The detection of circular RNA molecules (circRNAs) is typically based on short-read RNA sequencing data processed by computational detection tools. During the last decade, a plethora of such tools have been developed, but a systematic comparison with orthogonal validation is missing. Here, we set up a circRNA detection tool benchmarking study, in which 16 tools were used and detected over 315,000 unique circRNAs in three deeply sequenced human cell types. Next, 1,516 predicted circRNAs were empirically validated using three orthogonal methods. Generally, tool-specific precision values are high and similar (median of 98.8%, 96.3%, and 95.5% for qPCR, RNase R, and amplicon sequencing, respectively) whereas the sensitivity and number of predicted circRNAs (ranging from 1,372 to 58,032) are the most significant tool differentiators. Furthermore, we demonstrate the complementarity of tools through the increase in detection sensitivity by considering the union of highly-precise tool combinations while keeping the number of false discoveries low. Finally, based on the benchmarking results, recommendations are put forward for circRNA detection and validation.

中文翻译：

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circRNA 检测工具的大规模基准测试揭示了灵敏度上的巨大差异，但精度上没有

环状 RNA 分子 (circRNA) 的检测通常基于计算检测工具处理的短读 RNA 测序数据。在过去十年中，开发了大量此类工具，但缺少与正交验证的系统比较。在这里，我们建立了一个 circRNA 检测工具基准研究，其中使用了 16 种工具，并在三种深度测序的人类细胞类型中检测到超过 315,000 个独特的 circRNA。接下来，使用三种正交方法对 1,516 个预测的 circRNA 进行了经验验证。通常，特定于工具的精度值很高且相似（qPCR、RNase R 和扩增子测序的中位数分别为 98.8%、96.3% 和 95.5%），而预测的 circRNA 的灵敏度和数量（范围从 1,372 到 58,032）是最重要的工具差异化因素。此外，我们通过考虑高精度工具组合的结合，同时保持较低的错误发现数量，通过提高检测灵敏度来证明工具的互补性。最后，基于基准测试结果，提出了circRNA检测和验证的建议。