Comprehensive Assessment of Isoform Detection Methods for Third-Generation Sequencing Data

Abstract The advancement of Third-Generation Sequencing (TGS) techniques has significantly increased the length of sequencing to several kilobases, thereby facilitating the identification of alternative splicing (AS) events and isoform expressions. Recently, numerous computational methods for isoform detection using long-read sequencing data have been developed. However, there is lack of prior comparative studies that systemically evaluates the performance of these software tools, implemented with different algorithms, under various simulations that encompass potential influencing factors. In this study, we conducted a benchmarking analysis of eleven methods implemented in eight computational tools capable of identifying isoform structures from TGS RNA sequencing data. We evaluated their performances using simulated data, which represented diverse sequencing platforms generated by an in-house simulator, as well as experimental data. Our comprehensive results demonstrate the guided mode of StringTie2 and Bambu achieved the best performance in sensitivity and precision, respectively. This study provides valuable guidance for future research on AS analysis and the ongoing improvement of tools for isoform detection using TGS data.