ABSTRACT

Chimeric RNAs are gaining more and more attention as they have broad implications in both cancer and normal physiology. To date, over 40 chimeric RNA prediction methods have been developed to facilitate their identification from RNA sequencing data. However, a limited number of studies have been conducted to compare the performance of these tools; additionally, previous studies have become outdated as more software tools have been developed within the last three years. In this study, we benchmarked 16 chimeric RNA prediction software, including seven top performers in previous benchmarking studies, and nine that were recently developed. We used two simulated and two real RNA-Seq datasets, compared the 16 tools for their sensitivity, positive prediction value (PPV), F-measure, and also documented the computational requirements (time and memory). We noticed that none of the tools are inclusive, and their performance varies depending on the dataset and objects. To increase the detection of true positive events, we also evaluated the pair-wise combination of these methods to suggest the best combination for sensitivity and F-measure. In addition, we compared the performance of the tools for the identification of three classes (read-through, inter-chromosomal and intra-others) of chimeric RNAs. Finally, we performed TOPSIS analyses and ranked the weighted performance of the 16 tools.

摘要

嵌合 RNA 越来越受到关注，因为它们在癌症和正常生理学中具有广泛的意义。迄今为止，已经开发了 40 多种嵌合 RNA 预测方法，以促进从 RNA 测序数据中识别它们。但是，已经进行了有限数量的研究来比较这些工具的性能；此外，随着过去三年内开发了更多的软件工具，以前的研究已经过时。在这项研究中，我们对 16 种嵌合 RNA 预测软件进行了基准测试，其中包括之前基准研究中表现最好的 7 种软件，以及最近开发的 9 种软件。我们使用了两个模拟的和两个真实的 RNA-Seq 数据集，比较了 16 种工具的灵敏度、阳性预测值 (PPV)、F 度量，还记录了计算要求（时间和内存）。我们注意到没有一个工具是包容性的，它们的性能因数据集和对象而异。为了增加对真阳性事件的检测，我们还评估了这些方法的成对组合，以建议灵敏度和 F 度量的最佳组合。此外，我们比较了用于鉴定三类嵌合 RNA（通读、染色体间和其他内部）的工具的性能。最后，我们进行了 TOPSIS 分析并对 16 种工具的加权性能进行了排名。我们比较了用于鉴定三类嵌合 RNA（通读、染色体间和其他内部）的工具的性能。最后，我们进行了 TOPSIS 分析并对 16 种工具的加权性能进行了排名。我们比较了用于鉴定三类嵌合 RNA（通读、染色体间和其他内部）的工具的性能。最后，我们进行了 TOPSIS 分析并对 16 种工具的加权性能进行了排名。