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在WGBS中使用CNN模型回顾低覆盖点的DNA甲基化水平
Ximei Luo1,2, Yansu Wang1,2, Quan Zou2,3
1School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen, Guangdong, China.
PLoS computational biology
|June 14, 2023
概括
使用相邻的数据,RcWGBS准确地将DNA甲基化水平与低覆盖范围的全基因组二硫酸盐测序 (WGBS) 位点相关. 这种计算方法提高了DNA甲基化数据的利用率,并降低了研究人员的测序成本.
科学领域:
- 表观遗传学 在表观遗传学中,表观遗传学是指表观遗传学.
- 基因组学就是基因组学.
- 计算生物学 计算生物学
背景情况:
- 基因甲基化调节基因转录,并通过全基因组双硫酸盐测序 (WGBS) 量化测量.
- WGBS需要高的测序深度,导致许多CpG站点的覆盖率不足,以及不准确的甲基化水平估计.
- 现有的计算方法往往需要额外的欧米克数据或交叉样本信息,只能预测甲基化状态.
研究的目的:
- 开发一种新的计算方法,RcWGBS,用于在WGBS数据中赋值缺失或低覆盖DNA甲基化值.
- 通过深度学习利用邻近站点的DNA甲基化水平进行准确的预测.
主要方法:
- 提出了RcWGBS,一种基于深度学习的方法来计算DNA甲基化水平.
- 使用了来自H1-hESC和GM12878细胞系的下方采样WGBS数据集.
- 将RcWGBS性能与METHimpute在低测序深度 (例如12×) 的性能进行比较.
主要成果:
- 与H1-hESC和GM12878细胞中的高深度数据相比,RcWGBS实现了高准确性,平均差异小于0.03和0.01,分别为.
- 即使在12×的低测序深度下,RcWGBS的性能也超过了METHimpute.
- 该方法有效地使用邻近站点的信息来归因DNA甲基化水平.
结论:
- RcWGBS为处理低深度WGBS数据提供了准确和高效的解决方案.
- 这种方法可以显著降低测序成本,并提高甲基化数据的实用性.
- 通过使有限的测序深度能够对表观基因组数据进行可靠的分析,促进了更广泛的研究应用.
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