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加强新型异构体发现:利用纳米孔长读测序和机器学习方法
Kristina Santucci1, Yuning Cheng1, Si-Mei Xu1
1School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW 2052, Australia.
Briefings in functional genomics
|August 19, 2024
概括
长读测序提供了比短读方法更好的转录分析. 本综述比较了25种用于新型异形发现的工具,强调了转录学中标准化生物信息学管道的需要.
科学领域:
- 基因组学和转录基因组学
- 生物信息学和计算生物学
背景情况:
- 长读数测序捕获完整的RNA转录,与短读数测序相比,提高了转录模型的准确性.
- 长读精度和机器学习的进步增强了新型拼接异型检测和转录组重建的功能.
- 目前用于长期读取的转录学的生物信息学工具缺乏关于最佳性能的共识,以获得准确和一致的结果.
研究的目的:
- 通过使用长时间读取的测序数据,审查和比较生物信息学工具的性能,以发现新型异构体.
- 为转录学研究设计的25种不同的计算工具进行分析.
- 强调长读转录学中标准化分析管道和公约的必要性.
主要方法:
- 对25种不同的生物信息学工具进行比较分析,用于新型异构体的发现.
- 基于转录组学研究的精度和一致性的工具性能评估.
- 对应用到长读序列数据的当前方法和算法的文献综述.
主要成果:
- 在可用的生物信息学工具中确定了广泛的性能变化,用于长时间读取的转录学.
- 突出了机器学习和深度学习对提高转录组分析可靠性的影响.
- 展示了在不同计算管道中实现一致的新型异构体发现的挑战.
结论:
- 对于标准化生物信息学管道和工具,对于可靠的新型异构体发现有着至关重要的需求.
- 需要进一步发展,以建立长期阅读的转录组学研究的最佳实践和公约.
- 标准化将提高使用长读测序技术进行转录组分析的准确性和可重复性.
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