在未定向DNA的预处理和规范化方面的挑战和良好实践 附带组学数据 在暴露组学研究中
Pablo Vangeenderhuysen1, Matthijs Vynck1, Liesa Engelen2
1Laboratory of Integrative Metabolomics (LIMET), Ghent University, 9820 Merelbeke, Belgium.
Analytical chemistry
|March 16, 2026
概括
这项研究优化了非向的DNA附加组学数据预处理,用于暴露组学研究. 我们使用xcms R包开发了一种可重复的工作流程,用于对大型样本集进行可靠的分析.
科学领域:
- 环境健康科学 环境健康科学
- 分析化学 分析化学
- 生物分子分析
背景情况:
- 基因添加学对暴露学至关重要,但对于大样本序列缺乏标准化预处理.
- 非定向的DNA附加学数据预处理对于准确的分析至关重要,但很少被应用.
- 优化数据预处理对于通过DNA adductomics推进暴露学研究至关重要.
研究的目的:
- 为了优化一个DNA adductomics数据预处理工作流在真正的非目标模式.
- 建立一个可复制的程序,用于分析DNA adductomics的大样本序列.
- 评估用于可靠的下游数据分析的规范化方法在DNA adductomics.
主要方法:
- 利用xcms R包优化峰值检测,保留时间对齐和峰值分组的参数.
- 应用了来自胎盘组织 (n=375) 和血液样本 (n=51) 的LC-MS数据,以优化工作流程.
- 测试和定量评估了六种基于样本和特征的规范化方法.
主要成果:
- 开发了一种成功且可重复的程序,用于优化xcms参数用于DNA附加组学.
- 对标准化方法的评估突出了客观 (RSD*,D比率) 和主观 (PCA分数图) 度量的重要性和局限性.
- 优化的工作流允许可靠地检测和整合假定DNA adduct LC-MS峰值.
结论:
- 拟议的工作流支持可复制和透明的非定向DNA附加组学数据预处理.
- 这一进步对于在大规模的暴露学研究中实施DNA附加组学至关重要.
- 标准化预处理提高了DNA增生学数据的可靠性和可比性.
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