction, genes involved, and metabolites) on the map currently being displayed on a user's browser can be shown on the screen (Fig. 3A). This means that users are capable of knowing the quantitative values and other information for each transcript or metabolite listed. Using the element list, users can select and display the metabolite reference page (Fig. 3B); the gene reference page, in which each gene identifier is linked to the relevant gene information page of The Arabidopsis Information Resource database (http://www.arabidopsis.org/; data not shown); and the enzyme reference page (Fig. 3C). Users can extract all of the pathways that relate to the element currently being displayed by clicking on symbols of the elements.
The manner in which diagrams are displayed gives users the opportunity to grasp the contents of the image at a glance, which is useful in plant research as multiple transcripts are often associated with a single reaction (Fig. 4A). Furthermore, the names of pathways immediately up or downstream are indicated on each map, and related pathway maps can be displayed in pop-up windows at the users request (Fig. 4B). The metabolic pathways thus generated were classified as being one of 25 subclasses that were further subdivided under seven major metabolic categories (described below). Using this classification, all of the pathway indicators are represented on the bird's-eye map (Fig. 4C), allowing users to appreciate the overall picture of the changes in transcripts and metabolites. Furthermore, users can access individual maps by clicking on the pathway indicators on the bird's-eye map.
To achieve a dynamic graphical representation of the quantitative changes in transcripts and/or metabolites, we adapted the SVG format for map drawing. We also ensured that the dimensions of the maps were compliant with standard computational viewing sizes and that printing A4 or letter-sized maps was possible. Due to the vector nature of SVG format, the sizes of maps generated using the program can be changed on screen without any loss of picture quality using a browser function.
We prepared a set of comprehensive metabolic pathway maps for Arabidopsis. These maps include 1,263 enzymatic reactions that could be classified into seven major metabolic categories with 25 subclasses (Table I). We classified the enzymatic reactions along with the carbon flows derived from the intake carbon dioxide by plants during photosynthesis. Therefore, we avoided functional categories such as "plant hormone" and "secondary metabolism" in our classification, but rather positioned such reactions as branches of the metabolic flows. For example, the biosynthesis pathway of brassinosteroids, which are known to be plant hormones, was classified as a subclass of isoprenoid metabolism. Isoprenoid metabolism and phenylpropanoid metabolism, which are often categorized in secondary metabolism, were classified in independent categories. Furthermore, to avoid the production of fragmentary maps and facilitate presentation of the image of transcript/metabolite changes in a particular metabolic pathway, we integrated related metabolic reactions into single maps. However, considerable care was taken to avoid too much integration, e.g. the mevalonate terpenoid pathway, the nonmevalonate terpenoid pathway, the isoprenoid pathway, the sterol pathway, the carotenoid and abscisic acid pathway, the tocopherol pathway, and the brassinosteroid pathway, which are included in a single map as in steroid biosynthesis in KEGG/PATHWAY, are all classified independently. Consequently, KaPPA-View has fewer maps (n = 130) than AraCyc (n = 220) but more than KEGG (n = 98 for Arabidopsis). To help users find a metabolic pathway based on functional category, however, we included link indicators to functional categories in a classification list. In cases where the same metabolites are localized in distinct subcellular compartments, such as lipid metabolism in the cytoplasm and plastids, both pathways were shown using distinct metabolic pathway maps. Otherwise, information of cellular location was not explicitly given on the maps.
In the initial version, we prepared the metabolic pathway maps for the model plant Arabidopsis. Although only a limited number of the metabolic reactions included in our maps have been proven experimentally in Arabidopsis, we included the reactions that are assigned with the Arabidopsis gene annotation. Thus, although alkaloid biosynthesis has not been reported in Arabidopsis, we included the indole alkaloid synthesis pathway under miscellaneous pathways because a putatively assigned gene for indole alkaloid synthesis has been found in Arabidopsis (see below). The genes assigned to the metabolic reactions in AraCyc and the Arabidopsis metabolic reactions in KEGG/PATHWAY were used as queries to search for the Arabidopsis protein sequences. The genes that were not found in the gene sets were manually refined and incorporated in the metabolic pathway maps. Furthermore, the metabolic reactions that are not included in AraCyc and KEGG/PATHWAY were incorporated on the basis of recent knowledge of plant metabolism, as described in the following comments for each metabolic category.
Carbohydrate Metabolism
Amino Acids, Nucleic Acids, and Nitrogen-Containing Derivative Metabolism
Biosynthesis of the nitrogen- and sulfur-containing glucosinolate metabolites, which are known to have physiological activity in the Brassicaceae (which includes Arabidopsis), was cited from the review article by Wittstock and Halkier (2002)
Lipid Metabolism
Isoprenoid Metabolism
Phenylpropanoid Metabolism
As several flavonol glucosides (Graham, 1998
We included the biosynthesis of tocopherols, plastoquinones, phylloquinones, and ubiquinones in the terpenoid quinone pathway (Lange and Ghassemian, 2003
Miscellaneous Pathways While there are no reports of alkaloids in Arabidopsis, the genome contains the genes (At1g74000, At1g74010, At1g74020, At2g41290, At2g41300, At3g57010, At3g57020, At3g57030, and At3g59530) that share homology with the genes for strictosidine synthase, which is involved in the synthesis of indole alkaloids in Eschscholtzia californica. The gene products are also referred to as FAD-binding domain-containing proteins. Therefore, we included the indole alkaloid pathway in miscellaneous pathways.
To demonstrate the usefulness of our KaPPA-View tool, we represent quantitative data of transcripts and metabolites of wild-type and the PAP1-overexpressing plants on a bird's-eye map and on individual metabolic pathway maps (Fig. 4). Overexpression of the PAP1 gene encoding a MYB transcription factor in Arabidopsis was recently reported to cause the accumulation of high levels of anthocyanins and to induce the transcription of a set of genes, including those of the flavonoid biosynthesis pathway (Tohge et al., 2005
The pathway indicators in the bird's-eye map (Fig. 4A) are colored according to a key (Fig. 4, section D for transcripts and section E for metabolites). The bird's-eye map reveals activation of the flavonoid biosynthesis pathway as was reported by Tohge et al. (2005)
The flavonoid synthesis pathway map shows the overall activation of the pathway at the transcriptional level (Fig. 4A), which is consistent with the accumulation of anthocyanins (Fig. 4B) as reported by Tohge et al. (2005)
Given the wealth of transcriptomic data and the increasing amounts of metabolomic data, we designed a tool for the analysis of plant metabolic pathways, KaPPA-View, to display individual quantitative changes in transcripts and/or metabolites on a set of comprehensive metabolic pathway maps. As exemplified by comparative analysis between wild type and transgenic plants that overexpress the PAP1 gene that encodes a MYB transcription factor (Tohge et al., 2005 ; Fig. 4), simultaneous presentation of both transcripts and metabolites on the bird's-eye and metabolic pathway maps allows users to quickly grasp the differences in the samples being examined. Although the visualization tool is user friendly, intuitive, and simplifies the review of complex results of transcription analyses, the data presented on the metabolic maps should be interpreted with caution; simple conclusions should not be made in the absence of a statistical treatment of the data and/or biochemical experimentation. In this regard, we are currently preparing various transgenic Arabidopsis plants carrying various genes under the control of a strong promoter, and we are comparing these with the wild-type host plants using the KaPPA-View tool. These comparisons will provide an understanding of the effects of the introduced genes on the transcriptome and metabolome, leading to hypotheses on the function of specific genes. In this context, the KaPPA-View tool works well as a generator of hypotheses rather than for making definitive conclusions about the role of specific genes. Comparison of transcriptome and/or metabolome of distinct tissues, such as leaves and roots, under various growth conditions might also be useful for finding trends in transcription and metabolism.
The KaPPA-View tool is complementary to other metabolic pathway tools and databases. The user-driven tool MAPMAN is designed to present quantitative data of all Arabidopsis transcripts that are categorized on the basis of functionality of their products on pictorial graphs of various biological processes (Thimm et al., 2004
The KaPPA-View tool differs conceptually from other "omics" data tools such as MetNet (Wurtele et al., 2003 We adapted the SVG format to be able to display and change the colors of different symbols used to represent quantitative data of transcripts and metabolites on an Internet browser. The SVG technology proved suitable not only for the generation of figures, but also for submission of new or edited pathway maps from researchers to the KaPPA-View administrator using the SVG editor SVG Map Drawer we provide. Consequently, the present versions of our maps will change along with new knowledge of metabolic pathways. The method of submitting new maps will facilitate quick updates and maintenance of pathway information. In future versions of KaPPA-View, we will incorporate appropriate suggestions from users regarding the addition of genes, compounds, and reactions. If users wish to alter information, such as metabolite names and the names of genes presented on our maps, for their presentations, and we believe such a demand is likely given the variety of chemical and genetic nomenclature in use, they may edit the SVG source text files using a text file editor. The SVG-formatted maps for Arabidopsis can be used as universal plant metabolic pathway maps for analysis of other plant species, although some pathways, such as those for isoflavonoids, have not been included. In the SVG Arabidopsis maps, the Arabidopsis Genome Initiative (AGI) gene numbers given to all Arabidopsis genes on the basis of their genome sequences were used to assign SVG tags (square symbols). Therefore, if users prepare a correspondence table between AGI numbers and gene identification numbers for the plant species of interest, the DNA array data assigned to the identification numbers can be represented using maps as well as metabolite data. Such a correspondence table could be generated using BLAST match of one's own gene sequence dataset to that of the Arabidopsis genome sequence. However, in cases where the number of genes assigned to a reaction number more than that assigned to the reaction in the Arabidopsis map, the user must choose the genes according to the numbers of assigned Arabidopsis genes. Therefore, to increase the applicability of the tool, the use of immovable square symbols on the maps will be changed such that they will be flexible and ordered according to the numbers assigned to the reaction in future versions of KaPPA-View. Other plant metabolic pathways not found in Arabidopsis also will be included. Because the BLAST match strategy could produce a significant number of incorrect functional annotations, however, users must interpret the results with caution. We are currently preparing a comprehensive set of metabolic pathways for the legume Lotus japonicus.
Recently, a new tool for integrating the Arabidopsis transcriptomic and metabolomic data, BioPathAt, which operates as a visual interface in a commercial software package, GeneSpring, was reported (Lange and Ghassemian, 2005
Architecture of the KaPPA-View Tool The application engine (KV-Engine) was designed to control the pathway information library that contained the pathway map files and the metabolite structure files, the data library with its experimental data files, and the information library that contained information such as the correlations between maps and genes (Fig. 5). The pathway information library and the data library were managed using a relational database management system (MySQL server; http://www.mysql.com/). The script for the KV-Engine was written using JAVA 1.4.2 (Sun Microsystems, Santa Clara, CA) and run on a Debian GNU/Linux 3.0 operating system (http://www.debian.org). The KV-Engine and MySQL (4.0.20) were connected using Java Database Connectivity Application Program Interface technology. The Web server was constructed using Tomcat 4_4.0.3_3 woody3 application server and Apache 1.3.26_0 woody3 WWW server controller. JavaServer Pages technology was used to generate the dynamic Web contents. The KaPPA-View tool can be accessed at http://kpv.kazusa.or.jp/kappa-view/.
To generate SVG-formatted files for the metabolic pathway maps and pathway indicators, we drew the maps using a drawing tool, SVG Map Drawer, available for download from the KaPPA-View Web site. The symbols on the metabolic pathway maps were assigned using SVG identification tags to receive the values from the information library. The AGI code numbers for Arabidopsis (Arabidopsis thaliana) genes were given SVG identification tags for transcripts on the SVG-formatted maps as the tag value. The EC numbers for enzymes were named according to International Union of Biochemistry and Molecular Biology Enzyme Nomenclature (http://www.chem.qmw.ac.uk/iubmb/enzyme). All compound names appearing on the KaPPA-View maps were proofread using ontology data files of the Chemical Entities of Biological Interest site of EMBL-European Bioinformatics Institute (http://www.ebi.ac.uk/chebi/) and the GENE ONTOLOGY site (http://www.geneontology.org/GO.downloads.shtml). The chemical structures of plant metabolites were drawn using ChemDrawUltra 7.0 (CambridgeSoft, Cambridge, UK) and stored as gif-formatted figures and mol-formatted files in the metabolite structure files. The data library was designed to store quantitative data of transcripts and metabolites. As the file format of the transcript data of the initial version of the KaPPA-View tool does not correspond to the Minimum Information About a Microarray Experiment (MIAME) format (http://www.mged.org/Workgroups/MIAME/miame.html), the data included in the data library are imported from other transcript databases such as the ArrayExpress held at European Bioinformatics Institute (http://www.ebi.ac.uk/arrayexpress/) and Gene Expression Omnibus held at the National Center for Biotechnology Information (http://www.ncbi.nlm.nih.gov/geo/), where MIAME-formatted descriptions of experimental details are accessible. Information of the imported data, such as experimental identification numbers in the original database, is kept in the comment field of the data library. In future versions of KaPPA-View, the data library will be formatted using MIAME as the standard.
To display SVG-formatted pathway maps on client computers, users need to install the Adobe SVG Viewer Web-browser plug-in, which is freely available from the Adobe SVG Viewer download site (http://www.adobe.com/svg/viewer/install/main.html). The KaPPA-View tool can be used with any personal computers using Windows OS or Macintosh OS X. The user's transcriptome/metabolome data should be prepared as CSV-formatted text files (see the KaPPA-View manual).
We thank Dr. Kentaro Yano (Kazusa DNA Research Institute) for his help in analyzing Arabidopsis genes and Dr. Takayuki Tohge (Chiba University) for providing transcript and metabolite data for PAP1-overexpressing plants. We thank Dr. Youji Takeuchi for his help preparing pathway maps, Kanami Moriya and Mayumi Hasegawa for their advice on the KaPPA-View manual, and Yuuko Tazawa for drawing the SVG-formatted metabolic pathway maps. Received February 6, 2005; returned for revision April 28, 2005; accepted April 28, 2005.
1 This work was supported by New Energy and Industrial Technology Development (as part of the project called Development of Fundamental Technologies for Controlling the Process of Material Production of Plants). 
2 Present address: Graduate School of Frontier Sciences, University of Tokyo, 5–1–5 Kashiwanoha, Kashiwa, Chiba, 277–8561, Japan.
[w] The online version of this article contains Web-only data. www.plantphysiol.org/cgi/doi/10.1104/pp.105.060525. * Corresponding author; e-mail shibata{at}kazusa.or.jp; fax 81–438–52–3948.
Aharoni A, Ric de Vos CH, Verhoeven HA, Maliepaard CA, Kruppa G, Bino R, Goddenowe DB (2002) Nontargeted metabolome analysis by use of Fourier Transform Ion Cyclotron Mass Spectrometry. OMICS 6: 217–234[CrossRef][Medline] Aharoni A, Vorst O (2002) DNA microarrays for functional plant genomics. Plant Mol Biol 48: 99–118[CrossRef][Web of Science][Medline] Arabidopsis Genome Initiative (2000) Analysis of the genome sequence of the flowering plant Arabidopsis thaliana. Nature 408: 796–815[CrossRef][Medline]
Beisson F, Koo AJ, Ruuska S, Schwender J, Pollard M, Thelen JJ, Paddock T, Salas JJ, Savage L, Milcamps A, et al (2003) Arabidopsis genes involved in acyl lipid metabolism. A 2003 census of the candidates, a study of the distribution of expressed sequence tags in organs, and a Web-based database. Plant Physiol 132: 681–697 Bloor SJ, Abrahams S (2002) The structure of the major anthocyanin in Arabidopsis thaliana. Phytochemistry 59: 343–346[CrossRef][Web of Science][Medline] Breitling R, Amtmann A, Herzyk P (2004) Graph-based iterative Group Analysis enhances microarray interpretation. BMC Bioinformatics 5: 100[CrossRef][Medline]
Chen F, Tholl D, D'Auria JC, Farooq A, Pichersky E, Gershenzon J (2003) Biosynthesis and emission of terpenoid volatiles from Arabidopsis flowers. Plant Cell 15: 481–494 Dixon RA, Strack D (2003) Phytochemistry meets genome analysis, and beyond. Phytochemistry 62: 815–816[CrossRef][Web of Science][Medline] Donson J, Fang Y, Espiritu-Santo G, Xing W, Salazar A, Miyamoto S, Armendarez V, Volkmuth W (2002) Comprehensive gene expression analysis by transcript profiling. Plant Mol Biol 48: 75–97[CrossRef][Web of Science][Medline] Feng Q, Zhang Y, Hao P, Wang S, Fu G, Huang Y, Li Y, Zhu J, Liu Y, Hu X, et al (2002) Sequence and analysis of rice chromosome 4. Nature 420: 316–320[CrossRef][Medline]
Glawischnig E, Hansen BG, Olsen CE, Halkier BA (2004) Camalexin is synthesized from indole-3-acetaldoxime, a key branching point between primary and secondary metabolism in Arabidopsis. Proc Natl Acad Sci USA 101: 8245–8250
Goff SA, Ricke D, Lan TH, Presting G, Wang R, Dunn M, Glazebrook J, Sessions A, Oeller P, Varma H, et al (2002) A draft sequence of the rice genome (Oryza sativa L. ssp. japonica). Science 296: 92–100
Goto S, Okuno Y, Hattori M, Nishioka T, Kanehisa M (2002) LIGAND: database of chemical compounds and reactions in biological pathways. Nucleic Acids Res 30: 402–404 Goujon T, Sibout R, Eudes A, MacKay J, Jouanin L (2003) Genes involved in the biosynthesis of lignin precursors in Arabidopsis thaliana. Plant Physiol Biochem 41: 677–687[CrossRef] Graham TL (1998) Flavonoid and flavonol glycoside metabolism in Arabidopsis. Plant Physiol Biochem 36: 135–144
Grosu P, Townsend JP, Hartl DL, Cavalieri D (2002) Pathway Processor: a tool for integrating whole-genome expression results into metabolic networks. Genome Res 12: 1121–1126
Kanehisa M, Goto S, Kawashima S, Nakaya A (2002) The KEGG databases at GenomeNet. Nucleic Acids Res 30: 42–46
Krieger CJ, Zhang P, Müller LA, Wang A, Paley S, Arnaud M, Pick J, Rhee SY, Karp PD (2004) MetaCyc: a multiorganism database of metabolic pathways and enzymes. Nucleic Acids Res 32: D438–D442 Lange BM, Ghassemian M (2003) Genome organization in Arabidopsis thaliana: a survey for genes involved in isoprenoid and chlorophyll metabolism. Plant Mol Biol 51: 925–948[CrossRef][Web of Science][Medline] Lange BM, Ghassemian M (2005) Comprehensive post-genomic data analysis approaches integrating biochemical pathway maps. Phytochemistry 66: 413–451[CrossRef][Web of Science][Medline]
Mueller LA, Zhang P, Rhee SY (2003) AraCyc: a biochemical pathway database for Arabidopsis. Plant Physiol 132: 453–460
Nelson DR, Schuler MA, Paquette SM, Werck-Reichhart D, Bak S (2004) Comparative genomics of rice and Arabidopsis. Analysis of 727 cytochrome P450 genes and pseudogenes from a monocot and a dicot. Plant Physiol 135: 756–772 Pan D, Sun N, Cheung KH, Guan Z, Ma L, Holford M, Deng X, Zhao H (2003) PathMAPA: a tool for displaying gene expression and performing statistical tests on metabolic pathways at multiple levels for Arabidopsis. BMC Bioinformatics 4: 56[CrossRef][Medline]
Raes J, Rohde A, Christensen JH, Van de Peer Y, Boerjan W (2003) Genome-wide characterization of the lignification toolbox in Arabidopsis. Plant Physiol 133: 1051–1071 Sasaki T, Matsumoto T, Yamamoto K, Sakata K, Baba T, Katayose Y, Wu J, Niimura Y, Cheng Z, Nagamura Y, et al (2002) The genome sequence and structure of rice chromosome 1. Nature 420: 312–316[CrossRef][Medline] Sato S, Soga T, Nishioka T, Tomita M (2004) Simultaneous determination of the main metabolites in rice leaves using capillary electrophoresis mass spectrometry and capillary electrophoresis diode array detection. Plant J 40: 151–163[CrossRef][Web of Science][Medline] Sumner LW, Mendes P, Dixon RA (2003) Plant metabolomics: large-scale phytochemistry in the functional genomics era. Phytochemistry 62: 817–836[CrossRef][Web of Science][Medline] Thimm O, Bläsing O, Gibon Y, Nagel A, Meyer S, Krüger P, Selbig J, Müller LA, Rhee SY, Stitt M (2004) MAPMAN: a user-driven tool to display genomics data sets onto diagrams of metabolic pathways and other biological processes. Plant J 37: 914–939[CrossRef][Web of Science][Medline] Tohge T, Nishiyama Y, Hirai MY, Yano M, Nakajima J, Awazuhara M, Inoue E, Takahashi H, Goodenowe DB, Kitayama M, et al (2005) Functional genomics by integrated analysis of metabolome and transcriptome of Arabidopsis plants over-expressing an MYB transcription factor. Plant J 42: 218–235[CrossRef][Web of Science][Medline] Veit M, Pauli GF (1999) Major flavonoids from Arabidopsis thaliana leaves. J Nat Prod 62: 1301–1303[CrossRef][Medline] Wittstock U, Halkier BA (2002) Glucosinolate research in the Arabidopsis era. Trends Plant Sci 7: 263–270[CrossRef][Web of Science][Medline] Wurtele ES, Li J, Diao L, Zhang H, Foster CM, Fatland B, Dickerson J, Brown A, Cox Z, Cook D, et al (2003) MetNet: software to build and model the biogenetic lattice of Arabidopsis. Comp Funct Genomics 4: 239–245[CrossRef]
Yokoyama R, Nishitani K (2001) A comprehensive expression analysis of all members of a gene family encoding cell-wall enzymes allowed us to predict cis-regulatory regions involved in cell-wall construction in specific organs of Arabidopsis. Plant Cell Physiol 42: 1025–1033
Yokoyama R, Nishitani K (2004) Genomic basis for cell-wall diversity in plants. A comparative approach to gene families in rice and Arabidopsis. Plant Cell Physiol 45: 1111–1121
Yokoyama R, Rose JK, Nishitani K (2004) A surprising diversity and abundance of xyloglucan endotransglucosylase/hydrolases in rice. Classification and expression analysis. Plant Physiol 134: 1088–1099
Yu J, Hu S, Wang J, Wong GK, Li S, Liu B, Deng Y, Dai L, Zhou Y, Zhang X, et al (2002) A draft sequence of the rice genome (Oryza sativa L. ssp. indica). Science 296: 79–92 This article has been cited by other articles:
|
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
| ASPB Publications | PLANT PHYSIOLOGY® | THE PLANT CELL | |
|---|---|---|---|
TOP
ABSTRACT
RESULTS
