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234 Analytical Platforms and Databases

users can obtain structural information for peaks detected during metabolic profiling

without the need for performing additional MS/MS analysis; structural information can be

obtained by searching for the corresponding MS2T accession in the libraries.

Pathway databases

In order to understand the molecular basis of life, it is important to clarify the relationships

between cells, organisms and the biosphere. Hence, in the recent years, interactions

between and pathways involving genes and molecules are rapidly being organized after

which protein activities, gene annotations and metabolites will be integrated into a single

database. Some databases for metabolic pathways are listed in Table 14.8.

Table 14.8. Pathway databases.

AraCyc at TAIR http://www.Arabidopsis.org/biocyc/index.jsp

PlantCyc http://plantcyc.org/

Kyoto Encyclopedia of Genes and

Genomes (KEGG) Pathway Database

http://www.genome.jp/kegg/pathway.html

MetaCrop http://metacrop.ipk-gatersleben.de/

MapMan http://gabi.rzpd.de/projects/MapMan/

Kazusa Plant Pathway Viewer (KaPPA-

View)

http://kpv.kazusa.or.jp/kpv3/guestIndex.jsp

Metabolic Pathway Search at KATANA http://www.kazusa.or.jp/katana/pathway.html

MetaCyc http://metacyc.org/

BioCyc http://biocyc.org/

AraCyc

http://www.Arabidopsis.org/biocyc/

AraCyc (Mueller et al., 2003) provides access to manually curated or reviewed information

on metabolic pathways in Arabidopsis– the model plant. These pathways may be unique to

Arabidopsis or common to other organisms. Presently, this database features more than 170

pathways, and includes information on compounds, intermediates, cofactors, reactions,

genes, proteins, and subcellular locations of proteins. The database uses the Pathway Tools

software (Karp et al., 2002), which allows users to visualize all pathways in the database

down to the individual chemical structures of the compounds.

PlantCyc of the Plant Metabolic Network

http://plantcyc.org/

PlantCyc contains pathways and the catalytic enzymes and genes involved in pathways as

well as compounds from many plant species. Majority of the pathway diagrams in PlantCyc

were manually extracted from the plant literature. They are either supported by

experimental evidence or are based on expert hypotheses. A small portion of the pathways

were computationally predicted. These pathways are known to exist in non-plant organisms

but have not been found in plants. However, some hints indicate that plants may utilize the

same pathway or similar routes. Both experimentally verified and computationally

predicted enzymes and genes are assigned to pathways. Evidence codes are designated to

the pathways and genes/enzymes are assigned in order to provide information on data

Analytical Platforms and Databases 235

quality. In addition to new pathways curated in-house, the first release of PlantCyc contains

all pathways from AraCyc (Mueller et al., 2003), all plant pathways from MetaCyc (Caspi

et al., 2008), and a number of curated pathways from other plant pathway databases

including RiceCyc (Jaiswal et al., 2006) and MedicCyc (Urbanczyk-Wochniak and Sumner,

2007). Moreover PlantCyc integrates computational assignments of enzymes and genes

from other plant pathway databases including AraCyc, RiceCyc, MedicCyc, and SolaCyc.

KEGG PATHWAY

http://www.genome.jp/kegg/pathway.html

The KEGG pathway contains information on metabolites and genes as well as graphical

representations of metabolic pathways and complexes derived from various biological

processes (Kanehisa et al., 2002). Thus far, 94,739 pathways generated from 327 reference

pathways for over 200 organisms, including Arabidopsis and rice, have been constructed.

Maps of organism-specific metabolic pathways can be generated on the basis of the

assignment information in the KEGG/GENES database.

MetaCrop

http://metacrop.ipk-gatersleben.de/

MetaCrop (Grafahrend-Belau et al., 2008) is a database containing manually curated,

highly detailed information on metabolic pathways in crop plants; this database includes

location information as well as information on transport processes and reaction kinetics.

The web interface enables an easy exploration of information from overview pathways to

single reactions, and thus helps users understand the metabolism of crop plants. It also

supports automatic data export for the creation of detailed models of metabolic pathways in

order to support simulation approaches. MetaCrop contains hand-curated information on

approximately 40 major metabolic pathways in various crop plants; in particular, the

metabolism of agronomically important organs such as seed or tuber is available. In this

database, species of both monocotyledons and dicotyledons are represented. Information on

involved metabolites, stoichiometry, and the precise location (species, organ, tissue,

compartment, and developmental stage) has been incorporated in the reactions.

Furthermore, with regard to central metabolism (glycolysis, TCA cycle), kinetic data of the

reactions are available. References and relevant PubMed IDs are provided. In order to have

a controlled vocabulary for allowing the comparison of data from different sources,

ontology terms have been used. In total, the database currently contains information for six

major crop plants (approximately 400 enzymatic reactions, 60 transport processes, 6

compartments, and 600 references) and initial information for several more crop plants. All

information was manually extracted through an extensive survey of literature and online

databases.

MapMan

http://gabi.rzpd.de/projects/MapMan/

MapMan (Thimm et al., 2004; Usadel et al., 2005) is a user-driven tool that displays large

genomics datasets onto diagrams of metabolic pathways or other processes. MapMan

consists of a Scavenger module that collects and classifies the measured parameters into

hierarchical functional categories, and an ImageAnnotator module that uses these

236 Analytical Platforms and Databases

classifications to organize and display data on diagrams of the user's choice. Each gene is

represented by a discrete signal, allowing individual responses to be identified. Signals for

genes that are involved in a particular process are grouped spatially, making it possible to

discern general trends that would be less apparent from lists of individual genes. Now,

users can browse more than a dozen sample data sets. Moreover the MapMan has been

distributed as a JAVA application, therefore, users can perform their analyses by using

their own PCs.

Kazusa Plant Pathway Viewer

http://kpv.kazusa.or.jp/kappa-view/

With advances in DNA array technology, vast amounts of transcriptome data have been

produced, and mass spectrometers have contributed to the massive production of

metabolome data. Interpretation of metabolome data in conjunction with transcriptome data

is one of the major concerns associated with research on the identification of metabolism-

related gene functions. Kazusa Plant Pathway Viewer (KaPPA-View) (Tokimatsu et al.,

2005) facilitates the representation of quantitative data for individual transcripts and/or

metabolites on plant metabolic pathway maps, and easy comprehension of the transcripts

and metabolites; this in turn facilitates investigation into the hypotheses of gene functions

in metabolic pathways.

Assembly of Transcriptomics and Metabolomics

In order to understand the mechanisms underlying the functioning of a living organism by

linking a range of information from the genome, transcriptome, proteome and metabolome

of the organism, it is essential to have a platform that is capable of integrating a range of

‘omics’ data so that these data can be analysed together. ‘Batch-learning self-organizing

map’ (BL-SOM) is one of the tools that can support such integrated analyses (Kanaya et al.,

2001; Abe et al., 2003). The transcriptome and metabolome datasets of this tool consist of

a matrix in which signal intensities are arranged in multiple columns (experimental series)

and multiple rows (gene and metabolite IDs). BL-SOM can analyse an integrated matrix of

both transcriptome and metabolome data after appropriate normalization of the data and

preliminary calculations; thus, this tool allows researchers to visualize the correlations

between elements. Genes and metabolites are classified into clusters in a two-dimensional

‘feature map’ based on the expression and accumulation patterns of genes and metabolites.

The high reproducibility and resolution supported by BL-SOM enable the elucidation of

gene-to-metabolite, gene-to-gene and metabolite-to-metabolite correlations, which facilitate

the identification of a gene’s function (Hirai et al., 2004, 2005).

One example stated above is PRIMe (Akiyama et al., 2008). PRIMe is a web site that

has been designed and implemented to support research and analysis workflows ranging

from metabolome to transcriptome analysis. This site provides access to a growing

collection of standardized measurements of metabolites obtained by using NMR, GC-MS,

LC-MS, CE-MS, and metabolomics tools that support related analyses (SpinAssign for the

identification of metabolites by means of NMR, and KNApSAcK (Shinbo et al., 2006) for

searches within metabolite databases). In addition, the transcriptomics tools support

Correlated Gene Search, and Cluster Cutting for the analysis of mRNA expression. The use

of these tools and databases can contribute to the analysis of biological events at the level

Analytical Platforms and Databases 237

of metabolite and gene expression. For Arabidopsis thaliana, we describe one example of

such an analysis using BL-SOM (Kanaya et al., 2001), which can be accessed from the

web site.

Conclusion

Because of space constraints, we were unable to introduce all the existing databases and

tools; however, several hundreds of information resources for plant research have been

generated and are being updated. This implies that our research environment is also

improving. We can reduce the cost of research activities and efficiently focus on further

advancements in our studies, which will generate newly analysed results. In other words,

new information resources will be more enriched, and will certainly accelerate research in

the field of plant sciences. The recent technological advancement in the proteome and

metabolome research areas is remarkable. We strongly feel that there have been increasing

advances in studies on proteomics and metabolomics. Similarly, there is an increasing need

for the study of phenomics. Further, we should consider using new integrative techniques

and methods for our future research. If advancements in these are successful, we may

obtain further insights into cells and life systems.

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