Comprehensive assessment of isoform detection methods for third-generation data

The advancement of Third-Generation Sequencing (TGS) techniques managed to increase the sequencing length to several kilobases, which leads to a bright future for completely reserving alternative splicing (AS) events and isoform expressions. In recent years, many computational methods for isoform detection from long-read sequencing data have been developed and published. However, there is no prior comparative study that systemically evaluates the performance of the software implemented with different algorithms. Here we benchmarked nine methods implemented in seven computational tools that can identify isoform structures from TGS RNA sequencing data and analyzed their performances from various aspects using both simulated datasets produced by an in-house simulator and previously published experimental data. Our results comprehensively demonstrate the relative effectiveness of the approaches and provide guidance as well as recommendations for future research on AS analysis and further improvement of the tools for isoform detection using TGS data.