RiceMetaSys for salt and drought stress responsive genes in rice: a web interface for crop improvement

Abstract

Background: Genome-wide microarray has enabled development of robust databases for functional genomics studies in rice. However, such databases do not directly cater to the needs of breeders. Here, we have attempted to develop a web interface which combines the information from functional genomic studies across different genetic backgrounds with DNA markers so that they can be readily deployed in crop improvement. In the current version of the database, we have included drought and salinity stress studies since these two are the major abiotic stresses in rice.

Results: RiceMetaSys, a user-friendly and freely available web interface provides comprehensive information on salt responsive genes (SRGs) and drought responsive genes (DRGs) across genotypes, crop development stages and tissues, identified from multiple microarray datasets. 'Physical position search' is an attractive tool for those using QTL based approach for dissecting tolerance to salt and drought stress since it can provide the list of SRGs and DRGs in any physical interval. To identify robust candidate genes for use in crop improvement, the 'common genes across varieties' search tool is useful. Graphical visualization of expression profiles across genes and rice genotypes has been enabled to facilitate the user and to make the comparisons more impactful. Simple Sequence Repeat (SSR) search in the SRGs and DRGs is a valuable tool for fine mapping and marker assisted selection since it provides primers for survey of polymorphism. An external link to intron specific markers is also provided for this purpose. Bulk retrieval of data without any limit has been enabled in case of locus and SSR search.

Conclusions: The aim of this database is to facilitate users with a simple and straight-forward search options for identification of robust candidate genes from among thousands of SRGs and DRGs so as to facilitate linking variation in expression profiles to variation in phenotype. Database URL: http://14.139.229.201.

Keywords: DNA markers; Drought; Meta-analysis; Rice; Salinity.

Fig. 1

Distribution and functional annotation of overlapping SRGs and DRGs. (a) Distribution of the 12,501 DEGs present in the RiceMetaSys. 17% of the DEGs are common between DRGs and SRGs (b) Functional annotation of overlapping 2134 DEGs under salt and drought. These genes broadly regulate molecular processes belonging to protein phosphorylation, redox processes, electron carrier activity and DNA and RNA binding activities etc.

Fig. 2

Distribution of DEGs in RiceMetaSys (a) and (c) Distribution of salt stress responsive genes across growth stages and tissues (b) and (d) Distribution of drought stress responsive genes across growth stages and tissues

Fig. 3

Gene Ontology of the identified stress responsive genes (a) Majority of the identified SRGs corresponds to biological process (45.17%) followed by molecular function (31%) (b) The distribution pattern was vice-versa for DRGs with major proportion of the identified genes in the category molecular function (46.2%) followed by biological process (29.4%)

Fig. 4

Distribution of microsatellites in the DRGs and SRGs of rice

Fig. 5

An overview of RiceMetaSys (a) Snapshot of the RiceMetaSys database showing the homepage with links to SRGs, DRGs and common genes between SRGs and DRGs. (b) Search options such as variety, tissue, stage, commonly expressed genes among varieties and SSRs. (c) Physical position search option and its output. Selecting the ‘Physical position” search opens a window in which chromosome number and the genomic interval (start and end point) are to be provided as input by the user. This lists the stress responsive genes in the interval in another window. Selecting individual genes from this list provides detailed information on its stress responsiveness

Fig. 6

Snapshot of Graph tool in RiceMetaSys. User can submit up to 10 locus ID’s and can view expression profile of, (a) candidate genes among different varieties (shown in black bars) or, (b) candidate genes within a variety e.g. Dhaggadeshi (shown in green bars). *for the sake of clarity we have shown data of 3 genes (locus IDs)