Specific identification and quantification of circular RNAs from sequencing data
Open Access
- 9 November 2015
- journal article
- research article
- Published by Oxford University Press (OUP) in Bioinformatics
- Vol. 32 (7), 1094-1096
- https://doi.org/10.1093/bioinformatics/btv656
Abstract
Motivation: 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. Results: 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. Availability and implementation: 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]. Contact:christoph.dieterich@age.mpg.de Supplementary information: Supplementary data are available at Bioinformatics online.This publication has 7 references indexed in Scilit:
- Erratum to: Statistically based splicing detection reveals neural enrichment and tissue-specific induction of circular RNA during human fetal developmentGenome Biology, 2016
- Statistically based splicing detection reveals neural enrichment and tissue-specific induction of circular RNA during human fetal developmentGenome Biology, 2015
- CIRI: an efficient and unbiased algorithm for de novo circular RNA identificationGenome Biology, 2015
- Genome-wide Analysis of Drosophila Circular RNAs Reveals Their Structural and Sequence Properties and Age-Dependent Neural AccumulationCell Reports, 2014
- Circular RNAs: diversity of form and functionRNA, 2014
- Detecting and characterizing circular RNAsNature Biotechnology, 2014
- STAR: ultrafast universal RNA-seq alignerBioinformatics, 2012