Plant Transcriptomics: Data-driven Global Approach to Understand Cellular Processes and Their Regulation in Model and Non-Model Plants

Under the new paradigms of integrative and network biology, comparison of expression changes among a small subset of candidate genes across phenotypic variants is hardly informative or conclusive in the context of cellular response regulation and the underlying genetic mechanisms. Integration of global changes in gene expression under multiple conditions with existing genomic databases that have been curated systematically are key for the efficient extraction of robust and biologically meaningful patterns and signatures that are reflective of cellular states. Despite the increasing availability of a wide array of computational tools, the resolution of RNA-seq-based transcriptome profiling is as good as the experimental design that determines the window of information revealed relative to the hypothesis being tested, and this intricacy is often underestimated. In this chapter, we discuss the important aspects of data analytics and the basic principles that must be taken into consideration to better bridge the design of the wet-lab experiments with the requirements of a robust dry-lab knowledge dissection and integration. We also highlight the unique assumptions and requirements between transcriptome experiments conducted using plant genetic models with comprehensive and annotated genomes for reference-guided assembly and extraction of biological knowledge, in comparison with the non-model plant species, which rely on a de novo assembly of transcriptome datasets followed by homology-based comparison with closely related species with reference genome.