功能解释的多omics差分推理 (MoDIFI):一个统计框架来优先考虑神经发育变异的细胞系
bioRxiv : the preprint server for biology
|February 9, 2026
概括
选择正确的细胞系对于研究神经发育障碍 (NDD) 的非编码变体至关重要. 我们的新方法MoDIFI有助于识别细胞特异性调节效应,以进行准确的功能测试.
科学领域:
- 基因组学就是基因组学.
- 神经科学是一个神经科学.
- 计算生物学 计算生物学
背景情况:
- 非编码变体与神经发育障碍 (NDD) 有关.
- 这些变异的调控效应往往是细胞类型特异性的.
- 选择合适的体外模型用于高通量测试是具有挑战性的.
研究的目的:
- 为了确定哪些细胞系和调控区域最好地揭示非编码变异的等位基特效应.
- 开发一个用于整合多omics数据的计算框架,以预测细胞特定的监管活动.
主要方法:
- 在人类和小鼠神经元细胞系中生成匹配的RNA-seq,ATAC-seq和Hi-C配置文件,以及一个非神经元细胞系.
- 开发了一种贝叶斯框架MoDIFI (功能解释的多omics差分推理),一个贝叶斯框架.
- 量化了细胞系特异性调控活性,使用对差异性基因循环相互作用的后置包含概率.
主要成果:
- MoDIFI成功地集成了正交的多omics数据,以识别细胞系解析的监管图.
- 通过协调的3D接触,染色质可访问性和转录输出支持的确定调节区域.
- 突出了不同细胞类型的共享突触程序和上下文依赖的调节机制.
结论:
- 莫迪菲为NDD变异功能测试提供了优先考虑信息细胞系和监管要素的实用策略.
- 这种方法有助于针对性地调查神经发育障碍中的非编码变异.
- 能够更高效,更准确地对与NDD相关的遗传变异进行功能验证.
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