Discovery of Functional SNPs via Genome-Wide Exploration of Malaysian Pigmented Rice Varieties

Toward Integrated Multi-Omics Intervention: Rice Trait Improvement and Stress Management

Rice (Oryza sativa) is an imperative staple crop for nearly half of the world's population. Challenging environmental conditions encompassing abiotic and biotic stresses negatively impact the quality and yield of rice. To assure food supply for the unprecedented ever-growing world population, the improvement of rice as a crop is of utmost importance. In this era, "omics" techniques have been comprehensively utilized to decipher the regulatory mechanisms and cellular intricacies in rice. Advancements in omics technologies have provided a strong platform for the reliable exploration of genetic resources involved in rice trait development. Omics disciplines like genomics, transcriptomics, proteomics, and metabolomics have significantly contributed toward the achievement of desired improvements in rice under optimal and stressful environments. The present review recapitulates the basic and applied multi-omics technologies in providing new orchestration toward the improvement of rice desirable traits. The article also provides a catalog of current scenario of omics applications in comprehending this imperative crop in relation to yield enhancement and various environmental stresses. Further, the appropriate databases in the field of data science to analyze big data, and retrieve relevant information vis-à-vis rice trait improvement and stress management are described.

Comprehensive proteomics data on whole rice grain of selected pigmented and non-pigmented rice varieties using SWATH-MS approach

The proteome data of whole rice grain is considerably limited particularly for rice with pigmentations such as black and red rice. Hence, we performed proteome analysis of two black rice varieties (BALI and Pulut Hitam 9), two red rice varieties (MRM16 and MRQ100) and two white rice varieties (MR297 and MRQ76) using label-free liquid chromatography Triple TOF 6600 tandem mass spectrometry (LC-MS/MS). Our aim was to profile and identify proteins related to nutritional (i.e. antioxidant, folate and low glycaemic index) and quality (i.e. aromatic) traits based on peptide-centric scoring from the Sequential Window Acquisition of All Theoretical Mass Spectra (SWATH-MS) approach. Both information dependent acquisition (IDA) and SWATH-MS run were performed in this analysis. Raw data was then processed using ProteinPilot software to identify and compare proteins from the six different varieties. In future, this proteomics data will be integrated with previously obtained genomics [1] and transcriptomics [2] data focusing on the above nutritional and quality traits, with an ultimate aim to develop a panel of functional biomarkers related to those traits for future rice breeding programme. The raw MS data of the pigmented and non-pigmented rice varieties have been deposited to ProteomeXchange database with accession number PXD018338.