Rice (Oryza sativa L.) is a major staple crop in India as well as it is consumed in other parts of world. It is feeding about 50% of the world’s population as well as has a chief contribution in Indian economy. It is one of the most grown crops in India. Rice production is affected because of various biotic and abiotic factors. These factors lead to decrease in crop yield as well as it affects rice omics and lead to various disease (sheath blight, rice BLAST, etc.). Previous works and papers show that bacterial, fungal and viral pathogens are constantly destroying the rice yields and it is affecting the crop from germinating stage. Omics study is helping in prediction, identification, and modification of disease. Omics techniques such as genomics, transcriptomics, proteomics, epigenomics, etc., are helpful in prediction of disease. Disease can be predicted in the initial stage of crop development. Using, transcriptomics disease prediction in rice is nowadays very easy and through this we can decrease the chance of crop damage and maximise its productivity. Bioinformatics tools and databases are applied for study of disease prediction models. Datasets are generated after conducting various experiments which helps researchers to find out disease affecting crops at genetic level. Nowadays, many crops are modified genetically to improve disease resistance (Genetically Modified Crops). These datasets are stored in different rice databases (gramene, international rice information system, etc). These databases can be utilized in the creation of GM crops using bioinformatic tools like clustal omega, SRS, etc.

REFERENCE

1.   Himani H. Assistant professor in economics. IOSR J Humanit Soc Sci. An analysis of the agriculture sector in Indian economy. 2014;19(1):47-54.

2.       ^zg sf C^. Dynamic of rice economy in India: emerging scenario and policy options [internet]. Nabard.org [cited Nov 27 2020]. Available from: https://www.nabard.org/demo/auth/writereaddata/File/OC%2047.pdf.

3.       Cultivating rice crop in India – A production update [internet]. Geographyandyou.com [cited Nov 27 2020]. Available from: https://www.geographyandyou.com/cultivating-rice-crop.

4.       Aryal S. Rainfall and water requirement of rice during growing period. J Agric & Environ. 2013;13(0):1-4. doi: 10.3126/aej.v13i0.7576.

5.       Dou F, Soriano J, Tabien RE, Chen K. Soil texture and cultivar effects on rice (Oryza sativa, L.) grain yield, yield components and water productivity in three water regimes. PLOS ONE. 2016;11(3):e0150549. doi: 10.1371/journal.pone.0150549, PMID 26978525.

6.       Omics knowledge portal for rice [internet]. Ic4r.org [cited Nov 27 2020]. Available from: http://wiki.ic4r.org/index.php/Omics_Knowledge_Portal_for_Rice.

7.       Song S, Tian D, Zhang Z, Hu S, Yu J. Rice genomics: over the past two decades and into the future. Genomics Proteomics Bioinformatics. 2018;16(6):397-404. doi: 10.1016/j.gpb.2019.01.001, PMID 30771506.

8.       Srivastava GP, Yadav N, Yadav BNS, Yadav RK, Yadav DK. Pan-interactomics and its applications. In: Barh D, Soares S, Tiwari S, Azevedo V, editors. Pan-genomics: applications, challenges, and future prospects. San Diego: Elsevier; 2020. p. 397-435.

9.       Lu Y, Zhou DX, Zhao Y. Understanding epigenomics based on the rice model. Züchter Genet Breed Res. 2020;133(5):1345-63. doi: 10.1007/s00122-019-03518-7, PMID 31897514.

10.     Oikawa A, Matsuda F, Kusano M, Okazaki Y, Saito K. Rice metabolomics. Rice. 2008;1(1):63-71. doi: 10.1007/s12284-008-9009-4.

11.     Chern CG, Fan MJ, Yu SM, Hour AL, Lu PC, Lin YC, Wei FJ, Huang SC, Chen S, Lai MH, Tseng CS, Yen HM, Jwo WS, Wu CC, Yang TL, Li LS, Kuo YC, Li SM, Li CP, Wey CK, Trisiriroj A, Lee HF, Hsing YI. A rice phenomics study--phenotype scoring and seed propagation of a T-DNA insertion-induced rice mutant population. Plant Mol Biol. 2007;65(4):427-38. doi: 10.1007/s11103-007-9218-z, PMID 17701278.

12.     Phenomics [internet]. Sciencedirect.com [cited Nov 27 2020]. Available from: https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/phenomics.

13.     Lu YC, Feng SJ, Zhang JJ, Luo F, Zhang S, Yang H. Genome-wide identification of DNA methylation provides insights into the association of gene expression in rice exposed to pesticide atrazine [Sci Rep:18985]. Sci Rep. 2016;6:18985. doi: 10.1038/srep18985, PMID 26739616.

14.     Awasthi S, Chauhan R, Srivastava S, Tripathi RD. The journey of arsenic from soil to grain in rice. Front Plant Sci. 2017;8:1007. doi: 10.3389/fpls.2017.01007, PMID 28676805.

15.     Mahender A, Ali J, Prahalada GD, Sevilla MAL, Balachiranjeevi CH, Md J, Maqsood U, Li Z. Genetic dissection of developmental responses of agro-morphological traits under different doses of nutrient fertilizers using high-density SNP markers. PLOS ONE. 2019;14(7):e0220066. doi: 10.1371/journal.pone.0220066, PMID 31335882.

16.     Beest T. Rice Bla st. Plant Health Instr. 2007. doi: 10.1094/PHI-I-2007-0313-07.

17.     Blast (leaf and collar) - IRRI Rice Knowledge Bank [Internet]. Knowledgebank; 2020 [cited Dec 9 2020]. Available from: irri.org. Available from: http://www.knowledgebank.irri.org/training/fact-sheets/pest-management/diseases/item/blast-leaf-collar.

18.     Maeda S, Dubouzet JG, Kondou Y, Jikumaru Y, Seo S, Oda K, Matsui M, Hirochika H, Mori M. The rice CYP78A gene BSR2 confers resistance to Rhizoctonia solani and affects seed size and growth in Arabidopsis and rice [Sci Rep:2019:9(1):587]. doi: 10.1038/s41598-018-37365-1, PMID 30679785.

19.     Molla KA, Karmakar S, Molla J, Bajaj P, Varshney RK, Datta SK, Datta K. Understanding sheath blight resistance in rice: the road behind and the road ahead. Plant Biotechnol J. 2020;18(4):895-915. doi: 10.1111/pbi.13312, PMID 31811745.

20.     Zhang F, Zeng D, Zhang CS, Lu JL, Chen TJ, Xie JP, Zhou YL. Genome-wide association analysis of the genetic basis for sheath blight resistance in rice. Rice. 2019;12(1):93. doi: 10.1186/s12284-019-0351-5, PMID 31853678.

21.     Richa K, Tiwari IM, Devanna BN, Botella JR, Sharma V, Sharma TR. Novel chitinase gene LOC_Os11g47510 from Indica rice Tetep provides enhanced resistance against sheath blight pathogen Rhizoctonia solani in rice. Front Plant Sci. 2017;8:596. doi: 10.3389/fpls.2017.00596, PMID 28487708.

22.     Sheath blight [internet]. Irri. Org [cited Nov 27 2020]. Available from: http://www.knowledgebank.irri.org/training/fact-sheets/pest-management/diseases/item/sheath-blight.

23.     Hittalmani S, Mahesh HB, Mahadevaiah C, Prasannakumar MK. De novo genome assembly and annotation of rice sheath rot fungus Sarocladium oryzae reveals genes involved in Helvolic acid and cerulenin biosynthesis pathways. BMC Genomics. 2016;17(1):271. doi: 10.1186/s12864-016-2599-0, PMID 27036298.

24.     Sheath rot [internet]. Irri. Org [cited Nov 27 2020]. Available from: http://www.knowledgebank.irri.org/training/fact-sheets/pest-management/diseases/item/sheath-rot.

25.     Kumar M, Singh RP, Singh ON, Singh P, Arsode P, Namrata, et al. Genetic analysis for bacterial blight resistance in indica rice (Oryza sativa L.) cultivars. Oryza (Cuttack). 2019;56(3):247-55.

26.     Zhang F, Wu ZC, Wang MM, Zhang F, Dingkuhn M, Xu JL, Zhou YL, Li ZK. Genome-wide association analysis identifies resistance loci for bacterial blight in a diverse collection of indica rice germplasm. PLOS ONE. 2017;12(3):e0174598. doi: 10.1371/journal.pone.0174598, PMID 28355306.

27.     Bacterial blight [internet]. Irri. Org [cited Nov 27 2020]. Available from: http://www.knowledgebank.irri.org/decision-tools/rice-doctor/rice-doctor-fact-sheets/item/bacterial-blight.

28.     Kannan M, Saad MM, Talip N, Baharum SN, Bunawan H. Complete genome sequence of rice tungro bacilliform virus infecting Asian rice (Oryza sativa) in Malaysia. Microbiol Resour Announc. 2019;8(20). doi: 10.1128/MRA.00262-19, PMID 31097500.

29.     Sivamani and Hervé Huet2 with: Ping Shen2 E, Ong CA, de Kochko A, Fauquet C, Beachy RN. Sivamani and Hervé Huet2 with: ping Shen2 E. Mol Breed. 1999;5(2):177-85. doi: 10.1023/A:1009633816713.

30.     Tungro [internet]. Irri. Org [cited Nov 27 2020]. Available from: http://www.knowledgebank.irri.org/training/fact-sheets/pest-management/diseases/item/tungro.

31.     van Esse HP, Reuber TL, van der Does D. Genetic modification to improve disease resistance in crops. New Phytol. 2020;225(1):70-86. doi: 10.1111/nph.15967, PMID 31135961.

32.     Gupta P, Naithani S, Tello-Ruiz MK, Chougule K, D’Eustachio P, Fabregat A, Jiao Y, Keays M, Lee YK, Kumari S, Mulvaney J, Olson A, Preece J, Stein J, Wei S, Weiser J, Huerta L, Petryszak R, Kersey P, Stein LD, Ware D, Jaiswal P. Gramene database: navigating plant comparative genomics resources. Curr Plant Biol. 2016;7-8:10-5. doi: 10.1016/j.cpb.2016.12.005, PMID 28713666.

33.     Yamazaki Y, Yoshimura A, Nagato Y, Kurata N. Oryzabase: an integrated rice science database. In: Advances in rice genetics. World Scientific Publishing; 2008. p. 380-3.

34.     Xia L, Zou D, Sang J, Xu X, Yin H, Li M, Wu S, Hu S, Hao L, Zhang Z. Rice Expression Database (RED). Rice Expression Database (RED): an integrated RNA-Seq-derived gene expression database for rice. J Genet Genomics. 2017;44(5):235-41. doi: 10.1016/j.jgg.2017.05.003, PMID 28529082.

35.     Dash S, Van Hemert J, Hong L, Wise RP, Dickerson JA. PLEXdb: gene expression resources for plants and plant pathogens. Nucleic Acids Res. 2012;40((Database issue)):D1194-201. doi: 10.1093/nar/gkr938, PMID 22084198.

36.     Petryszak R, Burdett T, Fiorelli B, Fonseca NA, Gonzalez-Porta M, Hastings E, Huber W, Jupp S, Keays M, Kryvych N, McMurry J, Marioni JC, Malone J, Megy K, Rustici G, Tang AY, Taubert J, Williams E, Mannion O, Parkinson HE, Brazma A. Expression Atlas update--a database of gene and transcript expression from microarray- and sequencing-based functional genomics experiments. Nucl Acids Res. 2014;42((Database issue)):D926-32. doi: 10.1093/nar/gkt1270, PMID 24304889.

37.     Researchgate.net [cited Nov 27 2020]. Available from: https://www.researchgate.net/publication/321168673_Expression_Atlas_Gene_and_protein_expression_across_multiple_studies_and_organisms.

38.     About [internet]. Planteome. Org [cited Nov 27 2020]. Available from: https://planteome.org/about.

39.     Dong Q, Schlueter SD, Brendel V, Plant GDB, plant genome database and analysis tools. Nucleic Acids Res. 2004;32((Database issue)):D354-9.

40.     Researchgate.net [cited Nov 27 2020]. Available from: https://www.researchgate.net/publication/5758414_The_rice_annotation_project_database_RAP-DB_2008_update_Nucleic_Acids_Res_36D1028-D1033.

41.     Researchgate.net [cited Nov 27 2020]. Available from: https://www.researchgate.net/publication/229006264_QTL_Cartographer_version_115.

42.     Heffelfinger C, Fragoso CA, Lorieux M. Constructing high-density linkage maps with MapDisto 2.0 [internet]. doi: 10.1101/089177; 2016. bioRxiv.

43.     Hermjakob H, Montecchi-Palazzi L, Lewington C, Mudali S, Kerrien S, Orchard S, Vingron M, Roechert B, Roepstorff P, Valencia A, Margalit H, Armstrong J, Bairoch A, Cesareni G, Sherman D, Apweiler R. IntAct: an open source molecular interaction database. Nucleic Acids Res. 2004;32((Database issue)):D452-5. doi: 10.1093/nar/gkh052, PMID 14681455.

44.     Bradbury PJ, Zhang Z, Kroon DE, Casstevens TM, Ramdoss Y, Buckler ES. TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics. 2007;23(19):2633-5. doi: 10.1093/bioinformatics/btm308, PMID 17586829.

45.     Chenna R, Sugawara H, Koike T, Lopez R, Gibson TJ, Higgins DG, Thompson JD. Multiple sequence alignment with the Clustal series of programs. Nucleic Acids Res. 2003;31(13):3497-500. doi: 10.1093/nar/gkg500, PMID 12824352.