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.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.