A Comprehensive Overview on Application of Bioinformatics and Computational Statistics in Rice Genomics Toward an Amalgamated Approach for Improving Acquaintance Base

Rice (Oryza sativa L.) is a major crop in the world and provides the staple food for over half of the world population. From thousands of years of cultivation and breeding to recent genomics and systems biology approach, rice has been the focus of agriculture and plant research. Modern scientific research depends on computer technology to organize and analyze large datasets. Rice informatics – a relatively new discipline – has been developing rapidly as a subdiscipline of bioinformatics. Rice informatics devotes to leveraging the power of nature’s experiment of breeding and evolution to extract key findings from sequence and experimental data. Recent advances in high-throughput genotyping and sequencing technologies have changed the landscape of data collection and its analysis by using friendly database access and information retrieval. It focuses on developing and applying database tools and computationally intensive techniques and statistical software (e.g., pattern recognition, data mining, machine learning algorithms, R-statistical, MATLAB, and visualization) which give the opportunity to quickly and efficiently study heap of genomics information, chemical structure, and model generation study. Over recent years, various newly emerged diseases to rice varieties have an increasing concern to agriculturists and pathologists. The establishments of International Rice Information System, Rice Genome Research Project, Integrated Rice Genome Explorer, and Rice Proteome Databases are important initiatives for rice improvement using in silico software (e.g., homology modeling using SWISS Model, Modeler, and Autodock); the recent ongoing research on rice protein and its role in metabolic pathways works is being done around the world. Rice informatics has already started showing its profound impact on agricultural research and developments.