选择差异剪接方法：实践考量
Selecting Differential Splicing Methods: Practical Considerations
AI总结：
本文综述22种RNA-seq差异剪接生物信息学工具，按统计方法和分析层级分类，比较其性能与适用场景，并提出工具选择指南。
摘要：
可变剪接在基因调控中至关重要，并在临床环境和生物技术中具有重要意义。本综述文章汇编了用于研究差异剪接的生物信息学 RNA-seq 工具；对其统计方法、案例应用和优势进行了详细考察。共有22个工具按其统计方法族（参数、非参数和概率）以及分析层级（转录本、外显子和事件）进行分类。量化可变剪接的核心挑战包括正确识别剪接位点以及准确进行转录本的异构体去卷积。基准测试研究表明，工具性能尚无共识，揭示了不同场景之间存在相当大的差异。建议未来研究人员使用具有较高引用频率且开发者持续维护的工具，例如 DEXSeq 和 rMATS。为帮助选择工具，本文根据数据输入变化和所需分析层级提出了一个指南示意图。此外，长读长 RNA 测序的进展预计将推动差异剪接工具的演进，减少对异构体去卷积的需求，并促进进一步创新。
英文摘要：
Alternative splicing is crucial in gene regulation, with significant implications in clinical settings and biotechnology. This review article compiles bioinformatics RNA-seq tools for investigating differential splicing; offering a detailed examination of their statistical methods, case applications, and benefits. A total of 22 tools are categorised by their statistical family (parametric, non-parametric, and probabilistic) and level of analysis (transcript, exon, and event). The central challenges in quantifying alternative splicing include correct splice site identification and accurate isoform deconvolution of transcripts. Benchmarking studies show no consensus on tool performance, revealing considerable variability across different scenarios. Tools with high citation frequency and continued developer maintenance, such as DEXSeq and rMATS, are recommended for prospective researchers. To aid in tool selection, a guide schematic is proposed based on variations in data input and the required level of analysis. Additionally, advancements in long-read RNA sequencing are expected to drive the evolution of differential splicing tools, reducing the need for isoform deconvolution and prompting further innovation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
