可靠检测随机表观遗传突变和与心血管衰老的关联
Yaroslav Markov1, Morgan Levine2, Albert T Higgins-Chen3,4
1Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA.
GeroScience
|May 12, 2024
概括
随机表观遗传突变 (SEMs) 是不可靠的衰老生物标志物,由于技术噪音. 一个新的机器学习过器提高了SEM的可靠性和分析,增强了他们对老龄化研究的潜力.
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.
- 生物标志物 生物标志物
- 计算生物学 计算生物学
背景情况:
- 随机表观遗传突变 (SEMs) 是被提议的衰老生物标志物.
- SEM代表了异常DNA甲基化模式.
- 对于生物解释来说,SEM检测的可靠性至关重要.
研究的目的:
- 评估SEM检测的可靠性.
- 确定影响SEM可靠性的因素.
- 开发一种用于过不可靠的SEM的方法,并分析它们与衰老的关联.
主要方法:
- 在三个数据集中分析技术复制数据.
- 确定影响SEM可靠性的因素 (例如,细胞组成,探头统计数据).
- 为SEM开发和验证基于机器学习的过器.
- 在Framingham心脏研究中评估SEM与衰老表型的关联.
主要成果:
- 检测SEM的可靠性很低,其中很大一部分在复制品之间无法重现.
- 血液细胞类型,探针统计,基因组位置和SNP影响SEM可靠性.
- 机器学习过器提高了验证数据集中的SEM可靠性.
- 衰老关联主要与可靠的SEM子集相关,并在过和调整细胞组成后被保留/增强.
结论:
- 由于技术因素,大量检测到的SEM是不可靠的.
- 一个机器学习过器有效地提高了SEM可靠性.
- 可靠的SEM,特别是特定探头子集中的hypoSEM和hyperSEM,显示出与老化表型的强有力的关联.
- 最佳实践和R包SEMdetectR被引入用于可靠的SEM分析.
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