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.