Abstract
Protein-encoding circular RNAs (circRNAs) are newly identified RNA molecules characterized by intense interaction with translating ribosome. Emerging evidence has implicated physiological and pathological significance of these non-canonical RNAs, yet a large body of them remains unidentified. Due to limited tools at hand, we developed CircProPlus, an automated computational pipeline for de novo detection of translated circRNAs. In comparison to previously established CircPro, CircProPlus adjusts the overall workflow and integrates more robust implements for achieving easier accessibility, higher flexibility and productivity. In present study, we tested the performance of CircProPlus when using different circRNA-detecting implements (i.e., CIRI2, CirComPara2) in the evaluation of coding ability of circRNAs. Results showed that CirComPara2, a state-of-the-art algorithm, consistently outperformed CIRI2 when coupled with CircProPlus in testing real data collected from different RNA libraries and species, which highlighted its potency in data mining of circRNAs with protein-coding potential.