Specific identification and quantification of circular RNAs from sequencing data.

Abstract

Circular RNAs (circRNAs) are a poorly characterized class of molecules that have been identified decades ago. Emerging high-throughput sequencing methods as well as first reports on confirmed functions have sparked new interest in this RNA species. However, the computational detection and quantification tools are still limited. We developed the software tandem, DCC and CircTest DCC uses output from the STAR read mapper to systematically detect back-splice junctions in next-generation sequencing data. DCC applies a series of filters and integrates data across replicate sets to arrive at a precise list of circRNA candidates. We assessed the detection performance of DCC on a newly generated mouse brain data set and publicly available sequencing data. Our software achieves a much higher precision than state-of-the-art competitors at similar sensitivity levels. Moreover, DCC estimates circRNA versus host gene expression from counting junction and non-junction reads. These read counts are finally used to test for host gene-independence of circRNA expression across different experimental conditions by our R package CircTest We demonstrate the benefits of this approach on previously reported age-dependent circRNAs in the fruit fly. The source code of DCC and CircTest is licensed under the GNU General Public Licence (GPL) version 3 and available from https://github.com/dieterich-lab/[DCC or CircTest]. christoph.dieterich@age.mpg.de Supplementary data are available at Bioinformatics online.