Integrative Computational Genomics Based Approaches to Uncover the Tissue-Specific Regulatory Networks in Development and Disease
Regulatory protein families such as transcription factors (TFs) and RNA Binding Proteins (RBPs) are increasingly being appreciated for their role in regulating the respective targeted genomic/transcriptomic elements resulting in dynamic transcriptional (TRNs) and post-transcriptional regulatory networks (PTRNs) in higher eukaryotes. The mechanistic understanding of these two regulatory network types require a high resolution tissue-specific functional annotation of both the proteins as well as
... heir target sites. This dissertation addresses the need to uncover the tissue-specific regulatory networks in development and disease. This work establishes multiple computational genomics based approaches to further enhance our understanding of regulatory circuits and decipher the associated mechanisms at several layers of biological processes. This study potentially contributes to the research community by providing valuable resources including novel methods, web interfaces and software which transforms our ability to build high-quality regulatory binding maps of RBPs and TFs in a tissue specific manner using multi-omics datasets. The study deciphered the broad spectrum of temporal and evolutionary dynamics of the transcriptome and their regulation at transcriptional and post transcriptional levels. It also advances our ability to functionally annotate hundreds of RBPs and their RNA binding sites across tissues in the human genome which help in decoding the role of RBPs in the context of disease phenotype, networks, and pathways. The approaches developed in this dissertation is scalable and adaptable to further investigate the tissue specific regulators in any biological systems. Overall, this study contributes towards accelerating the progress in molecular diagnostics and drug target identification using regulatory network analysis method in disease and pathophysiology.