The advancement of Long-Read Sequencing (LRS) techniques has significantly increased the length of sequencing to several kilobases, thereby facilitating the identification of alternative splicing events and isoform expressions. Recently, numerous computational tools for isoform detection using long-read sequencing data have been developed. Nevertheless, there remains a deficiency in comparative studies that systemically evaluate the performance of these tools, which are implemented with different algorithms, under various simulations that encompass potential influencing factors. In this study, we conducted a benchmark analysis of thirteen methods implemented in nine tools capable of identifying isoform structures from long-read RNA-seq data. We evaluated their performances using simulated data, which represented diverse sequencing platforms generated by an in-house simulator, RNA sequins (sequencing spike-ins) data, as well as experimental data. Our findings demonstrate IsoQuant as a highly effective tool for isoform detection with LRS, with Bambu and StringTie2 also exhibiting strong performance. These results offer valuable guidance for future research on alternative splicing analysis and the ongoing improvement of tools for isoform detection using LRS data.

长读长测序（LRS）技术的进步已将测序长度显着增加至数千碱基，从而促进了可变剪接事件和异构体表达的识别。最近，已经开发了许多使用长读长测序数据进行异构体检测的计算工具。然而，在包含潜在影响因素的各种模拟下系统地评估这些工具的性能的比较研究仍然存在缺陷，这些工具是使用不同的算法实现的。在这项研究中，我们对 9 种能够从长读 RNA-seq 数据中识别异构体结构的工具中实施的 13 种方法进行了基准分析。我们使用模拟数据评估了他们的性能，模拟数据代表了内部模拟器生成的不同测序平台、RNA亮片（测序尖峰）数据以及实验数据。我们的研究结果表明，IsoQuant 是一种使用 LRS 检测异构体的高效工具，Bambu 和 StringTie2 也表现出强大的性能。这些结果为未来选择性剪接分析的研究以及使用 LRS 数据进行异构体检测工具的持续改进提供了宝贵的指导。