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噪音2读:通过图形学习编辑距离,准确地纠正数以百万计的错误短读

Pengyao Ping¹, Shuquan Su^1,2, Xinhui Cai¹

¹School of Computer Science, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney 2007, Australia.

Genomics, proteomics & bioinformatics

|November 29, 2025

概括

噪声2读取通过识别具有丰富邻居的罕见读取来纠正数据序列错误,从而保持数据完整性. 这种方法显著提高了短读质量和下游基因组分析.

科学领域:

基因组学和生物信息学
计算生物学计算生物学

背景情况:

短读序列数据,尽管每个基数的错误率很低 (0.1%-0.5%),但可以包含数百万个错误的读数 (10%-15%).
现有的错误纠正方法经常引入新的错误或无法完全恢复原始序列,从而损害数据完整性.

研究的目的:

开发一种新的方法, noise2read,用于精确纠正序列数据中的错误读取.
通过将错误的读取回归到它们的原始状态而不会产生不存在的序列来维护数据完整性.

主要方法:

从聚合酶连锁反应 (PCR) 错误机制中获得了一个可计算的规则:如果它有一个非常丰富的邻近读数,那么一个罕见的读数可能是错误的.
构建了一个图形,将读数与小编辑距离连接起来,以识别错误的读数,并将这些对作为训练数据用于改进错误识别.

主要成果:

在基于UMI的数据集上的19个指标中,Noise2read显著超过了最先进的方法.
在基因组丰度量化,同型鉴定,SNP分析和基因组编辑效率估计方面取得了实质性的改进.
案例研究证实 noise2read 能够提高短读序列的质量.

结论:

噪音2读取有效地纠正PCR增强测序数据中的错误读取.
该方法保持了数据完整性,并为各种基因组应用提供了显著的优势.
噪音2阅读是公开可用的,促进其在研究界的采用.

关键词:

编辑距离为两个读数.读数的图表读数的图表机器学习是机器学习.聚合酶连锁反应发生错误.短读错误纠正短读错误纠正

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