CirCodAn: A GHMM-based tool for accurate prediction of coding regions in circRNA.

Abstract

Studies focusing on characterizing circRNAs with the potential to translate into peptides are quickly advancing. It is helping to elucidate the roles played by circRNAs in several biological processes, especially in the emergence and development of diseases. While various tools are accessible for predicting coding regions within linear sequences, none have demonstrated accurate open reading frame detection in circular sequences, such as circRNAs. Here, we present cirCodAn, a novel tool designed to predict coding regions in circRNAs. We evaluated the performance of cirCodAn using datasets of circRNAs with strong translation evidence and showed that cirCodAn outperformed the other tools available to perform a similar task. Our findings demonstrate the applicability of cirCodAn to identify coding regions in circRNAs, which reveals the potential of use of cirCodAn in future research focusing on elucidating the biological roles of circRNAs and their encoded proteins. cirCodAn is freely available at https://github.com/denilsonfbar/cirCodAn.