Transcriptome-Wide Methods for functional and Structural Annotation of Long Non-Coding RNAs

Abstract

Non-coding RNAs across the genome have been associated with various biological processes, ranging from regulation of splicing to remodeling of chromatin. Amongst the repertoire of non-coding sequences lies a critical species of RNAs called long non-coding RNAs (lncRNAs). LncRNAs significantly contribute to a large spectrum of human phenotypes, including cancers, Heart failure, Diabetes, and Alzheimer’s disease. This dissertation emphasizes the need to characterize the functional role of lncRNAs to improve our understanding of human diseases. This work consolidates a resource from multiple computational genomics and natural language processing-based approaches to advance our ability to functionally annotate hundreds of lncRNAs and their interactions, providing a one-stop lncRNA functional annotation and dynamic interaction network and multi-facet omics data visualization platform. RNA interactions are vital in various cellular processes, from transcription to RNA processing. These interactions dictate the functional scope of the RNA. However, the multifaceted functional nature of RNA stems from its ability to form secondary structures. Therefore, this work establishes a computational method to characterize RNA secondary structure by integrating SHAPE-seq and long-read sequencing to enhance further our understanding of RNA structure in modulating the post-transcriptional regulatory processes and deciphering the influence at several layers of biological features, ranging from structure composition to consequent protein occupancy. This study will potentially impact the research community by providing methods, web interfaces, and computational pipelines, improving our functional understanding of long non-coding RNAs. This work also provides novel integration methods of technologies like Oxford Nanopore-based long-read sequencing, RNA structure-probing methods, and machine learning. The approaches developed in this dissertation are scalable and adaptable to investigate further the functional and regulatory role of RNA and its structure. Overall, this study accelerates the development of RNA-based diagnostics and the identification of therapeutic targets in human disease.