从转录组学和染色质可访问性数据推断基因调节网络的单细胞多式视图
Jens Uwe Loers1,2,3, Vanessa Vermeirssen1,2,3
1Lab for Computational Biology, Integromics and Gene Regulation (CBIGR), Cancer Research Institute Ghent (CRIG), Corneel Heymanslaan 10, 9000 Ghent, Belgium.
Briefings in bioinformatics
|August 29, 2024
概括
基因调控网络推断正在与多omics数据一起推进. 在单细胞水平上整合转录基因组学和表观基因组学是理解发育和疾病中的基因调节的关键.
科学领域:
- 分子生物学分子生物学
- 系统生物学 系统生物学
- 基因组学就是基因组学.
背景情况:
- 细胞基因调节是复杂的,涉及组合,动态和定量过程,对发育和疾病至关重要.
- 基因调控网络 (GRNs) 模拟了这些过程,最近的进展是由多omics数据驱动的,特别是在单细胞水平.
- 增强器GRN (eGRNs) 专门模拟转录因子,调节元件和基因之间的相互作用.
研究的目的:
- 通过转录学和染色质可访问性数据,审查成功的GRN和eGRN推断的关键组件.
- 突出最先进的方法,挑战和该领域的未来方向.
- 强调将多omics数据集成为机械网络推理.
主要方法:
- 使用单细胞RNA测序 (scRNA-seq) 和单细胞ATAC测序 (scATAC-seq) 数据.
- 运用计算策略对omics配对,转录因子结合部位检测和色素相互作用分析.
- 探索线性和3D方法来识别监管元素相互作用.
主要成果:
- 转录学和染色质可访问性的组合使细粒度的调节程序预测能够超出简单的表达相关性.
- eGRNs提供了调节通路内的分子相互作用的详细模型.
- 该审查涵盖了预处理,元细胞生成和omics集成技术.
结论:
- 在单细胞水平上整合转录基因组学和表观基因组学数据代表了机械网络推断的新标准.
- 未来的进步在于整合额外的奥米克层,时空数据,并转向定量和因果建模.
- 这种方法对于更深入地了解生物过程和疾病中的基因调节至关重要.
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