Comprehensive assessment of mRNA isoform detection methods for long-read sequencing data

Yaqi Su, Zhejian Yu, Siqian Jin, Zhipeng Ai, Ruihong Yuan, Xinyi Chen, Ziwei Xue, Yixin Guo, Di Chen, Hongqing Liang, Zuozhu Liu and Wanlu Liu ()
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Yaqi Su: Zhejiang University
Zhejian Yu: Zhejiang University
Siqian Jin: Zhejiang University
Zhipeng Ai: Zhejiang University
Ruihong Yuan: Zhejiang University
Xinyi Chen: Zhejiang University
Ziwei Xue: Zhejiang University
Yixin Guo: Zhejiang University
Di Chen: Zhejiang University
Hongqing Liang: Zhejiang University
Zuozhu Liu: Zhejiang University
Wanlu Liu: Zhejiang University

Nature Communications, 2024, vol. 15, issue 1, 1-19

Abstract: Abstract 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.

Date: 2024
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DOI: 10.1038/s41467-024-48117-3

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