1Kazusa DNA Research Institute, Kazusa-Kamatari 2-6-7, Kisarazu, Chiba, 292-0818 Japan
2Graduate School of Pharmaceutical Science, Chiba University, Yayoi-cho 1-33, Inage-ku, Chiba, 263-8522 Japan

Various public databases provide Arabidopsis gene
information via the internet. It is useful to abstract
information obtained from such databases. We havedeveloped the KAGIANA tool, which allows a user toretrieve summary information obtained from selective
databases and to access pages for a gene of interest in
those databases. The tool is based on Microsoft Excel andprovides several macro programs for gene expression
analyses. It can assist plant biologists in accessing omics
information for plant biology. The KAGIANA tool is freely
available at http://pmnedo.kazusa.or.jp/kagiana/.
Keywords: Annotation • Arabidopsis • Database • Gene
expression • Omics.
Abbreviations: AGI, Arabidopsis Genome Initiative; GO,
Gene Ontology; NCBI, The National Center for Biotechnology
Information; TAIR, The Arabidopsis Information Resource.
Since the completion of the genome sequence of the model
plant Arabidopsis thaliana (Arabidopsis Genome Initiative
2000), advances in genome and gene expression analysis
have resulted in a vast number of data sets generated for
Arabidopsis. Data sets of the Arabidopsis genome sequence
are available at GenBank (Benson et al. 2008; http://www.
ncbi.nlm.nih.gov/entrez/query.fcgi?db=Nucleotide) and DDBJ
(Sugawara et al. 2008; http://www.ddbj.nig.ac.jp/). In Arabidopsis, the sequence is separated into ﬁve chromosomes,
which have 33,282 gene loci according to TAIR8 (Swarbreck
et al. 2008; http://www.arabidopsis.org/). Amino acid sequencesof proteins encoded by genes have been analyzed for various
purposes. On the basis of localization signals included insuch sequences, subcellular localization of proteins has been
predicted using tools such as WoLF PSORT (Horton et al. 2007;
http://wolfpsort.org/) and TargetP (Emanuelsson et al. 2007;

http://www.cbs.dtu.dk/services/TargetP/). Domain structures,
which show functional properties in proteins, have been
predicted on the basis of amino acid sequences using
tools such as SCOP (Andreeva et al. 2008; http://scop.
berkeley.edu/), and can be found in databases such as
InterPro (Mulder et al. 2007; http://www.ebi.ac.uk/interpro/). Analysis of transmembrane domains has been
performed using tools such as TMHMM (Moller et al.
2001; http://www.cbs.dtu.dk/services/TMHMM/) and thatof hydropathy can be found in databases such as SUBA
(Heazlewood et al. 2007; http://www.plantenergy.uwa.edu.
au/suba2/). Data sets of gene expression are available fromdatabases such as the Gene Expression Omnibus (GEO)
database (Barrett et al. 2006; http://www.ncbi.nlm.nih.gov/geo/). Several databases such as ATTED-II (Obayashi et al.
2007; http://atted.jp/) and Genevestigator (Zimmermann
et al. 2004; https//www.genevestigator.ethz.ch/) provide afunction to perform gene expression analysis. Using a vast
number of gene expression data sets, approaches for
detecting co-expressed genes, such as the ARACNE tool
(Margolin et al. 2006; http://amdec-bioinfo.cu-genome.org/
html/ARACNE.htm), the average clustering coefﬁcient index(Gupta et al. 2006) and the Confeito algorithm (http://pmnedo.kazusa.or.jp/kagiana/coexprocess/) have been developed. On the basis of these analyses, molecular function,
subcellular localization and biological processes of genes
have ﬁnally been consistently assigned to ‘molecular function’, ‘cellular component’ and ‘biological process’, respectively, the three aspects of the Gene Ontology(GO) terminology (Gene Ontology Consortium 2008; http://
geneontology.org/).
To obtain genomic and transcriptomic information on
genes of interest, a user can visit these databases and accessthese tools via the internet or download them for personal*Corresponding author: E-mail, shibata@kazusa.or.jp; Fax, +81-438-52-3948.
Plant Cell Physiol. 50(1): 173–177 (2009) doi:10.1093/pcp/pcn179, available online at www.pcp.oxfordjournals.org© The Author 2008. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists. All rights reserved.
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open
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Plant Cell Physiol. 50(1): 173–177 (2009) doi:10.1093/pcp/pcn179 © The Author 2008.

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Yoshiyuki Ogata1, Nozomu Sakurai1, Koh Aoki1, Hideyuki Suzuki1, Koei Okazaki1, Kazuki Saito2 andDaisuke Shibata1,*

Short Communication

KAGIANA: An Excel-Based Tool for Retrieving Summary
Information on Arabidopsis GenesY. Ogata et al.

The KAGIANA tool is downloadable as a ZIP-format ﬁleat http://pmnedo.kazusa.or.jp/kagiana/. The KAGIANA tool
is formatted as a Microsoft Excel workbook ﬁle, composed ofﬁve worksheets [one database sheet (‘Data20080524’), tworeadme sheets (‘ReadMe_1st’ and ‘ReadMe_Tools’) and two
retrieval sheets (‘Selected_Link’ and ‘Selected_GO’)] andone macro program (‘Tools’) comprising four analysis tools(‘Confeito’, ‘GX bar chart’, ‘GO pie chart’ and ‘ATTED chart’).
In KAGIANA, AGI codes (e.g. At1g01010) are used for the
retrieval and performance of the tool.
The database sheet is composed of the following information for 33,362 loci (Fig. 1A), which was obtained from theTAIR database. First, the A to D columns represent AGI
codes, a short description, description, and identiﬁers for
NCBI, respectively. Secondly, the E to J columns display representative GO terms, which certainly accompany the evidence codes, and their Evidence Code categories, which areabbreviated as ‘X’ (experimental) for EXP, IDA, IPI, IMP, IGI
and IEP; ‘S’ (statement) for TAS and IC; ‘C’ (computational) for
ISS, ISO, ISA, ISM, IGC and RCA; ‘L’ (electronic) for IEA; and‘N’ (not available) for NAS and ND, in the three aspects of GO
terminology, i.e. cellular component (the E and F columns),
molecular function (the G and H columns) and biologicalFig. 1 Composition of the KAGIANA worksheets. (A) The database sheet, including summary information of Arabidopsis genes obtained
from the selected databases. (B) The sheet for hyperlinks to the selected public databases, as shown in Table 1. (C) The sheet for summary
information of the selected omics analyses, i.e. GO terms and results from analyses of WoLF PSORT, TargetP, SCOP and TMHMM.

174

Plant Cell Physiol. 50(1): 173–177 (2009) doi:10.1093/pcp/pcn179 © The Author 2008.

Downloaded from http://pcp.oxfordjournals.org at Institute of Physical and Chemical Research(RIKEN) on April 7, 2010use. However, to retrieve the information, users generally
require knowledge of the omics information published in
the databases; for example, how to select an adequate website and how to set an adequate threshold value such as thegene-to-gene correlation coefﬁcient for acquiring data ofinterest in the website. For biological users, unfamiliar withomics analyses such as genomics and transcriptomics, it isuseful to have access to abstracted gene information fromsuch databases and analyses and to use quick links to these
databases.
We have developed the KAGIANA (Kazusa Arabidopsis
Gene Information And Network Analysis) tool to summarize various Arabidopsis omics analyses from the abovementioned databases and tools, and to provide links to pages
for genes of interest in the databases. The tool is based on
Microsoft Excel (version 2003 or higher) and thus requires
only enough skill for basic Excel operation. The implementation of this tool is veriﬁed using Windows XP or higher for
PC, and OS X or higher for Macintosh. The macro programsof the tools are available only for Windows users as ofNovember 2008. Our goal is to assist plant biologists inaccessing information from omics analyses so that they can
incorporate it into their plant biology research.

KAGIANA for retrieval of Arabidopsis genomic data

In the ‘Selected_GO’ sheet, a user can retrieve information
on genes of interest from various omics analyses (Fig. 1C), i.e.
the three GO term aspects, WoLF PSORT, TargetP, SCOP
and TMHMM as mentioned above. Steps for retrieval aresimilar to those in the ‘Selected_Link’ sheet. The terms in
the third row are the same as those in the database sheet
mentioned above, and the ‘ReadMe_1st’ sheet has the explanation for such retrieval. By selecting the ‘Selected_Link’ andthe ‘Selected_GO’ sheets, a user can manage to operatethem simultaneously, e.g. when inputting AGI codes.
KAGIANA provides ‘Tools’ macro programs including thefour analyses (Fig. 2A), i.e. including ‘Confeito’, ‘GX bar chart’,‘GO pie chart’ and ‘ATTED chart’. The ‘Confeito’ tool allows
a user to extract co-expressed genes using the Confeito algorithm on the basis of a co-expression network approach(http://pmnedo.kazusa.or.jp/kagiana/coexprocess/). The way
to use the tools is described in the ‘ReadMe_Tools’ sheet.
The ‘GX bar chart’ tool allows a user to depict bar charts of gene
expression proﬁles for multiple genes of interest (Fig. 2B).
Bar charts are depicted using 1,245 DNA microarray datafrom the AtGenExpress project, which are available at http://
www.weigelworld.org/resources/microarray/AtGenExpress/. The ‘GO pie chart’ tool allows a user to depict a pie
chart of the distribution of GO-SLIM terms associated withmultiple genes of interest (Fig. 2C). GO-SLIM terms are available at the TAIR database. This tool counts all multiple GOSLIM terms assigned to a gene. For this version of KAGIANA,
such terms were obtained at May 2008. The ‘ATTED chart’tool helps users download the charts of AtGenExpress gene
expression proﬁles for individual genes from the ATTED
database onto a worksheet in KAGIANA per gene.

Downloaded from http://pcp.oxfordjournals.org at Institute of Physical and Chemical Research(RIKEN) on April 7, 2010

process (the I and J columns), respectively. A GO term wasselected as the representative term for each aspect for agene, according to the order of Evidence Code categories, i.e.
X, S, C, L and N. The following columns represent information from the analytical tools. The K and L columns represent data from WoLF PSORT, which predicts the subcellular
localization of proteins, and the reliability index, whose bestscore is 14, respectively. The M column represents informationfrom TargetP, which also predicts subcellular localization,and the reliability index, ranging from 0 to 9 at the maximum.
The N and O columns represent that from SCOP, which predicts domains of proteins, and the reliability index, whichis the negative logarithm of the actual value, respectively.
The P column represents TMHMM, which predicts thenumber of transmembrane domains of proteins.
The ‘Selected_Link’ sheet provides hyperlinks to 19 selectedpublic databases for information retrieval of genes of interestand their Short Description and Description (Fig. 1B). Thesehyperlinks lead a user to the pages for individual genes in theindividual databases by the following steps: (i) input AGI
code(s) in the A column from the A4 cell to the lower cells(e.g. input ‘At1g01010’ in the A4 cell and ‘At1g01020’ in theA5 cell); (ii) select the range of the B4 to the W4 cells; and(iii) double-click the right lower corner (a black square) tocopy the equations in the fourth row into the lower rows in
the same columns (e.g. copy the B4-W4 into the B5-W5).
Then, a user can access a database of interest from among
the C to U columns (e.g. click the T4 cell for access to thepage for the query gene in the KaPPA-View tool). The tool
provides access to the databases shown in Table 1. The way
to use this sheet is also described in the ‘ReadMe_1st’ sheet.
Table 1 Abstract of databases linkable from the KAGIANA tool
Abbreviation

Databases

URL

TA
EG
MP
IP
TD
SB
KG
GR
DN
SH
GA
GC
RV
eB
AV
AT
AC
KV
AP

TAIR (Swarbreck et al. 2008)
Entrez Gene (Maglott et al. 2005)
MPSS (Brenner et al. 2000)
InParanoid (Berglund et al. 2008)
SIGnAL (Alonso et al. 2003)
SUBA (Heazlewood et al. 2007)
KEGG (Kanehisa et al. 2008)
GRAMENE (Liang et al. 2008)
NASCArrays Digital Northern (Craigon et al. 2004)
NASCArrays Spot History (Craigon et al. 2004)
Genevestigator Gene Atlas (Zimmermann et al. 2004)
Genevestigator Gene Chronologer (Zimmermann et al. 2004)
Genevestigator Response Viewer (Zimmermann et al. 2004)
eFP Browser (Winter et al. 2007)
AtGenExpress Visualization Tool
ATTED-II (Obayashi et al. 2007)
AraCyc (Zhang et al. 2005)
KaPPA-View 3 (Sakurai and Shibata 2006)
AtProteome (Baerenfaller et al. 2008)

http://www.arabidopsis.org/
http://www.ncbi.nlm.nih.gov/sites/entrez?db=gene
http://mpss.udel.edu/at/
http://inparanoid.sbc.su.se/cgi-bin/index.cgi
http://signal.salk.edu/cgi-bin/tdnaexpress
http://www.plantenergy.uwa.edu.au/applications/suba2/
http://www.genome.jp/kegg/
http://www.gramene.org/
http://affymetrix.arabidopsis.info/narrays/digitalnorthern.pl
http://affymetrix.arabidopsis.info/narrays/spothistory.pl
https://www.genevestigator.ethz.ch/gv/index.jsp
https://www.genevestigator.ethz.ch/gv/index.jsp
https://www.genevestigator.ethz.ch/gv/index.jsp
http://bbc.botany.utoronto.ca/efp/cgi-bin/efpWeb.cgi
http://jsp.weigelworld.org/expviz/expviz.jsp
http://atted.jp/
http://www.arabidopsis.org/biocyc/index.jsp
http://kpv.kazusa.or.jp/kappa-view3/
http://fgcz-atproteome.unizh.ch/

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Y. Ogata et al.

A

Gene Expression Bar Chart

1200

1200

Expression

800
At1g03495
At1g56650

600

At3g29590
At4g14000
At4g22870

400

At5g17220
At5g19470
At5g42800

200

CLight(55)

Hor mone(110)Biotic Stress(134)

Short Day(118)

Abiotic Stress(398)Long Day(161)

Root(183)

Continuous(281)

Stem(10)

Leaf(275)

Pollen(9)Flower(76)Transition(16)Apex(109)

Seedling,8d-(33)Inflorescence(197)

Seedling,5-7d(175)

Seedling,1-4d(102)Seed(54)0

At5g54060

Molecular Function: 9genes

transferase activityhydrolase activity
other enzyme activity
other molecular functionstranscription factor activity
othersmolecular function unknown

Fig. 2 Composition of the KAGIANA tools. (A) The window of the ‘Tools’ macro program, including analyses of ‘Confeito’, ‘GX bar chart’, ‘GO
pie chart’ and ‘ATTED chart’. (B) The result from the ‘GX bar chart’ analysis. (C) The result from the ‘GO pie chart’ analysis, showing the aspectof molecular function.

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B

KAGIANA for retrieval of Arabidopsis genomic data

Funding
The New Energy and Industrial Technology Development(NEDO) program, part of the ‘Development of Fundamental
Technologies for Controlling the Material Production Process of Plants’ project.

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Detailed steps for using these tools are described in the‘ReadMe_Tools’ sheet in the KAGIANA workbook. Brieﬂy,
the steps are (i) click ‘Tools’ in the menu bar; (ii) select‘Macro’ and click ‘Macros’; (iii) select ‘Tools’ in the macro
box and click ‘Execute’ (open the ‘Tools’ window); (iv) selecta tool in the ‘Analysis’ frame in the window; (v) input AGI
codes into different lines in the textbox left of the frame; (vi)select the option frame when selecting ‘GX bar chart’ and‘GO pie chart’ tools; and then (vii) click the ‘OK’ button if
the character color on the button is black (otherwise, there
is insufﬁcient information for retrieval).

(Received October 22, 2008; Accepted November 17, 2008)

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