Over the past couple of decades, a surge of discoveries have revealed RNA regulation as a central player in cellular processes. Circular RNAs (circRNAs), formed when the two ends of linear transcripts are joined together, were recently identified as a large class of post-transcriptional regulators that perform a range of functions in biological systems. RNAs are regulated by RNA-binding proteins (RBPs) at all post- transcriptional stages, including splicing, transportation, stabilization and translation. Identifyng the functional targets (including both linear and circular RNAs) of these RBPs ranks among the key biomedical research questions and opens a new direction for drug discoveries. Moreover, investigating RBP-RNA binding is now possible on a genome-wide scale, due to the advent of a technique that couples cross-linking immunoprecipitation with high-throughput sequencing (CLIP-seq). The overall goal of this study is to develop novel analytical models and a comprehensive research platform to study RBPs and, more broadly, RNA regulation. A rapidly-expanding amount of CLIP-seq data together with function data, which measure the genome-wide functional changes caused by the binding of a specific RBP, has triggered a critical need for computational methods to systematically analyze the functional targets of RBPs. For this purpose we have already collected extensive CLIP-seq data as well as RNA-seq data, which represent the functional changes caused by RBP-binding. Integrating these large-scale and complementary data sets from different sources will unlock a great opportunity to identify the functional targets of RBPs and to examine the direct interactions between RBPs and circRNAs. More importantly, the findings from our computational analysis will be experimentally validated by our collaborators.
In Aim 1. 1 of this study, we will propose a novel statistical approach to improve the analysis of CLIP- seq data and the identification of RBP-binding sites. We will compare and evaluate these new computational approaches by using benchmark datasets from the public domain, as well as by generating our own experimental data with experimental validations.
In Aim 1. 2 of this study, we will develop a powerful computational model to identify the functional targets of RBPs by integrating RNA sequence, secondary structure, RBP-binding and functional data sets. Promising functional targets will be experimentally validated by our collaborators. Circular RNA (circRNA) may bind and sequester RBPs into regulatory roles, and studying RBP- circRNA interactions may provide insights into the role of circRNAs in disease pathogenesis and their potential as therapeutic targets. However, as of yet no computational method has been developed to study RBP- circRNA interactions.
In Aim 2, we will propose a novel computational method to systematically study RBP- circRNA interactions and their functions using CLIP-Seq data and RNA-seq data.
In Aim 3 of this study, we will develop a publicly-available, comprehensive RBP-RNA interaction web portal with a user-friendly interface and a powerful analysis engine. This web-portal will include all the results, computational algorithms and datasets used in this study. We will integrate these datasets in the web portal together with analytic algorithms developed from this study, so that researchers worldwide can utilize the data and computational tools we have generated. In partnership with the Galaxy team, we plan to develop a user- friendly and reproducible research environment for RNA regulation.

Public Health Relevance

RNA's diversity in sequence and structure endow it with crucial roles in cell biology. RNA-binding proteins (RBPs) bind to RNAs and modulate their processing, translation and functions, including splicing, export, localization and stability. Currently, genome-wide identification of the functional targets of RBPs and their role in biology and disease etiology is one of the key questions in biomedical research. The overall goal of this study is to develop novel statistical models and integrative analysis approaches to identify functional targets of RBPs.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
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Biodata Management and Analysis Study Section (BDMA)
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Ravichandran, Veerasamy
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University of Texas Sw Medical Center Dallas
Other Clinical Sciences
Schools of Medicine
United States
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