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

BackgroundGenome-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.ResultsRiceMetaSys, 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.ConclusionsThe 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

[1]  Yoshiaki Nagamura,et al.  RiceXPro: a platform for monitoring gene expression in japonica rice grown under natural field conditions , 2010, Nucleic Acids Res..

[2]  Archana Singh,et al.  Identification of a diverse mini‐core panel of Indian rice germplasm based on genotyping using microsatellite markers , 2015 .

[3]  Roberto Tuberosa,et al.  Genomics-based approaches to improve drought tolerance of crops. , 2006, Trends in plant science.

[4]  Viswanathan Chinnusamy,et al.  Molecular genetic perspectives on cross-talk and specificity in abiotic stress signalling in plants. , 2003, Journal of experimental botany.

[5]  Daeseok Choi,et al.  The Rice Oligonucleotide Array Database: an atlas of rice gene expression , 2012, Rice.

[6]  F. Rijsberman,et al.  More Crop Per Drop , 2007 .

[7]  D. Golldack,et al.  Tolerance to drought and salt stress in plants: Unraveling the signaling networks , 2014, Front. Plant Sci..

[8]  P. Ronald,et al.  Recent advances in dissecting stress-regulatory crosstalk in rice. , 2013, Molecular plant.

[9]  L. Lipovich,et al.  Computational and experimental analysis of microsatellites in rice (Oryza sativa L.): frequency, length variation, transposon associations, and genetic marker potential. , 2001, Genome research.

[10]  Jian-Kang Zhu,et al.  Salt and drought stress signal transduction in plants. , 2002, Annual review of plant biology.

[11]  A. Rafalski Applications of single nucleotide polymorphisms in crop genetics. , 2002, Current opinion in plant biology.

[12]  Ashutosh Kumar Singh,et al.  Combining QTL mapping and transcriptome profiling of bulked RILs for identification of functional polymorphism for salt tolerance genes in rice (Oryzasativa L.) , 2010, Molecular Genetics and Genomics.

[13]  Ashutosh Kumar Singh,et al.  Genic non-coding microsatellites in the rice genome: characterization, marker design and use in assessing genetic and evolutionary relationships among domesticated groups , 2009, BMC Genomics.

[14]  Daniel N. Frank,et al.  XplorSeq: A software environment for integrated management and phylogenetic analysis of metagenomic sequence data , 2008, BMC Bioinformatics.

[15]  Julie A. Dickerson,et al.  PLEXdb: gene expression resources for plants and plant pathogens , 2011, Nucleic Acids Res..

[16]  Viswanathan Chinnusamy,et al.  Physiological, anatomical and transcriptional alterations in a rice mutant leading to enhanced water stress tolerance , 2015, AoB PLANTS.

[17]  Nandula Raghuram,et al.  Microarray Analysis of Rice d1 (RGA1) Mutant Reveals the Potential Role of G-Protein Alpha Subunit in Regulating Multiple Abiotic Stresses Such as Drought, Salinity, Heat, and Cold , 2016, Front. Plant Sci..

[18]  Harkamal Walia,et al.  Comparing genomic expression patterns across plant species reveals highly diverged transcriptional dynamics in response to salt stress , 2009, BMC Genomics.

[19]  Yaqin Ma,et al.  BatchPrimer3: A high throughput web application for PCR and sequencing primer design , 2008, BMC Bioinformatics.

[20]  Matthew E. Ritchie,et al.  limma powers differential expression analyses for RNA-sequencing and microarray studies , 2015, Nucleic acids research.

[21]  Emma Marris,et al.  Water: More crop per drop , 2008, Nature.

[22]  Anshuman Singh,et al.  Multiple major QTL lead to stable yield performance of rice cultivars across varying drought intensities , 2014, BMC Genetics.

[23]  Nori Kurata,et al.  OryzaExpress: An Integrated Database of Gene Expression Networks and Omics Annotations in Rice , 2010, Plant & cell physiology.

[24]  Ashutosh Kumar Singh,et al.  Haplotype structure in grain weight gene GW2 and its association with grain characteristics in rice , 2012, Euphytica.

[25]  Atmakuri R. Rao,et al.  Genome-wide association mapping of salinity tolerance in rice (Oryza sativa) , 2015, DNA research : an international journal for rapid publication of reports on genes and genomes.

[26]  D. Schwartz,et al.  Improvement of the Oryza sativa Nipponbare reference genome using next generation sequence and optical map data , 2013, Rice.

[27]  Jian Wang,et al.  A microarray analysis of the rice transcriptome and its comparison to Arabidopsis. , 2005, Genome research.

[28]  Cheng Li,et al.  Adjusting batch effects in microarray expression data using empirical Bayes methods. , 2007, Biostatistics.

[29]  Manuel Ruiz,et al.  OryGenesDB: a database for rice reverse genetics , 2005, Nucleic Acids Res..

[30]  Y. Yamazaki,et al.  Oryzabase. An Integrated Biological and Genome Information Database for Rice1[OA] , 2005, Plant Physiology.

[31]  Akhilesh K Tyagi,et al.  Genome-wide generation and use of informative intron-spanning and intron-length polymorphism markers for high-throughput genetic analysis in rice , 2016, Scientific Reports.

[32]  Mukesh Jain,et al.  RiceSRTFDB: A database of rice transcription factors containing comprehensive expression, cis-regulatory element and mutant information to facilitate gene function analysis , 2013, Database J. Biol. Databases Curation.

[33]  Rob DeSalle,et al.  Oil palm genome sequence reveals divergence of interfertile species in old and new worlds , 2013, Nature.

[34]  Chandra Prakash,et al.  Unraveling the molecular basis of oxidative stress management in a drought tolerant rice genotype Nagina 22 , 2016, BMC Genomics.

[35]  Dennis B. Troup,et al.  NCBI GEO: archive for high-throughput functional genomic data , 2008, Nucleic Acids Res..

[36]  N. Grassly,et al.  United Nations Department of Economic and Social Affairs/population Division , 2022 .

[37]  Sangkyu Park,et al.  Microarray analysis of genes differentially expressed in melatonin‐rich transgenic rice expressing a sheep serotonin N‐acetyltransferase , 2013, Journal of pineal research.

[38]  Ashutosh Kumar Singh,et al.  Mapping QTLs for Salt Tolerance in Rice (Oryza sativa L.) by Bulked Segregant Analysis of Recombinant Inbred Lines Using 50K SNP Chip , 2016, PloS one.

[39]  T. Zhu,et al.  Microarray analysis of the transcriptome as a stepping stone towards understanding biological systems: practical considerations and perspectives. , 2006, The Plant journal : for cell and molecular biology.

[40]  Justin Preece,et al.  QlicRice: a web interface for abiotic stress responsive QTL and loci interaction channels in rice , 2011, Database J. Biol. Databases Curation.

[41]  Mark P. Robertson,et al.  Introduced and invasive cactus species: a global review , 2014, AoB PLANTS.

[42]  Nagendra Kumar Singh,et al.  High Resolution Mapping of QTLs for Heat Tolerance in Rice Using a 5K SNP Array , 2017, Rice.