CHAPTER 1Essentially, bioinformatics methodologies for omics constitute a crossover between bottom-up hypothesis-driven and top-down data-driven approaches, which is indispensable to integrate the acquisition, analysis and management of multiple omics data with genome-scale statistical and mathematical modelling and simulation (57).During recent decades, bioinformatics developed a score of methods for sequence analysis, in terms of sequence assembly, genome annotation, comparative genomics, genetics and population genomics, computational evolutionary biology; for expression analysis, in terms of gene expression, protein expression, metabolite profiling; for structural bioinformatics, in terms of genome 3D chromatin modelling, RNA secondary structure prediction, protein structure prediction, homology modelling; for network and system biology, in terms of biological network analysis, gene co-expression analysis, molecular interaction networks; and for data management, in terms of database and web service development, data curation, data visualization, workflow management, and so on. Perspective of crop omicsA global food crisis can soon develop, given the rapid growth of the world population and the lagging pace of yield increase for major crops, including rice, maize, wheat, and sorghum.Domestication, design, and development of genetically improved, stressresilient, and environmental adapted crops have become the research priority.The crop omics perspective necessitates the convergence of low-cost genome sequencing with improved computational power and high-throughput molecular phenotyping technologies to accelerate the identification of genes and/or loci underlying important agronomic traits relevant to food production and quality (58).As the basis of crop omics, the first essential step is assembly and decoding of the reference genomes.This was a challenge for NGS based methodologies as the size and dynamic nature of the plant genome are complicated and diversified (59,60).Plants
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