Computational tools to study relationships between sequence, structure, and function in lncRNAs - Abstract: Long non-coding RNAs (lncRNAs) are transcripts longer than 500 nucleotides that do not encode proteins and have been shown to have important roles in the cell, including gene regulation. Nevertheless, most lncRNAs are functionally uncharacterized, and identifying their functions remains a priority in human genomics. Two major impediments slow the discovery of regulatory functions in lncRNAs: (1) sequence ambiguities for expressed lncRNAs determined from short-read RNA-seq data and (2) knowledge gaps regarding which physical properties, including RNA structure, bestow regulatory function. LncRNAs are known to be spliced inefficiently, yet transcript annotation databases and RNA-seq abundance estimators typically do not consider intron- containing transcripts in their calculations, causing researchers to inadvertently ignore potentially functional lncRNA isoforms. Additionally, although structure is universally important for RNA function, there is a lack of means to identify and compare structural patterns across the transcriptome, hindering the study of the role of structure in lncRNAs. In this proposal, I will develop computational approaches that will enable researchers to (1) evaluate the empirical support for transcripts across annotated lncRNA loci to identify the most relevant lncRNA sequences for further study and (2) identify recurring patterns in the secondary structures of transcripts. I will apply these approaches to identify lncRNA transcripts associated with disease in GTEx and identify structural properties associated with specific protein-binding events in lncRNAs. My research will improve lncRNA research by ascertaining lncRNA expression, rigorously evaluating support for all transcripts, including intron-containing transcripts, and bettering our understanding of the role of structure in lncRNA functions by enabling analysis of structural patterns across the transcriptome. My long-term goal is to become a bioinformatician for an RNA drug discovery company where I can computationally identify drug targets and transcripts that can modulate critical cellular processes in humans. At the University of North Carolina at Chapel Hill (UNC-Chapel Hill), I have been conducting research in the Calabrese lab under the mentorship of Dr. Mauro Calabrese and Dr. Alain Laederach to become an independent researcher with strong skills relevant to RNA bioinformatics. The labs of my co-mentors are extremely collaborative and have expertise in diverse and complementary areas that are well-suited to my career goals. Under this fellowship, I will undergo extensive training in RNA biology, genomics, and bioinformatics through mentored research, as well as by attending conferences, symposiums, seminars, and classes. I will also gain experience in mentorship, leadership, and communication through resources provided at UNC-Chapel Hill and other local institutions. I will apply these skills in my research as well as in an internship with a local RNA drug discovery company. The experiences under this fellowship will enhance my ability to successfully transition and contribute to RNA drug discovery through computational bioinformatics.
