用深度卷积神经网络对基因表达和DNA结合部位的定位进行预测建模
Arman Karshenas1, Tom Röschinger2, Hernan G Garcia1,3,4,5,6
1Biophysics Graduate Group, University of California at Berkeley, Berkeley, CA, USA.
bioRxiv : the preprint server for biology
|January 7, 2025
概括
深度学习可适应的调控序列标识符 (DARSI) 使用MPRA数据来预测基因表达和识别转录因子结合部位. 这个工具通过揭示调节性DNA序列功能来增强基因组注释.
科学领域:
- 基因组学就是基因组学.
- 计算生物学 计算生物学
- 分子生物学分子生物学
背景情况:
- 基因组测序已经进步,但对调节性DNA上的转录因子结合部位的排列仍然在很大程度上没有特征.
- 大规模并行记者测试 (MPRA) 提供了一种方法来测量由数千种调节性DNA变异驱动的基因表达,但它们的分析通常假定独立的基对贡献.
- 目前的方法很难解释调节序列中远距离基体之间的相关性,限制了全面的基因组注释.
研究的目的:
- 开发一种计算工具,通过考虑监管DNA中远距离基之间的相关性来分析大规模并行报告测试 (MPRA) 数据.
- 为了能够直接从DNA序列中准确预测基因表达水平.
- 在监管区域内系统地确定单基对分辨率的转录因子结合点.
主要方法:
- 开发了深度学习适应性调节序列标识符 (DARSI),一个卷积神经网络.
- 训练有素的DARSI使用MPRA数据从原始调节性DNA序列预测基因表达.
- 通过对已知转录因子结合部位的精心策划的数据库进行比较,验证了DARSI的预测.
主要成果:
- DARSI准确地预测了转录因子结合部位,证明了与已建立的数据库的高度一致性.
- 该模型成功地确定了新的,以前未被绘制的转录因子结合部位.
- 达西的预测为未来的研究提供了实验性可操作的见解.
结论:
- 通过自动识别转录因子结合位点,DARSI提高了调节性DNA区域的注释.
- 该工具预测结合点的能力有助于理解转录控制机制.
- 达西的预测指导了未来的实验验证,推动了基因组学中的理论-实验循环.
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