Enhancer Predictions and Genome-Wide Regulatory Circuits.
Enhancer Predictions and Genome-Wide Regulatory Circuits.
复制标题
DOI：
10.1146/annurev-genom-121719-010946
复制
发表时间：
2020-08-31
影响因子：
8.7
通讯作者：
Huangfu D
中科院分区：
文献类型：
--
作者：
Beer MA;Shigaki D;Huangfu D
发育过程中基因表达的时空控制需要大量增强子的协调活动，这些增强子是顺式调节 DNA 序列，当与转录因子 (TF) 结合时，支持相关基因的选择性激活或抑制。增强子的正确激活在胚胎发育、成体组织稳态和再生过程中至关重要；不适当的增强子活性通常与癌症等病理状况有关。多个联盟（例如 ENCODE、Roadmap）和独立研究人员已经在大量细胞类型和组织中绘制了假定的调控区域，但细胞特异性增强子的序列决定因素尚未完全了解。在大量这些调控区域上训练的机器学习方法可以识别核心 TF 结合位点，并生成增强子活性和序列变异对活性的影响的定量预测。在这里，我们在增强子预测和指定细胞命运的基因调控网络模型的背景下回顾这些计算方法。
Spatiotemporal control of gene expression during development requires orchestrated activities of numerous enhancers, which are cis-regulatory DNA sequences that, when bound by transcription factors (TFs), support selective activation or repression of associated genes. Proper activation of enhancers is critical during embryonic development, adult tissue homeostasis, and regeneration; and inappropriate enhancer activity is often associated with pathological conditions such as cancer. Multiple Consortia (e.g., ENCODE, Roadmap) and independent investigators have mapped putative regulatory regions in a large number of cell types and tissues, but the sequence determinants of cell specific enhancers are not yet fully understood. Machine learning approaches trained on large sets of these regulatory regions can identify core TF binding sites and generate quantitative predictions of enhancer activity and the impact of sequence variants on activity. Here, we review these computational methods in the context of enhancer prediction and gene regulatory network models specifying cell fate.
登录
查看更多内容
影响因子：
1.9
作者：
Ghandi, Mahmoud;Mohammad-Noori, Morteza;Beer, Michael A.
通讯作者：
Beer, Michael A.
影响因子：
9.8
作者：
Gusev, Alexander;Lee, S. Hong;Price, Alkes L.
通讯作者：
Price, Alkes L.
影响因子：
64.5
作者：
Boyle EA;Li YI;Pritchard JK
通讯作者：
Pritchard JK
影响因子：
7
作者：
Gorkin DU;Lee D;Reed X;Fletez-Brant C;Bessling SL;Loftus SK;Beer MA;Pavan WJ;McCallion AS
通讯作者：
McCallion AS
影响因子：
4.3
作者：
Agius, Phaedra;Arvey, Aaron;Leslie, Christina
通讯作者：
Leslie, Christina