University of Chinese Academy of Sciences.
Computational Genomics Lab, Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, China.
Brief Bioinform. 2018 Sep 28;19(5):803-810. doi: 10.1093/bib/bbx014.
Computational detection methods have been widely used in studies on the biogenesis and the function of circular RNAs (circRNAs). However, all of the existing tools showed disadvantages on certain aspects of circRNA detection. Here, we propose an improved multithreading detection tool, CIRI2, which used an adapted maximum likelihood estimation based on multiple seed matching to identify back-spliced junction reads and to filter false positives derived from repetitive sequences and mapping errors. We established objective assessment criteria based on real data from RNase R-treated samples and systematically compared 10 circular detection tools, which demonstrated that CIRI2 outperformed its previous version CIRI and all other widely used tools, featured with remarkably balanced sensitivity, reliability, duration and RAM usage.
计算检测方法已广泛应用于环状 RNA (circRNA) 的生物发生和功能研究。然而，现有的所有工具在环状 RNA 检测的某些方面都存在缺陷。在这里，我们提出了一种改进的多线程检测工具 CIRI2，它使用基于多个种子匹配的自适应最大似然估计来识别回文拼接连接读取，并过滤来自重复序列和映射错误的假阳性。我们基于 RNase R 处理样品的真实数据建立了客观评估标准，并系统地比较了 10 种环状 RNA 检测工具，结果表明 CIRI2 优于其前一版本 CIRI 和所有其他广泛使用的工具，具有显著平衡的灵敏度、可靠性、时间和 RAM 使用。
