Enabling comprehensive and user-friendly circular RNA analyses with circtools.cloud

Project Summary/Abstract Circular RNAs (circRNAs) have been studied for only one decade but have been implicated in many different disease settings such as cancer, neurological, and cardiovascular disease. CircRNAs are endogenous RNAs with a covalently closed loop originating from back-splicing events of pre-mRNA, which can be detected from raw sequencing data. Moreover, they are seen as powerful biomarkers for major human diseases due to their stability. However, while there have been advances in computational circRNA analysis, in general, circRNA research faces computational challenges, specifically the requirement for bioinformatics expertise, the need for suitable infrastructure, and the location of auspicious datasets. This accessibility gap impedes researchers to perform critical analyses and contribute to the emerging and exciting field of circRNA research. Existing methods for circRNA detection from RNA-seq data have been in development for several years, but common to all of them is the requirement for advanced bioinformatics expertise. Moreover, preprocessing as well as the actual circRNA detection require considerable bioinformatics infrastructure. Each of these barriers are significant hurdles for researchers. This proposal is the result of 8 years of circRNA research and algorithm development. The laboratory of the PI has developed and maintains circRNA software tools that are actively used by many researchers around the world and has analyzed hundreds of circRNA datasets. The goals of this proposal are to substantially improve the accessibility and usability of computational circRNA research to all researchers, independent of computational expertise, infrastructure requirements, or source of data, addressed in three aims: 1) Develop innovative circtools modules with support for emerging approaches. New modules will significantly enhance the capabilities of the circtools core application with a specific focus on novel approaches, such as full-length circRNA sequencing. 2) Create a modular, multi-tool circRNA analysis pipeline providing functional insights. We will add multiple innovative features to the circtools pipeline that will allow for increased circRNA detection sensitivity using multiple circRNA detection tools and add functional insights using external data sources. 3) Design circtools.cloud, a user-friendly web portal to perform comprehensive circRNA analyses. Building on the expertise of this research team in creating R and Python-based software, the first dedicated circRNA web application will be created to process raw and processed data. This project will unlock computational circRNA tools for easy use by researchers who are currently unable to perform circRNA analyses due to missing infrastructure or computational expertise. Successful completion of this project will have an immediate and broad impact on circRNA research in human disease studies (as well as other species) involving circRNAs.