通过深度学习揭示调节性DNA的机制格局
Evan E Seitz1, David M McCandlish1, Justin B Kinney1
1Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
bioRxiv : the preprint server for biology
|November 24, 2025
概括
SEAM是一种新的计算工具,通过分析遗传突变如何影响调节性DNA序列来解码基因调节. 它识别了功能结合点,并揭示了不同的调节信号类别,有助于变体解释和合成序列设计.
科学领域:
- 基因组学和分子生物学
- 计算生物学和生物信息学
- 进化生物学 进化生物学
背景情况:
- 调节基因组通过复杂的cis-regulatory元素控制基因表达.
- 解读转录因子动机,语法和序列上下文的相互作用是具有挑战性的.
- 了解基因突变对基因调节的影响对于解释非编码变异至关重要.
研究的目的:
- 开发一个计算框架 (SEAM) 来绘制基因突变对基因调节的机制影响.
- 发现功能结合位点并评估在监管区域内对这些位点的突变效应.
- 根据它们的突变稳定性和可重编程性来描述不同类别的监管信号.
主要方法:
- 开发了SEAM (基于归因机制的系统解释),集成深度学习和可解释的AI.
- 将SEAM应用于人类和Drosophila调节点,以分析遗传突变.
- 确定了功能结合点和分类的突变影响 (保存,破坏,创建新网站).
主要成果:
- SEAM成功地确定了功能结合位点,并描述了突变对这些位点的影响.
- 发现了两种不同的调节信号类别:突变强度和易于重编程的信号.
- 证明了SEAM在解释非编码变体和告知合成序列设计方面的能力.
结论:
- SEAM为了解基因调节和遗传变异的影响提供了一个多功能计算框架.
- 这些发现通过区分稳定和可适应的调节信号来澄清调节DNA的进化潜力.
- SEAM可以增强对非编码基因变异的解释,并指导新型监管序列的工程.
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