Thursday, November 21, 2024
11/21/2024
PROJECT SUMMARY/ABSTRACT Transcriptional regulation of gene expression is an essential process in determining cell identity in multicellular organisms. Through this process, one single genome can create numerous different cell types. Dysregulation of transcription can result in aberrant gene expression and cause diseases. Transcription factors (TFs) play a critical role in altering and controlling the transcription program in every cell. Identification of functional transcriptional regulators is an important task for transcriptional regulation research, but the performance of current computational tools still has room for improvement. The global distribution of cis-regulatory elements (CREs) where TFs bind to DNA in the genome and its potential association with TF functions, and mechanisms of TF recruitment to the regulatory genome are still not fully understood. With the availability of large amounts of multi-modal genomics data in the public domain, innovative computational methods with rigorous statistical models are needed to leverage such big data for studying these fundamental problems in functional genomics. The research program of my lab focuses on developing statistical models and computational methods for functional data analysis to study transcriptional regulation, genomics and epigenomics. In the next five years, we will focus our research efforts on the following directions: (1) Developing improved statistical models and computational methods for functional TR prediction; (2) Investigating genomic clustering tendencies of CREs and their association with TF functions; and (3) Identifying novel mechanisms of TF recruitment to the genome by integrative analysis of CTCF binding with lncRNAs, R-loops, and DNA secondary structures using computational approaches. We are committed to developing all computational methods and tools as open- source, rigorous, robust, and user-friendly for the biomedical research community.
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