Sensitive, reliable and robust circRNA detection from RNA-seq with CirComPara2. Issue 1 (26th October 2021)

• Record Type: Journal Article
• Authors: Gaffo, Enrico; Buratin, Alessia; Dal Molin, Anna; Bortoluzzi, Stefania
• Is Part Of: Briefings in bioinformatics. Volume 23:Issue 1(2022)
• Publication Date: 2021-10-26
• Subjects: circRNAs -- bioinformatics -- computational pipeline -- RNA-seq; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbab418
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Circular RNAs (circRNAs) are a large class of covalently closed RNA molecules originating 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 characterized by low discovery rates and performance dependent on the analysed data set. We developed CirComPara2 (https://github.com/egaffo/CirComPara2 ), a new automated computational pipeline for circRNA discovery and quantification, which 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 transcriptome characterization.