Sensitive, reliable, and robust circRNA detection from RNA-seq with CirComPara2

Abstract

Circular RNAs (circRNAs) are a large class of covalently closed RNA molecules that originate by a process called back-splicing. CircRNAs are emerging as functional RNAs involved in the regulation of biological processes as well as in disease and cancer mechanisms. Current computational methods for circRNA identification from RNA-seq experiments are characterised by low discovery rates and performance dependent on the analysed data set. We developed a new automated computational pipeline, CirComPara2 (https://github.com/egaffo/CirComPara2), that consistently achieves high recall rates without losing precision by combining multiple circRNA detection methods. In our benchmark analysis, CirComPara2 outperformed state-of-the-art circRNA discovery tools and proved to be a reliable and robust method for comprehensive transcriptomics characterisation.