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一种无映射的基于自然语言处理的技术,用于在纳米孔长读数中进行序列搜索

Tomasz Strzoda¹, Lourdes Cruz-Garcia², Mustafa Najim²

¹Department of Data Science and Engineering, Silesian University of Technology, Gliwice, Poland.

BMC bioinformatics

|November 13, 2024

概括

一个新的自然语言处理工具为辐射剂量估计提供了快速而准确的基因表达映射. 这种方法显示出高负预测值 (NPV),在长纳米孔读数方面表现优于经典方法.

科学领域:

生物信息学是一种生物信息学.
计算生物学计算生物学
基因组学就是基因组学.

背景情况:

准确的基因表达分析对于辐射紧急情况的剂量估计至关重要.
针对性基因映射的现有方法可能是计算密集型和耗能.
需要快速,低能耗,现场测序分析工具.

研究的目的:

使用自然语言处理 (NLP) 技术开发一个序列识别工具.
为了实现高负预测值 (NPV) 的基因表达分析.
为了创建一个计算价格低廉和节能替代传统的绘图方法.

主要方法:

探索各种NLP模型,使用不同的词典组件和编码长度.
在RNA测序数据上的NLP模型的培训和验证.
将NLP模型的性能与经典的映射工具 (如minimap2.2) 的性能进行比较.

主要成果:

最优的NLP配置分析了整个序列的3个基数对单词长度.
最好的模型实现了FDXR基因的98.29%平衡精度 (BACC) 和99.25%的NPV.
缩小字典大小保持了高的NPV (98.15%内部,99.64%外部),对读数准确度的影响最小.

结论:

基于NLP的映射是针对长纳米孔读数的定向转录分析中经典方法的可靠替代品.
开发的模型可以适应不同的转录目标和长读测序技术.
经典的文字处理技术显示了核酸序列分析的巨大潜力.

关键词:

调整调整调整机器学习是机器学习.自然语言处理自然语言处理.测序测序是指测序的时间.文字转录学 (Transcriptomics) 是一个学科.

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