Nucleic Acids Research Advance Access originally published online on October 11, 2007
Nucleic Acids Research 2008 36(Database issue):D943-D946; doi:10.1093/nar/gkm798
Nucleic Acids Research, 2008, Vol. 36, Database issue D943-D946
© 2007 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.
The Generation Challenge Programme comparative plant stress-responsive gene catalogue
Samart Wanchana1,
Supat Thongjuea1,2,
Victor Jun Ulat1,
Mylah Anacleto1,
Ramil Mauleon1,
Matthieu Conte3,
Mathieu Rouard4,
Manuel Ruiz3,
Nandini Krishnamurthy5,
Kimmen Sjolander5,
Theo van Hintum6 and
Richard M. Bruskiewich1,*
1Crop Research Informatics Laboratory – International Rice Research Institute (IRRI), DAPO Box 7777, Metro Manila, Philippines, 2National Center for Genetic Engineering and Biotechnology, 113 Thailand Science Park, Phahonyothin Road, Klong 1, Klong Luang, Pathumthani 12120, Thailand, 3Centre International de Recherche Agronomique pour le Développement (CIRAD), Avenue Agropolis, 34398 Montpellier, Cedex 5, France, 4Bioversity International, Via dei Tre Denari 472/a, 00057 Maccarese, Rome, Italy, 5Department of Bioengineering, University of California, 473 Evans Hall #1762, Berkeley, CA 94720, USA and 6Wageningen Universiteit & Researchcentrum (WUR), 6700 HB Wageningen, Germany
* To whom correspondence should be addressed. Tel: +63 2 580 5600; Fax: +63 2 580 5699; Email: r.bruskiewich{at}cgiar.org
Received August 14, 2007. Revised September 16, 2007. Accepted September 17, 2007.
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ABSTRACT
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The Generation Challenge Programme (GCP;
www.generationcp.org)
has developed an online resource documenting stress-responsive
genes comparatively across plant species. This public resource
is a compendium of protein families, phylogenetic trees, multiple
sequence alignments (MSA) and associated experimental evidence.
The central objective of this resource is to elucidate orthologous
and paralogous relationships between plant genes that may be
involved in response to environmental stress, mainly abiotic
stresses such as water deficit (‘drought’). The
web-based graphical user interface (GUI) of the resource includes
query and visualization tools that allow diverse searches and
browsing of the underlying project database. The web interface
can be accessed at
http://dayhoff.generationcp.org.
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INTRODUCTION
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Comparative biology provides valuable insights into organismal
function and evolution, highlighting the divergence and conservation
of gene families and biological processes. In order to cross-reference
genes from one species to other related species, accurate predictions
of orthologous and paralogous relationships are necessary. Such
cross-referencing potentially permits researchers to infer the
molecular functions of genes lacking such annotation from experiments
in other, better-characterized organisms. Paralogous genes arising
from ancient duplication events are likely to have diverged
in function, whereas orthologous genes with common ancestry
separated only by speciation are more likely to retain identical
or highly similar function over evolutionary time (
1,
2). Such
orthologous and paralogous gene loci almost invariably share
some common molecular characteristics; thus, important inferences
of function may be possible once these relationships are clearly
defined.
The Generation Challenge Programme (GCP; www.generationcp.org) is a global crop research consortium striving to apply comparative genomics and molecular analysis to plant genetic resources to enhance efforts in plant breeding for plant stress tolerance. Clustering of orthologous genes across multiple crop species is a powerful strategy for the identification of stress-responsive gene loci and their corresponding alleles of high agronomic value, for application in breeding for stress tolerance.
To facilitate cross-species gene functional analysis, the GCP commissioned a project to assemble tools for the compilation and visualization of comparative information about stress-responsive genes. The result is an online resource, code-named Dayhoff, after Margaret Dayhoff, the famous early pioneer in comparative analysis of sequences.
Orthologues and paralogues of stress-responsive genes are presented by means of phylogenetic trees constructed using a phylogenomic inference method (3,4). The Dayhoff catalogue is expected to guide the bioinformatics analysis and interpretation of research results generated by comparative genomics experiments. For example, microarray data about drought stress obtained across diverse crop species will be analysed in a comparative manner to identify conserved gene expression profiles exhibited under similar stresses, in a similar fashion to experiments in other model species (5–7).
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DATABASE CONSTRUCTION AND IMPLEMENTATION
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Dayhoff is a MySQL database based mainly on the Chado schemata
of the Generic Model Organism Database project (
8) (
www.gmod.org),
with local enhancements where necessary, to store protein family
information such as protein multiple sequence alignments (MSA),
phylogenetic trees and supported stress evidence from experiments
and the literature. The web interface uses GCP Java-based software
technology (
http://pantheon.generationcp.org) connected to third-party
software such as ATV (
9), Jalview (
10) and BLAST (
11) for analysing
and viewing the query's results. The Dayhoff site is also cross-linked
to a complementary GCP-funded comparative gene analysis resource
called GreenPhyl. GreenPhyl provides comparative genomic analyses
of
Arabidopsis thaliana and
Oryza sativa whole-genome assemblies
and can be accessed directly at
http://greenphyl.cirad.fr/cgi-bin/greenphyl.cgi.
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DATA ANALYSIS AND CURATION
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The core data set in Dayhoff consists of stress-related protein
families characterized by a phylogenomic inference approach
(
4,
12). The method has been shown to enable the highest accuracy
in predicting protein molecular function (
12), to avoid most
false homology inference problems, and to distinguish between
orthologous and paralogous genes (
4). Phylogenetic trees representing
protein families were constructed by the following steps. First,
homologous sequences for each stress protein compiled from the
literature were gathered by using the FlowerPower tool on the
Berkeley Phylogenomics Group (BPG) web server (
13), with Uniprot
proteins (
14) used as a database. FlowerPower uses iterative
subfamily hidden Markov model (HMM) searches against PSI-BLAST-identified
homologues and alignment analysis to discriminate between partial
and global homologies (
12). Then, MSAs of homologous proteins
were constructed with the high-accuracy MSA program, MUSCLE
v. 3.52 (
15). After masking the alignments to remove columns
with many gap characters, functional subfamilies were identified
for each group using the SCI-PHY web server (
12). SCI-PHY uses
Bayesian and information-theoretic approaches to construct a
hierarchical tree and cut tree into subtrees to identify functional
subfamilies (
12). The analysed trees were saved in the extended
New Hampshire format (NHX) for display by the ATV program (
9).
Stress-responsive genes to be analysed were compiled from available literature documenting genes analysed from diverse experimental sources (Supplementary Table 1). In the current version of Dayhoff, stress genes include those analysed from drought, salt, cold, ABA and GA stress experiments. Both up- and down-regulated genes under those stress types are available for O. sativa and A. thaliana. To overlay this experimental evidence on the gene family trees, BLASTP searches of candidate stress genes were performed against the database of Uniprot proteins used in phylogenetic tree construction. The BLAST results were limited into the ranks of parameter cutoff values as following: 
80% to >95% similarity, E-value <1e–20 to <1e–50 and bit scores >50 to >1000.
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USER INTERFACE
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There are three main options for using the database: browsing
protein families, query database by gene names or protein names
and BLAST search against protein families (
Figure 1).
View larger version (63K):
[in this window]
[in a new window]
[Download PowerPoint slide]
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Figure 1. An example of browsing (A), querying (G) and BLAST searching the Dayhoff database (H). Phylogenetic tree and multiple sequence alignment of each protein family are displayed by ATV (C) and Jalview (D), respectively. Information on each protein family is shown in a new window (B) from the protein family ID links. Candidate stress proteins can be viewed in a new window when you toggle the Get Candidate Stress Protein button (B and E). A location of stress genes in the rice genome is drawn in the chromosome graphic (F). Dayhoff can be queried by protein names or family names (G). A protein sequence or nucleotide sequence can be submitted to BLAST the Dayhoff and GreenPhyl databases. The results of BLAST search are provided in both Dayhoff protein families and GreenPhyl classified families (H).
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Browsing protein families
The database can be used by browsing the entire set of stress
protein families that have been constructed (
Figure 1A). Users
can select for browsing the database from the main drop-down
menu. A list of protein families as well as links for phylogenetic
trees and MSA are shown on the front page. Details about each
protein family, for example, the list of Uniprot IDs, protein
names, Gene Ontology (GO) terms and key publications for each
protein obtained from Uniprot database (
14), can be accessed
through the family ID links (
Figure 1B). Additional information
can be displayed by selecting from the drop-down list. MSAs
and the phylogenetic trees can be viewed by Jalview and ATV,
respectively (
Figure 1C and D). There are two choices for presenting
the MSAs, by a whole family or users can select some proteins
of interest to be aligned by checking the check boxes (
Figure 1B).
Hyperlinks to the Uniprot database and other online resources
are also provided. Users can find stress evidence mapped to
the matched protein(s) in the family owing to the BLASTP search
results (
Figure 1E). BlastP cutoff values for% identity, E-value
and score are provided for filtering the BLASTP results. Users
may need to change the default parameters in order to receive
optimum results.
Query database
In the current version of Dayhoff, users can search the database by keywords within two fields of data type: Family name and Protein name (Figure 1G). By searching Family name, the matched family will be retrieved. Users can view more information through the family ID link as well as MSA and tree links. By searching Protein name, matched protein(s) will be listed together with Family ID link and some other information.
BLAST protein families
Users can submit a protein or DNA sequence in Fasta or raw format in order to BLAST the Dayhoff database as well as the GreenPhyl database (Figure 1H). Dayhoff is interconnected to the GreenPhyl database via a GCP-compliant BioMOBY (16) client web service. Users will receive the results of best hits of protein family from both Dayhoff and GreenPhyl. The results will be provided with links to Dayhoff protein families and hyperlinks to classified families at the GreenPhyl web site.
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FUTURE DIRECTIONS
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Further integration of the comparative stress-responsive gene
catalogue with the GCP platform software will enhance access
to comparative gene data in a variety of bioinformatics analysis
contexts. In particular, Dayhoff will be connected using GCP
technology to a MAXD gene expression database, for direct integration
into comparative microarray data analyses.
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SUPPLEMENTARY DATA
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Supplementary Data are available at NAR Online.
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ACKNOWLEDGEMENTS
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Funding to pay the Open Access publication charges for this
article was provided by Generation Challenge Programme.
Conflict of interest statement. None declared.
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ABSTRACT
INTRODUCTION