Published online 19 November 2010

Nucleic Acids Research, 2011, Vol. 39, Database issue D677–D684
doi:10.1093/nar/gkq989

KaPPA-View4: a metabolic pathway databasefor representation and analysis of correlation
networks of gene co-expression and metaboliteco-accumulation and omics data
Nozomu Sakurai1,*, Takeshi Ara1, Yoshiyuki Ogata1, Ryosuke Sano1, Takashi Ohno1,
Kenjiro Sugiyama1, Atsushi Hiruta1, Kiyoshi Yamazaki1, Kentaro Yano2, Koh Aoki1,
Asaph Aharoni3, Kazuki Hamada2, Koji Yokoyama2, Shingo Kawamura2,
Hirofumi Otsuka2, Toshiaki Tokimatsu4, Minoru Kanehisa4, Hideyuki Suzuki1,
Kazuki Saito5,6 and Daisuke Shibata1
1

Kazusa DNA Research Institute, 2-6-7 Kazusa-kamatari, Kisarazu, Chiba 292-0818, 2Bioinformatics Laboratory,
Department of Life Sciences, School of Agriculture, Meiji University, 1-1-1, Higashi-Mita, Tama-ku, Kawasaki,
Kanagawa 214-8571, Japan, 3Department of Plant Sciences, Weizmann Institute of Science, POB 26,
Rehovot 76100, Israel, 4Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji,
Kyoto 611-0011, 5Graduate School of Pharmaceutical Sciences, Chiba University, 1-33 Yayoi-cho, Inage-ku,
Chiba, Chiba 263-8522 and 6RIKEN Plant Science Center, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama,
Kanagawa 230-0045, Japan

Received August 14, 2010; Revised October 4, 2010; Accepted October 5, 2010

ABSTRACT
Correlations of gene-to-gene co-expression andmetabolite-to-metabolite co-accumulation calculated
from large amounts of transcriptome and metabolome data are useful for uncovering unknown
functions of genes, functional diversities of genefamily members and regulatory mechanisms of
metabolic pathway flows. Many databases andtools are available to interpret quantitative transcriptome and metabolome data, but there are
only limited ones that connect correlation data tobiological knowledge and can be utilized to findbiological significance of it. We report here a newmetabolic pathway database, KaPPA-View4 (http://
kpv.kazusa.or.jp/kpv4/), which is able to overlay
gene-to-gene and/or metabolite-to-metabolite relationships as curves on a metabolic pathway map, oron a combination of up to four maps. This representation would help to discover, for example, novel
functions of a transcription factor that regulates
genes on a metabolic pathway. Pathway maps of
the Kyoto Encyclopedia of Genes and Genomes(KEGG) and maps generated from their gene classificationsareavailable
at
KaPPA-View4
KEGG version (http://kpv.kazusa.or.jp/kpv4-kegg/).
At present, gene co-expression data from the databases ATTED-II, COXPRESdb, CoP and MiBASE for
human, mouse, rat, Arabidopsis, rice, tomato andother plants are available.
INTRODUCTION
A vast amount of DNA microarray data has been
accumulated in public repositories, contributing tostudies such as investigations of gene expression patterns
and discoveries of marker transcripts. Furthermore, correlation coefﬁcients as indicators of co-expression between
genes, calculated from transcriptome data from a large
variety of tissues and cell conditions, and analyses of gene
co-expression networks whose member genes are connected with high correlation coefﬁcients, have contributed
to the discovery of novel functions of genes (1–3). For
instance, the functions of key genes in the biosynthesis of
glucosinolates (4), cell walls (5) and ﬂavonoids (6) in plants
were successfully uncovered by co-expression analyses.
Recent advances in co-expression analysis include the*To whom correspondence should be addressed. Tel: +81 438 52 3947; Fax: +81 438 52 3948; Email: sakurai@kazusa.or.jp
ß The Author(s) 2010. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/
by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

D678 Nucleic Acids Research, 2011, Vol. 39, Database issue

introduction of the mutual rank (MR) as a better index ofco-expression (7), and new strategies to extract networks
whose member genes are densely and speciﬁcallyco-expressed (8,9). Meanwhile, efforts to obtain comprehensive quantitative data of metabolites in organisms(metabolomes) have also been made by chromatography–
mass spectrometry and nuclear magnetic resonance (10,11),and the number of public databases in which metabolome
data are complied has been increasing (12). Correlation
analyses of metabolite-to-metabolite co-accumulations
have also been made (13–15). Müller-Linow et al. (16) suggested that the distance between metabolites on a metabolic
pathway was not related to the co-accumulation correlation. Camacho et al. (17) reported several factors that
relate to how metabolites co-accumulate. Correlations ofmetabolite co-accumulations are thought to be useful forunderstanding the regulation of metabolic pathways when
they are comparatively analyzed between different sets ofdata sources (18).
So far, numerous databases and tools have been developed to understand the biological signiﬁcance of transcriptome and metabolome data. Tools for functional
categorization of genes, referred to as gene ontology(GO), have been published (19). To promote a moreintuitive understanding, databases and tools that project
data onto metabolic pathway maps have been developed(20). As a source for pathway maps, data from the Kyoto
Encyclopedia of Genes and Genomes (KEGG) have beenused in many tools, including KegArray provided by
KEGG itself (http://www.genome.jp/kegg/download/
kegtools.html) (21–23). We have also developed andupgraded a web-based viewer, KaPPA-View, which canrepresent transcriptome and metabolome data simultaneously on a single pathway map (24–26), and it hascontributed to several plant studies (27–29). In contrast
to the quantity of software available for analyzing transcriptome and metabolome quantitative data, only a
few tools have so far been developed for studying geneto-gene and metabolite-to-metabolite correlation data. In
ATTED-II (30) and COXPRESdb (2), genes mapped onto
KEGG pathways are highlighted on a gene network ﬁgurethat is represented according to a user’s query gene.
MapMan can represent the correlation value for eachgene on a map against a query gene (31). Both systems
only represent correlations for a single gene, and there are
no databases or tools that can overview all the gene-togene relationships on the pathway maps. Furthermore, notools for projecting metabolite-to-metabolite correlations
onto pathway maps have been developed so far.
Here, we report a novel database system, KaPPAView4, which enables the overlay of correlation data
onto pathway maps. After reporting the ﬁrst version of
KaPPA-View for an Arabidopsis-speciﬁc ‘omics’ data
viewer (24), we added functions for correlation representation in version 2, and for multiple map representationand for managing multiple organisms in version 3. To
meet the demands of dealing with multiple and huge setsof correlation data in recent years, we reviewed the architecture of the database system, totally reconstructed the
web application, especially for the pathway display
module, and released the newest version, KaPPA-View4,

as an ‘omics’ database that can immediately display
results. The ‘omics’ viewer functions were extended toaccept data uploading from outside systems. As far as
we know, this version is a unique database system thatcan represent all gene-to-gene and metabolite-tometabolite correlations on metabolic pathway maps.
MATERIALS AND METHODS
Implementation
We are currently providing two versions of the system.
One is KaPPA-View4 Classic (http://kpv.kazusa.or.jp/
kpv4/) whose pathway maps are based on the traditional
KaPPA-View maps of Arabidopsis (24). Another is
KaPPA-View4 KEGG (http://kpv.kazusa.or.jp/kpv4kegg/), where pathway maps are acquired from KEGG.
KaPPA-View4 Classic is primarily for plant scientists,
implementing information for genome-sequenced plant
species: Arabidopsis, rice and Lotus japonicus; and forplant species whose Affymetrix GeneChips and correlation data are available: tomato, soybean, barley,
poplar, wheat, wine grape and maize. KaPPA-View4
KEGG is for general users, including animal, microorganism and also plant scientists. One of the major advantagesof KaPPA-View4 KEGG is that we can share the latest
results of KEGG’s continuous effort of curation of genedescriptions, categorizations and assignments on themaps. At present, we provide information for 15 species,
namely human, mouse, rat, Caenorhabditis elegans,
Drosophila melanogaster, Arabidopsis, rice, poplar, castor
bean, sorghum, wine grape, maize, Physcomitrella patens
subsp. patens, Escherichia coli and budding yeast. Detailsof the basic information of the default installed data areshown in Supplementary Data Section 1. Both versions
are freely available.
Preparation of map data
In KaPPA-View4 Classic, the map data is based on
approximately 150 leaves of Arabidopsis pathway maps
generated for the initial version (24). The assignment ofgenes for the other plant species was done by sequencehomology searches by the blastx or blastp program
against Arabidopsis amino acid sequences TAIR9_pep
provided by The Arabidopsis Information Resource(TAIR, http://www.arabidopsis.org/). The best hit
Arabidopsis genes for each gene or target sequence ofAffymetrix GeneChip probes of the other species weredeﬁned as those having the minimum e-value among candidates with an e-value threshold of 1  10 30. Then thegenes or probes were associated with the enzymatic reactions where the best hit Arabidopsis genes are assigned.
For Lotus japonicus, two extra pathway maps for
isoﬂavonoid biosynthesis (I and II) were added and thegene assignments on the maps are manually performed.
A pipeline was constructed by Java (www.java.com) to
download ﬁles from the KEGG FTP (ﬁle transfer
protocol) and generate all the data ﬁles required for the
KaPPA-View4 KEGG setup including gene, compoundand reaction information and pathway map data. The
pathway maps were arranged as a tree structure for eachNucleic Acids Research, 2011, Vol. 39, Database issueorganism according to the KEGG BRITE hierarchy, andbird’s eye maps (24) were generated for the nodes of thetree. The hierarchically categorized gene family data
‘Genes and Proteins’ in the KEGG BRITE was added
to the tree where each family was represented as a map
and its member genes were arrayed on the map (Figure 2,
top-right map). During setup of the version on 14 June
2010, 290 and 379 sheets of metabolic pathway maps andgene family maps, respectively, were installed.
Preparation of gene-to-gene correlation data
Table 1 summarizes the gene co-expression data we
provide in the KaPPA-View4 system by default. The
data ﬁles were downloaded from ATTED-II and
COXPRESdb or provided by MiBASE (32) and CoP(33). In the case of the co-expression data described in
Pearson’s correlation coefﬁcients (PCC), strong positive
and negative correlations were ﬁltered at thresholdvalues of 0.6 and 0.6. In the case of mutual rank(MR), only positive values 100 were selected. In thecase of CoP data, signiﬁcant gene-to-gene relationships
evaluated as ‘Co-expressed Genes’ (33) were extracted,
and the co-expression values were described as cosign
theta correlation coefﬁcients.
In KaPPA-View4 KEGG, the gene IDs originated fromthe entry IDs of KEGG. In the case of rice, KEGG entry
IDs were derived from Entrez Gene IDs from the
National Center for Biotechnology Information (NCBI,

D679

http://www.ncbi.nlm.nih.gov/), while the co-expression
data from ATTED-II and CoP were written as gene IDs
from The Rice Annotation Project Database (RAP-DB,
http://rapdb.dna.affrc.go.jp/). Therefore, the gene IDs in
the co-expression data were converted to NCBI Entrez
Gene IDs according to the gene information ﬁle of
KEGG for rice.
System development
The web application of the KaPPA-View4 system wasdeveloped with Java 1.6 (www.java.com), Tomcat 5.5 (tomcat.apache.org), MySQL 5.0 (dev.mysql.com) and Flex
SDK 3 (opensource.adobe.com/wiki/display/ﬂexsdk/).
We chose the Adobe Flash Player (ver. 9.0 or higher) todisplay the map representations on Internet browsers. All
the map data on KaPPA-View4 were prepared in the SVG(scalable vector graphics) format, and the map data areconverted to Flash objects when displayed. Therefore, the
SVG Viewer plug-in that had been required for previous
versions is no longer needed for KaPPA-View4. This
modiﬁcation has remarkably improved the processing
speeds of map representations, thus avoiding longer
rendering time on the SVG Viewer plug-ins, especiallyfor the maps where a lot of objects were drawn, e.g.
maps in the Multiple Map mode (mentioned later) witha lot of genes and correlation curves. It also improved
compatibility with Internet browsers and operating
systems.

Table 1. Gene-to-gene co-expression correlation data installed in KaPPA-View4
Species

SourceNo. of Chipsa

for both KaPPA-View4 Classic and KaPPA-View4 KEGG
Arabidopsis thaliana
ATTED-II, ver. c4.1
1388

Threshold

Data name in KaPPA-View4

Ath: ATTED-II c4.1 (1388 chips) MR <= 100
Ath: ATTED-II c4.1 (1388 chips) PCC >= 0.6
Ath: ATTED-II c4.1 (1388 chips) PCC <= –0.6
Ath: CoP (5257 chips) CCCd Co-expressed Genese
Osa: ATTED-II c1.0 (208 chips) MR <= 100
Osa: CoP (432 chips) CCC Co-expressed GenesCoP
ATTED-II, ver. c1.0f
CoPf
for only KaPPA-View4 Classic
Solanum lycopersicum
MiBASE
Glycine max
CoP
Hordeum vulgare
CoP
Poplus trichocarpa
CoP
Triticum aestivum
CoP
Vitis vinifera
CoP
Zea mays
CoP
for only KaPPA-View4 KEGG
Homo sapiens
COXPRESdb, ver. c3.1

5257
208
432

MRb  100
PCCc  0.6
PCC  –0.6–
MR  100
–

50
2994
403
95
368
210
263

MR  100
–
–
–
–
–
–

Sly: MiBASE (50 chips) MR <= 100
Gma: CoP (2994 chips) CCC Co-expressed Genes
Hvu: CoP (403 chips) CCC Co-expressed Genes
Ptr: CoP (95 chips) CCC Co-expressed Genes
Tae: CoP (368 chips) CCC Co-expressed Genes
Vvi: CoP (210 chips) CCC Co-expressed Genes
Zma: CoP (263 chips) CCC Co-expressed Genes

4401

Mus musculus

COXPRESdb, ver. c2.1

2226

Rattus norvegicus

COXPRESdb, ver. c1.1

632

MR  100
PCC  0.6
PCC  –0.6
MR  100
PCC  0.6
PCC  –0.6
MR  100
PCC  0.6
PCC  –0.6

Hsa: COXPRESdb c3.1 (4401 chips) MR <= 100
Hsa: COXPRESdb c3.1 (4401 chips) PCC >= 0.6
Hsa: COXPRESdb c3.1 (4401 chips) PCC <= –0.6
Mmu: COXPRESdb c2.1 (2226 chips) MR <= 100
Mmu: COXPRESdb c2.1 (2226 chips) PCC >= 0.6
Mmu: COXPRESdb c2.1 (2226 chips) PCC <= –0.6
Rno: COXPRESdb c1.1 (632 chips) MR <= 100
Rno: COXPRESdb c1.1 (632 chips) PCC >= 0.6
Rno: COXPRESdb c1.1 (632 chips) PCC <= –0.6

Oryza sativaaNumber of DNA microarray chips for correlation calculations.
MR: Mutual rank deﬁned by Obayashi et al. (7).
PCC: Pearson’s correlation coefﬁcient.
d
CCC: Cosign theta correlation coefﬁcient.
e
All signiﬁcant gene-to-gene relationships evaluated as ‘Co-expressed Genes’ in CoP (33).
f
The gene IDs in the original data were converted to NCBI Entrez Gene IDs for KaPPA-View4 KEGG (see ‘Materials and Methods’ section).
bc

D680 Nucleic Acids Research, 2011, Vol. 39, Database issue

Both KaPPA-View4 Classic and KaPPA-View4 KEGG
run on a Red Hat Enterprise Linux ES v.4 server equippedwith a 3.2 GHz 64 bit Intel Xeon CPU and 2 GB RAM.
WEB APPLICATION
Representation of co-expression data
Gene-to-gene co-expression data are represented as curves
on pathway maps (colored in red in default) after selecting
the kind of data listed in the control panel placed below
the pathway map, displayed in the Map View function,
after selecting the species and pathway maps to view
(Figure 1a). All correlations ﬁltered by the control panel
are seen here. As shown in Figure 1a, the program ﬁnds
that a lot of the genes in the Calvin Cycle are co-expressedin Arabidopsis. Pathway maps having dense correlations
are distinguished on the bird’s eye map set to correlation
mode (Figure 1b). When the species is switched to tomato
and the correlation data is set to the tomato, we see thatthe genes in the Calvin Cycle are densely connected too,
implying that this is a general expression tendency acrossplant species (Figure 1c, see ‘Discussion’ section).
Metabolite-to-metabolite co-accumulations can also beoverlaid on pathway maps simultaneously with gene
co-expression and ‘omics’ data (Figure 1d, curves inblue). Users can utilize not only the default correlation
data but also user-created ones (as described later).
Correlations across the maps
The KaPPA-View4 system enables up to four maps to be
displayed in a single browser window (Multiple Mapmode) with correlation curves represented, resulting invisualization of correlations across the maps (Figure 2).
Users can choose the combination of the pathway maps
for the Multiple Map mode. In KaPPA-View4 KEGG,
gene family maps can be included too (Figure 2,
top-right panel). This greatly facilitates analysis of
non-metabolic pathway genes such as protein kinases
and transcription factors that are not available in other
KEGG-based programs developed so far. Furthermore,user-created SVG maps (Figure 2, bottom-right) andsimple maps where genes for user-input gene IDs arearrayed (Figure 2, bottom-left) are included in the representation in the Multiple Map mode. Therefore, correlation representations in the Multiple Map mode help
researchers primarily to understand relationships
between genes and metabolic pathways, which could
also be described as functional modules of biologicalsystems. As exempliﬁed in Supplementary Data
Section 2, it is possible to ﬁnd potential genes regulating
certain metabolic pathways by representing correlation
curves between the gene family maps and the pathway
maps (see ‘Discussion’ section).
Utilization of user’s data
Users are allowed to upload their own correlation data, as
well as their experimental data and map data, and utilize
them for their own analyses. Therefore, users can analyze
not only the correlation data calculated from the large

variety of sample conditions, but also data obtained
from sets of speciﬁc treatments to focus ontreatment-dependent responses. This functional extension
should especially help analyze metabolite correlations thatare presently of limited public availability. Furthermore,users can freely create their own user accounts to savethese data on the KaPPA-View4 server, which allows
users to start analyses immediately after login.
‘Omics’ data comparison between multiple species
As an extra mode for map representations, the
KaPPA-View4 system has a ‘Universal Map’ mode
where genes from multiple species are drawn simultaneously on a single map. This mode helps to recognize
the differences in gene assignments and orthologous
genes between the species selected. In this mode, transcriptome and metabolome data from different species canbe compared on the maps (Figure 3). This should help
study the diversity of paralogs and their functional differences between species by comparing omics data obtained
from different species under similar physiologicalconditions.
Viewing the data on outside systems
The ability to represent transcriptome and metabolome
data on the pathway maps was extended to an API (application programming interface), which is accessible fromoutside systems. Databases and application programs that
store omics data can upload their data to KaPPA-View4
by the API and view it directly from their systems without
logging in to KaPPA-View4 through a web user interface.
As an example, the microarray data stored in MiBASE are
immediately represented on KaPPA-View4 when users
click the ‘view’ button in the data list of MiBASE(Supplementary Data Section 3). Since the session is
kept alive as long as the browser is running, theuploaded data are stacked as temporary data on the
KaPPA-View4 server. This allows users to set andcompare other pairs of data.
DISCUSSION
Correlation analyses on KaPPA-View4
KaPPA-View4 is a unique and novel database systemthat helps users study the biological signiﬁcance of correlation data by overlaying it onto pathway maps.
ATTED-II (30), COXPRESdb (2) and MapMan (31)can represent gene-to-pathway relationships, but the
representations are restricted to a single query gene. One
advantage of these systems is that users can get all
the information for the query gene. In contrast, in
KaPPA-View4, whose subject is pathway information,
all the correlations are represented on the pathway
maps, although the representations are restricted to theselected pathways. As far as we know, KaPPA-View4 is
the only database that can overview all the correlations on
pathway maps.
One potential application of representing data in the
KaPPA-View4 style is hypothesis generation. We foundNucleic Acids Research, 2011, Vol. 39, Database issue

D681

Figure 1. Representation of correlation networks on metabolic pathway maps in KaPPA-View4 Classic. (a) Curves representing gene-to-gene correlations (red) are overlaid on the pathway map of the Calvin Cycle in Arabidopsis. Co-expression data from ATTED-II (MR  100) was selected.
The pathway tree for selecting a map to view is placed on the left. (b) A Bird’s Eye Map of KaPPA-View4 is displayed by clicking a node of thepathway tree. Nodes (pathway groups) are depicted as round rectangles and each metabolic pathway map included in a group is indicated as a roundrectangle in the round rectangle. The density of the correlation curves for each map is represented by a color scale. Summaries of the omics data andpathway names of the bars can also be shown. (c) Map of the Calvin Cycle for tomato overlaid with the gene co-expression data of tomato.
Co-expression data from MiBASE (MR  100) was selected. One to several genes for most of the reactions were co-expressed with each other as withthose in Arabidopsis shown in (a). (d) Metabolite-to-metabolite correlations can be represented on the maps simultaneously with gene co-expression,transcriptome and metabolome data. The sample data installed in KaPPA-View4 Classic are represented on the map of leucine, valine, isoleucine andalanine biosynthesis. The detail of how the ﬁgures are produced on the KaPPA-View4 system is shown in Supplementary Data Section 4.

D682 Nucleic Acids Research, 2011, Vol. 39, Database issue

Figure 2. Representation of correlation networks on a combination of four maps (Multiple Map mode) in KaPPA-View4 KEGG. The correlation
curves are drawn across the maps. The gene co-expression data from COXPRESdb for human (c3.1, MR  30) was selected. The map ‘Regulation ofactin cytoskeleton’ from the KEGG Pathway was placed at the top-left as an example of a metabolic pathway map. The map for Rab family proteinsis an example of a gene family map generated from the KEGG BRITE classiﬁcation (top-right). A Simple map was created by the gene IDs forthiamine monophosphate phosphohydrolases (bottom-left). A user map depicting p53 negative feedback (bottom-right) was created by the free SVG
drawing software Inkscape 0.47 (www.inkscape.org). The detail of how the ﬁgure is produced on the KaPPA-View4 system is shown in
Supplementary Data Section 4.

that one of the genes classiﬁed as a helix-turn-helix typetranscription factor in human is related to many of thegenes on the map of Hypertrophic cardiomyopathy(HCM) (Supplementary Data Section 2). The gene,
CSRP3 (cysteine and glycine-rich protein 3, cardiac LIM
protein, NCBI Entrez Gene ID: 8048), is also highly
co-expressed with a series of genes on the maps ofCardiac muscle contraction, Dilated cardiomyopathy(DCM) and Cell Communication/Tight junction(Supplementary Figure S2b). There is a recent reportshowing that CSRP3 is indeed involved in HCM and
DCM (34). Although the function of the CSRP3 genehas already been reported and the gene might not act astranscription factor in this case, this example clearly showsthat representing co-expression networks on pathway
maps is a powerful approach to discovering novel genefunctions.
Another advantage of the KaPPA-View4 style is shownin Figure 1 where a similarity in gene co-expression
networks is observed in the Calvin Cycle between
Arabidopsis and tomato. Differences in networks concerning the genes of ribulose bisphosphate carboxylases andtriosephosphate isomerases were observed too. To depict
the similarities and differences of the networks using

currently existing databases and tools that depend on
query genes, we would need to obtain correlation data
for all the genes in the Calvin Cycle for each species,
then re-categorize the data according to the gene assignments to the reactions, and compile them. As
KaPPA-View4 is a pathway map oriented system, we
can intuitively understand the nature of the relationships
and discuss the signiﬁcance of the similarities and differences observed. This is also an advantage when comparingthedifferences
betweenmetabolite-to-metaboliteco-accumulation data sets, as comparative correlation
analyses have been suggested to be useful for understanding the regulation of metabolic ﬂows (18).
As described here, pathway map-oriented representation of correlation networks in KaPPA-View4 helps
researchers to understand biological systems and to
generate working hypotheses to advance their studies,and powerfully complements existing databases andtools such as ATTED-II, COXPRESdb and MapMan.
Future developments
We have constructed a pipeline to prepare all the information ﬁles required for KaPPA-View4 KEGG. However,manual operations are still required for updating theNucleic Acids Research, 2011, Vol. 39, Database issue

D683

ACKNOWLEDGEMENTS
Most of the system programming was done by Axiohelix
Co. Ltd. (www.axiohelix.com/en). The authors thankDr Toshio Aoki of Nihon University for preparation ofpathway maps of isoﬂavonoids biosynthesis of Lotus
japonicus. We are grateful to all the people who had
been engaged in the PMPj for preparation of thepathway maps, testing of the system and making usefulcomments.
FUNDINGNew Energy and Industrial Technology Development
Organization (NEDO, Japan) as part of a project
entitled the ‘Development of Fundamental Technologies
for Controlling the Material Production Process of Plants’(PMPj) [P02001]; NEDO as part of a project named the‘New Energy Technology Development’ [P07015]; Japan
Society for the Promotion of Science (JSPS),
Grants-in-Aid for Scientiﬁc Research (B) [2038022 toK.Y.]; JSPS, Grants-in-Aid for Special Research on
Priority Areas [19043015 and 21024010 to K.Y.].
Funding for open access charge: Kazusa DNA ResearchInstitute Foundation.
Conﬂict of interest statement. None declared.
Figure 3. Comparative
representationoftranscriptome
andmetabolome data between Arabidopsis and rice in the Universal Map
mode. The species to be represented on the maps can be selected using
the ‘Select Species’ button below the pathway diagram. The data forboth species were randomly generated as examples, and the values are
represented as colors. In the rectangles for the enzyme reactions, bothof the genes from Arabidopsis (Ath) and rice (Osa) are represented. The
metabolome data from Arabidopsis is represented in the upper part ofthe circles for the metabolites, and the data from rice is in the lower
part. The detail of how the ﬁgure is produced on the KaPPA-View4
system is shown in Supplementary Data Section 4.

ﬁles. As the KEGG data is updated once a week, we are
now developing a program to automate this procedure sothat the data on KaPPA-View4 KEGG are always kept up
to date.
AVAILABILITY
Both KaPPA-View4 Classic and KaPPA-View4 KEGG
are freely accessible to the public. User accounts to savethe user’s data can be created freely. The KaPPA-View4
system program is freely available for academic users
upon request. All the information ﬁles for genes, compounds, enzyme reactions and pathway map hierarchies
are available from the Download function in the web application. The pathway map data in SVG can be downloaded from the map representation window. We will
support the updating and editing of default data according
to users’ requirements.
SUPPLEMENTARY DATA
Supplementary Data are available at NAR Online.

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