Specific identification and quantification of circular RNAs from sequencing data
Specific identification and quantification of circular RNAs from sequencing data
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DOI：
10.1093/bioinformatics/btv656
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发表时间：
2016-04-01
期刊：
影响因子：
5.8
通讯作者：
Dieterich, Christoph
中科院分区：
文献类型：
--
作者：
Cheng, Jun;Metge, Franziska;Dieterich, Christoph
Circular RNA（circRNA）是一种在几十年前就被发现的特征很差的分子。新兴的高通量测序方法以及关于确认功能的第一份报告引发了对这种RNA物种的新兴趣。然而，计算检测和定量工具仍然是有限的。结果：我们开发了软件串联，DCC和CircTest。DCC使用来自星星读取映射器的输出来系统地检测下一代测序数据中的反向剪接连接。DCC应用一系列过滤器并整合重复集的数据，以获得精确的circRNA候选物列表。我们评估了DCC在新生成的小鼠脑数据集和公开可用的测序数据上的检测性能。我们的软件在类似的灵敏度水平下比最先进的竞争对手实现了更高的精度。此外，DCC通过计数连接和非连接读段来估计circRNA相对于宿主基因的表达。这些读段计数最终用于通过我们的R软件包CircTest在不同实验条件下测试circRNA表达的宿主基因独立性。我们证明了这种方法对果蝇中先前报道的年龄依赖性circRNA的益处。
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.