Long-read sequencing technologies can capture entire RNA transcripts in a single sequencing read, reducing the ambiguity in constructing and quantifying transcript models in comparison to more common and earlier methods, such as short-read sequencing. Recent improvements in the accuracy of long-read sequencing technologies have expanded the scope for novel splice isoform detection and have also enabled a far more accurate reconstruction of complex splicing patterns and transcriptomes. Additionally, the incorporation and advancements of machine learning and deep learning algorithms in bioinformatic software have significantly improved the reliability of long-read sequencing transcriptomic studies. However, there is a lack of consensus on what bioinformatic tools and pipelines produce the most precise and consistent results. Thus, this review aims to discuss and compare the performance of available methods for novel isoform discovery with long-read sequencing technologies, with 25 tools being presented. Furthermore, this review intends to demonstrate the need for developing standard analytical pipelines, tools, and transcript model conventions for novel isoform discovery and transcriptomic studies.

中文翻译：

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加强新​​异构体的发现：利用纳米孔长读长测序和机器学习方法


长读长测序技术可以在单次测序读长中捕获整个 RNA 转录本，与更常见和早期的方法（例如短读长测序）相比，减少了构建和量化转录本模型时的模糊性。最近长读长测序技术准确性的提高扩大了新型剪接异构体检测的范围，并且还使得能够更准确地重建复杂的剪接模式和转录组。此外，机器学习和深度学习算法在生物信息学软件中的结合和进步显着提高了长读长测序转录组研究的可靠性。然而，对于哪些生物信息学工具和流程能够产生最精确和一致的结果，目前还缺乏共识。因此，本综述旨在讨论和比较使用长读长测序技术发现新异构体的可用方法的性能，并介绍了 25 种工具。此外，本综述旨在证明需要为新的亚型发现和转录组研究开发标准分析管道、工具和转录模型约定。