OncoRNALab, Cancer Research Institute Ghent (CRIG), Department of Biomolecular Medicine, Ghent University, Ghent, Belgium.
Department of Molecular Medicine, University of Padova, Padova, Italy.
Nat Methods. 2023 Aug;20(8):1159-1169. doi: 10.1038/s41592-023-01944-6. Epub 2023 Jul 13.
The detection of circular RNA molecules (circRNAs) is typically based on short-read RNA sequencing data processed using computational tools. Numerous 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 detected more than 315,000 unique circRNAs in three deeply sequenced human cell types. Next, 1,516 predicted circRNAs were validated using three orthogonal methods. Generally, tool-specific precision is 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 differentiators. Of note, precision values are lower when evaluating low-abundance circRNAs. We also show that the tools can be used complementarily to increase detection sensitivity. Finally, we offer recommendations for future circRNA detection and validation.
环状 RNA 分子 (circRNAs) 的检测通常基于使用计算工具处理的短读 RNA 测序数据。已经开发了许多这样的工具，但缺乏与正交验证的系统比较。在这里，我们建立了一个环状 RNA 检测工具基准研究，其中 16 个工具在三种深度测序的人类细胞类型中检测到超过 315,000 个独特的环状 RNA。接下来，使用三种正交方法验证了 1,516 个预测的环状 RNA。通常，工具特异性的精度较高且相似（qPCR、RNase R 和扩增子测序的中位数分别为 98.8%、96.3%和 95.5%），而敏感性和预测环状 RNA 的数量（范围从 1,372 到 58,032）是最显著的区分因素。值得注意的是，在评估低丰度环状 RNA 时，精度值较低。我们还表明，这些工具可以互补使用以提高检测灵敏度。最后，我们为未来的环状 RNA 检测和验证提供了建议。
