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Our aim is to provide a quick start guide to the nonexpert researchers for NGS-based transcriptome analysis. Further, we describe a method for using RNA-seq to characterize the transcriptome of a plant species, taking the example of a legume crop plant chickpea. Here, first we outline various important issues from experimental design to data analysis, including various strategies of transcriptome assembly, which need substantial consideration for a successful RNA-seq experiment. Further, the assembly of millions and billions of RNA-seq reads to construct the complete transcriptome poses great informatics challenges. Although becoming cheaper, transcriptome sequencing still remains an expensive endeavor. The transcriptome sequencing of an organism provides quick insights into the gene space, opportunity to isolate genes of interest, development of functional markers, quantitation of gene expression, and comparative genomic studies. Sequencing of mRNA using next-generation sequencing (NGS) technologies (RNA-seq) has the potential to reveal unprecedented complexity of the transcriptomes. The integration of data from genomics and proteomics analysis allows for the composition of interactomes, elucidating systems wide impacts resulting from disruption of the CCM signaling complex (CSC). Here we describe the methods currently being used to evaluate CCM-deficient strains in human brain microvascular endothelial cells (HBMVEC), zebrafish embryos as well as in vivo mouse model to evaluate impacts on various signaling cascades resulting from deficiencies in KRIT1 (CCM1), MGC4607 (CCM2), and PDCD10 (CCM3). Proteomics facilitates an understanding of mechanisms being altered at the translational level allowing for an understanding of multiple layers of regulation occurring, elucidating discrepancies between what is seen at the RNA level compared to what is translated to a functional protein. Genomics, performed through RNA-seq, allows the user to evaluate alterations at the transcription level, oftentimes more sensitive than other types of analysis, especially when attempting to understand lack of observation of an expected phenotype. Omics research has garnered popularity recently to integrate in-depth analysis of alterations at the molecular level to elucidate observable phenotypes resulting from knockdown/knockout models.
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