非编码突变的表观遗传影响通过预训练的DNA语言模型在单细胞分辨率下破译
Zhe Liu1, An Gu1, Yihang Bao1
1Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200230, China.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|January 31, 2025
概括
深度学习工具Methven预测了在单细胞分辨率下对DNA甲基化非编码突变的影响. 它准确地识别了与疾病相关的表观遗传变化,有助于理解复杂疾病和个性化医学.
科学领域:
- 基因组学就是基因组学.
- 表观遗传学 在表观遗传学中,表观遗传学是指表观遗传学.
- 计算生物学 计算生物学
背景情况:
- 基因甲基化对于基因调节和疾病至关重要,特别是在非编码区域.
- 在单细胞分辨率下预测非编码突变的表观遗传效应具有挑战性.
- 目前的工具缺乏动态,细胞类型特定的监管变化预测能力.
研究的目的:
- 介绍Methven,一个深度学习框架,用于预测在单细胞分辨率下对DNA甲基化非编码突变的影响.
- 以高准确度建模SNP-CpG相互作用和监管变化.
- 为了确定与疾病相关的表观遗传变化和途径.
主要方法:
- 梅思文集成了DNA序列和单细胞ATAC-seq数据.
- 它采用了分裂与征服的方法和预训练的DNA语言模型.
- 模型SNP-CpG相互作用超过100 kbp的基因组距离.
主要成果:
- 梅思文准确地预测了短期和长期的监管相互作用.
- 它的性能优于现有的方法,并且可以将其推广到单细胞数据集.
- 确定了与类风湿性关节炎和免疫调节通路相关的CpG部位.
结论:
- 梅思文为理解复杂疾病中的非编码突变和表观遗传变化提供了一个强大的工具.
- 它提供了对基因调节和疾病进展的见解.
- 证明了基础研究,临床应用和个性化医学的潜力.
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