Nucleic Acids Research Advance Access published online on September 27, 2008
Nucleic Acids Research, doi:10.1093/nar/gkn638
© 2008 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
RiceGeneThresher: a web-based application for mining genes underlying QTL in rice genome
Supat Thongjuea1,2,
Vinitchan Ruanjaichon1,2,
Richard Bruskiewich3 and
Apichart Vanavichit1,*
1RiceGeneDiscovery Unit, Kasetsart University, Kamphangsaen Campus, Nakhon Pathom 73140, 2National Center for Genetic Engineering and Biotechnology, 113 Thailand Science Park, Phahonyothin Road, Klong 1, Klong Luang, Pathumthani 12120, Thailand and 3Crop Research Informatics Laboratory – International Rice Research Institute (IRRI), DAPO Box 7777, Metro Manila, Philippines
*To whom correspondence should be addressed. Tel: 66 34 355 193; Fax: 66 34 355 197; Email: vanavichit{at}gmail.com
Received July 24, 2008. Revised September 10, 2008. Accepted September 15, 2008.
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ABSTRACT
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RiceGeneThresher is a public online resource for mining genes
underlying genome regions of interest or quantitative trait
loci (QTL) in rice genome. It is a compendium of rice genomic
resources consisting of genetic markers, genome annotation,
expressed sequence tags (ESTs), protein domains, gene ontology,
plant stress-responsive genes, metabolic pathways and prediction
of protein–protein interactions. RiceGeneThresher system
integrates these diverse data sources and provides powerful
web-based applications, and flexible tools for delivering customized
set of biological data on rice. Its system supports whole-genome
gene mining for QTL by querying using DNA marker intervals or
genomic loci. RiceGeneThresher provides biologically supported
evidences that are essential for targeting groups or networks
of genes involved in controlling traits underlying QTL. Users
can use it to discover and to assign the most promising candidate
genes in preparation for the further gene function validation
analysis. The web-based application is freely available at
http://rice.kps.ku.ac.th.
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INTRODUCTION
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Genetic studies of many traits in rice over the past decade
have generated data suggesting that specific regions of rice
chromosomes contain sites that influence expressions of quantitative
traits or quantitative trait loci (QTL). In recent years, many
quantitative traits in rice have been discovered by QTL mapping.
There are more than 8000 QTL controlling various complex traits
that have been located on different chromosome regions in rice
(
http://www.gramene.org/qtl/index.html). The goal of QTL mapping
is to identify the genes underlying polygenic traits and gain
a better understanding of their physiology and biochemistry
(
1). To identify genes, the candidate gene approach has been
applied in plant genetics in the past decade for the characterization
and cloning of QTL. It constitutes a complementary strategy
to map-based cloning for identifying the genes placed within
the QTL intervals (
2). The process of selecting candidate genes
relies on a wealth of information gained through traditional
genetics and molecular approaches (
3). Presently, many publicly
useful biological data, especially, the high-throughput technologies
are significantly increasing the volume of biological information
to assist gene function identification. Most of these biological
data are published on the public domain databases. Scientists
target these databases to apply bioinformatics approaches and
data integration systems to find the most promising candidate
genes and to elucidate functions of rice genes. This study presents
the newly improved RiceGeneThresher. It has been designed and
developed to integrate catalogs from the public domain databases
on rice that involve genetic information, genome annotation,
expressed sequence tags (ESTs), protein information such as
protein domains, gene ontology (GO), metabolic pathway information,
prediction of protein–protein interaction and stress-responsive
genes. RiceGeneThresher system provides a generic data warehousing
solution for fast and flexible querying of rice biological data
sets and integration system. It is able to generate evidences
from each type of omics information that are essential for analyzing
and targeting groups or networks of loci involved in the controlling
of gene expression for the specific traits underlying the QTL
regions. Users, ranging from breeders, laboratory researchers
to the experienced molecular biologists, can use it in a wide
variety of applications and scenarios to find the most promising
candidate gene to improve rice cultivars. To compare RiceGeneThresher
with the relevant existing database like GRAMENE (
4), although
GRAMENE is a large resource for major crop genomes, it does
not provide an easy way for mining biological data underlying
QTL. Unlike RiceGeneThresher, it was specially designed to use
for doing QTL mining intuitively and easily. GRAMENE contains
various biological data, which are displayed across diverse
software such as Ensembl Genome Browser, CMap, BioMart and BioCyc.
Differ from RiceGeneThresher, it consolidates both diverse biological
data and third-party software into one system that is convenient
for users to discover and display candidate genes.
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DATA SOURCES AND ANALYSIS
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Rice genome and gene annotations were collected from Michigan
State University (
http://rice.plantbiology.msu.edu/data_download.shtml).
ESTs of rice were retrieved from National Center for Biological
Information (NCBI) EST database (
http://www.ncbi.nlm.nih.gov/projects/dbEST/).
ESTs were trimmed of contaminated DNA sequences by SeqClean
(
http://compbio.dfci.harvard.edu/tgi/software/) and were aligned
on the TIGR pseudomolecules by using BLAT (
5) with cut-off percent
identity
95%,
E-value
1e-10 and similarity scores
200. Rice
DNA markers were collected from the GRAMENE database. They were
aligned to the TIGR pseudomolecules by using DNA sequences and
DNA primer sequences by BLAT and electronic polymerase chain
reaction (e-PCR) (
6), respectively. The entire protein domains
of rice were analyzed by InterProScan (
7). GO terms were downloaded
from
http://www.geneontology.org/GO.downloads.shtml and each
rice protein was assigned to GO terms by InterPro2GO. KEGG metabolic
pathways information for rice genes were downloaded from ftp://ftp.genome.jp/pub/kegg/genes/organisms/osa/.
Rice proteins from KEGG were mapped to assign metabolic pathways
to rice proteins from TIGR by using BLASTp (
8) with the best
BLAST hit cut-off score. To predict the entire protein–protein
interactions of rice, evidences of protein–protein interactions
of
Arabidopsis thaliana were obtained from The Arabidopsis Information
Resource (TAIR) database (
9) (
http://www.arabidopsis.org). The
Interolog Mapping (
10) method and the phylogenomic approach
(
11) were combined to apply for inferring protein–protein
interaction evidences from Arabidopsis to rice. In addition,
by connecting with the Generation Challenge Programme (GCP),
The Generation Challenge Programme Comparative Plant Stress-responsive
Gene Catalogue (
12) has been incorporated into RiceGeneThresher
database for identifying candidate stress-responsive genes underlying
QTL. The GCP is an online resource documenting stress-responsive
genes comparatively across plant species.
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RICEGENETHRESHER DATABASE AND IMPLEMENTATION
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RiceGeneThresher is a MySQL database based mainly on the Chado
schemata of the Generic Model Organism Database project (
http://www.gmod.org)
(
13), with local enhancements where necessary. This has been
developed and designed to store biological data such as genome
sequences, genome annotation, protein sequences, protein domains,
metabolic pathways and so on. RiceGeneThresher requires access
to many sources of biological data, many of which are distributed
across the internet. To make data sources for feeding RiceGeneThresher
system, RiceGeneThresherDataSource API was developed as the
middleware for querying and transferring data to the front-end.
RiceGeneThresherDataSource incorporates not only the static
data from RiceGeneThresher database (MySQL) but it also incorporates
the dynamic data across the internet by using GCP-compliant
BioMOBY (
14). The front-end is a web-based interface that uses
Java-based software technology running on the top of Apache
Tomcat Web Server. RiceGeneThresher web interface was implemented
by Asynchronous JavaScript and XML (AJAX) technology, which
consists of DWR (
http://getahead.org/dwr/), Velocity (
http://velocity.apache.org/)
and EXT2.0 (
http://extjs.com/). Third-party bioinformatics software
such as Cytoscape (
15), ATV (
16), Jalview (
17) and BLAST, an
NCBI search tool, were combined with RiceGeneThresher for analyzing
and displaying the queried results. In addition, the European
Bioinformatics Institute (EBI) web services (
18), for instance
WUBLAST, NCBIBLAST and InterProScan are also incorporated into
RiceGeneThresher web-based software for doing the similarity
searching of DNA or protein sequences on the fly.
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USER INTERFACE
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Presently, there are two main options for using RiceGeneThresher:
(i) mining genome region of interest by submitting DNA marker
names or DNA sequences, (ii) querying genes by gene locus names
or gene annotation keywords.
Mining a genome region
Users can select the genome region of interest by submitting DNA marker names or DNA sequences (Figure 1A and B). After the submission, RiceGeneThresher displays the position of those DNA markers or DNA sequences on the rice physical map and it automatically selects the widest flanking positions on rice physical map (Figure 1C).
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Figure 1. The figure shows RiceGeneThresher main menu (A), the query by using flanking DNA marker names (B), the genome region of interest for mining rice biological data (C), the example of genome feature information (D) and the particular rice gene information (E).
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For changing the auto selection, users can specify the genome
region of interest by selecting on the flanking DNA marker positions
or DNA sequence positions. RiceGeneThresher explores various
kinds of rice biological information found on the particular
genome region of interest. Main features consist of genome,
transcriptome, proteome, metabolome, phylogenomic and interactome.
Genome feature
The genome feature (Figure 1D) describes genome structure information. It displays data of genome structure; for example, a total number of contigs tilling path, the total size of DNA sequences, a total number of both transposable element (TE)-related and non-TE-related genes, gene density, average gene length and total number of DNA markers found in that particular genome region. It also categorizes genes into a group of annotation types such as those consisting of hypothetical genes, known function genes, putative function genes, similar to function genes, expressed genes and transposable related genes.
Transcriptome feature
The transcriptome feature shows a collection of EST libraries that have high similarity searching scores, E-values and percentage of identities against genes found in the genome region of interest. EST libraries were divided into a group of EST experiment names and a group of EST tissue types. Users can select candidate stress-responsive genes, for example, drought stress-responsive genes, by selecting on genes that are involved in EST library names such as ‘IRRI Drought Stress’.
Proteome feature
The proteome feature displays a list of GO terms of each gene product divided into a group of molecular function, biological process and cellular component, and it also displays a group of protein domains analyzed by the InterProScan.
Metabolome feature
The metabolome feature displays the mapping of rice gene found in the selected genome region into KEGG metabolic pathways. Its feature explores three kinds of pathway information. The first information shows the number of protein-coding genes that is predicted as the enzyme are located on the genome region of interest. The second shows the number of predicted metabolic pathways that are found on that particular genome region and the last information shows, in each metabolic pathway, the protein-coding genes involved in that particular metabolic pathway.
Phylogenomic feature
The phylogenomic feature shows data curated by GCP Comparative Plant Stress-responsive Gene Catalogue, which located in the genome region of interest. Users can easily select candidate stress-responsive genes by using this feature and it provides more information about protein families, phylogenetic trees, multiple sequence alignments (MSA) and associated experimental evidence.
Protein–protein interaction feature
The protein–protein interaction feature shows results of protein–protein interaction found in the genome region of interest. RiceGeneThresher displays protein–protein interaction prediction in a table format. In addition, the third-party software such as Cytoscape is incorporated to display all protein–protein interactions in the graphic mode by using Java web start technology.
Querying genes
Users can query genes in rice genome by using gene annotation keywords for example ‘protein kinase’ or gene locus id. When users select on a gene of interest, RiceGeneThresher displays individual gene information in both graphical user interface and standard web pages. Gene information (Figure 1E) consist of a picture of gene structure that locates on a rice chromosome location, coding DNA sequences, whole-gene DNA sequences, upstream DNA sequences and translated protein sequences. The result of protein analyses such as protein domains, protein–protein interaction prediction, and similarity searching results of protein by using EBI's web services, and protein phylogenomics by querying against the GreenPhyl database (http://greenphyl.cines.fr/cgi-bin/greenphyl.cgi) are also included in the information.
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FUTURE DIRECTIONS
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Currently, the authors of this study are generating expression
data from many experiments by using rice Affymetrix GeneChip
(
http://www.affymetrix.com) together with microarray data from
public databases and literatures. The authors also plan to expand
RiceGeneThresher system to support those microarray data for
finding candidate genes underlying genome region of interest.
As draft whole-genomic sequences generated from new generation
sequencing technologies become more available, the authors plan
to develop new features into RiceGeneThresher in order to integrate
these short shot-gun sequences into its gene mining platform.
There are also plans to update the rice genome database to use
the TIGR rice genome release 6.0.
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FUNDING
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GCP Fellowship Award (to RiceGeneThresher project); the National
Center for Genetic Engineering and Biotechnology (BIOTEC), Thailand
(to RiceGeneThresher project). Funding for open access charge:
BIOTEC.
Conflict of interest statement. None declared.
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REFERENCES
|
|---|
- Korstanje R, Paigen B. From QTL to gene: the harvest begins. Nat. Genet. (2002) 31:235–236.[CrossRef][Web of Science][Medline]
- Pflieger S, Lefebvre V, Causse M. The candidate gene approach in plant genetics: a review. Mol. Breeding (2001) 7:275–291.[CrossRef]
- Borevitz JO, Chory J. Genomics tools for QTL analysis and gene discovery. Curr. Opin. Plant. Biol. (2004) 7:132–136.[CrossRef][Web of Science][Medline]
- Liang C, Jaiswal P, Hebbard C, Avraham S, Buckler ES, Casstevens T, Hurwitz B, McCouch S, Ni J, Pujar A, et al. Gramene: a growing plant comparative genomics resource. Nucleic Acids Res. (2008) 36:D947–D953.[Abstract/Free Full Text]
- Kent WJ. BLAT–the BLAST-like alignment tool. Genome Res. (2002) 12:656–664.[Abstract/Free Full Text]
- Schuler GD. Sequence mapping by electronic PCR. Genome Res. (1997) 7:541–550.[Abstract/Free Full Text]
- Mulder NJ, Apweiler R, Attwood TK, Bairoch A, Bateman A, Binns D, Bradley P, Bork P, Bucher P, Cerutti L, et al. InterPro, progress and status in 2005. Nucleic Acids Res. (2005) 33:D201–D205.[Abstract/Free Full Text]
- Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J. Mol. Biol. (1990) 215:403–410.[CrossRef][Web of Science][Medline]
- Garcia-Hernandez M, Berardini TZ, Chen G, Crist D, Doyle A, Huala E, Knee E, Lambrecht M, Miller N, Mueller LA, et al. TAIR: a resource for integrated Arabidopsis data. Funct. Integr. Genomics (2002) 2:239–253.[CrossRef][Medline]
- Yu H, Luscombe NM, Lu HX, Zhu X, Xia Y, Han J.-DJ, Bertin N, Chung S, Vidal M, Gerstein M. Annotation transfer between genomes: protein-protein interologs and protein-DNA regulogs. Genome Res. (2004) 14:1107–1118.[Abstract/Free Full Text]
- Zmasek C, Eddy S. RIO: analyzing proteomes by automated phylogenomics using resampled inference of orthologs. BMC Bioinformatics (2002) 3:14.[CrossRef][Medline]
- Wanchana S, Thongjuea S, Ulat VJ, Anacleto M, Mauleon R, Conte M, Rouard M, Ruiz M, Krishnamurthy N, Sjolander K, et al. The generation challenge programme comparative plant stress-responsive gene catalogue. Nucleic Acids Res. (2008) 36:D943–D946.[Abstract/Free Full Text]
- Mungall CJ, Emmert DB. A Chado case study: an ontology-based modular schema for representing genome-associated biological information. Bioinformatics (2007) 23:i337–i346.[Abstract/Free Full Text]
- Wilkinson M, Schoof H, Ernst R, Haase D. BioMOBY successfully integrates distributed heterogeneous bioinformatics web services. The PlaNet exemplar case. Plant Physiol. (2005) 138:5–17.[Abstract/Free Full Text]
- Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. (2003) 13:2498–2504.[Abstract/Free Full Text]
- Zmasek CM, Eddy SR. ATV: display and manipulation of annotated phylogenetic trees. Bioinformatics (2001) 17:383–384.[Abstract/Free Full Text]
- Clamp M, Cuff J, Searle SM, Barton GJ. The Jalview Java alignment editor. Bioinformatics (2004) 20:426–427.[Abstract/Free Full Text]
- Labarga A, Valentin F, Anderson M, Lopez R. Web services at the European Bioinformatics Institute. Nucleic Acids Res. (2007) 35:W6–W11.[Abstract/Free Full Text]
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ABSTRACT
INTRODUCTION