Systematic Identification of Core Regulatory Circuitry from ENCODE Data

从 ENCODE 数据系统识别核心监管电路

基本信息

批准号：
10238262
负责人：
Michael A Beer
金额：
$ 57.23万
依托单位：
JOHNS HOPKINS UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-02-01 至 2023-01-31
项目状态：
已结题

项目摘要

While much progress has been made generating high quality chromatin state and accessibility data from the ENCODE and Roadmap consortia, accurately identifying cell-type specific enhancers from these data remains a significant challenge. We have recently developed a computational approach (gkmSVM) to predict regulatory elements from DNA sequence, and we have shown that when gkmSVM is trained on DHS data from each of the human and mouse ENCODE and Roadmap cells and tissues, it can predict both cell specific enhancer activity and the impact of regulatory variants (deltaSVM) with greater precision than alternative approaches. The gkmSVM model encapsulates a set of cell-type specific weights describing the regulatory binding site vocabulary controlling chromatin accessibility in each cell type. A striking observation is that the significant gkmSVM weights are generally identifiable with a small (~20) set of TF binding sites which vary by cell-type, consistent with the hypothesis that cell-type specific expression programs are controlled by a small set of core factors tightly coupled in mutually interacting regulatory circuits. Perturbations of these core regulators enable transitions between stable differentiated cell-type states of this genetic circuit. Here, we will use gkmSVM to systematically identify the core regulatory circuitry in all existing ENCODE and Roadmap human and mouse cell lines and tissues, and produce DNA sequence based genomic regulatory maps and fine-scale predictions of core regulator binding sites within predicted regulatory regions. We will generate binding site models for core regulators in each cell type, assess the accuracy of our predictions through direct experimental validation. The value of this map critically depends on its accuracy, so we demonstrate that gkmSVM predictions consistently outperform alternative methods in massively parallel enhancer reporter and luciferase validation assays, in blind community assessments of regulatory element predictions (CAGI), and in predicting validated causal disease associated variants. In contrast, we show that methods using PWM descriptions of TF binding sites are significantly less accurate. We will produce base-pair resolution predictions of the cell specific TF binding sites (TFBS) within broader regulatory regions detected by multiple ENCODE epigenomic Mapping datasets, and to test these TFBS predictions in collaboration with Functional Characterization Centers (FCC). Our regulatory maps will help design and inform focused experiments probing regulatory mechanisms, and aid in the interpretation of disease associated non-coding variants.

尽管已经取得了很大的进步，从而产生了高质量的染色质状态和可访问性数据编码和路线图联盟，从这些数据中准确识别细胞类型的特定增强子仍然重大挑战。我们最近开发了一种计算方法（GKMSVM）来预测调节 DNA序列的元素，我们已经表明，当GKMSVM在每个DHS数据上训练时在人类和小鼠编码以及路线图细胞和组织中，它可以预测两个细胞特异性增强子与替代方法相比，活性和调节变体（DeltasVm）的影响更高。 GKMSVM模型封装了一组细胞类型的特定权重，描述了调节结合位点词汇控制每种单元类型中的染色质可及性。一个惊人的观察是重要的 GKMSVM权重通常可以通过一组小（约20）个TF结合位点来识别，这些位点因细胞类型而变化，与细胞类型特定表达程序受到小集控制的假设一致核心因素与相互相互作用的调节电路紧密耦合。这些核心调节器的扰动在该遗传回路的稳定分化细胞类型状态之间启用过渡。在这里，我们将使用 GKMSVM系统地识别所有现有编码和路线图人的核心监管电路和小鼠细胞系和组织，并产生基于DNA序列的基因组调节图和细尺度预测调节区域内的核心调节剂结合位点的预测。我们将生成绑定每种单元格中核心调节器的站点模型，通过直接评估我们预测的准确性实验验证。该地图的价值在很大程度上取决于其准确性，因此我们证明 GKMSVM预测在大规模并行增强器报告中的替代方法始终优于替代方法在监管要素预测（CAGI）的盲目社区评估中，荧光素酶验证测定法（CAGI）和预测已验证的因果疾病相关变体。相反，我们显示了使用PWM的方法 TF结合位点的描述明显降低了精度。我们将产生基本对分辨率预测由多个编码检测到的更广泛调节区域内的细胞特异性TF结合位点（TFB）表观基因组映射数据集，并与功能合作测试这些TFB的预测表征中心（FCC）。我们的监管地图将有助于设计并为重点实验提供信息探测调节机制，并有助于解释疾病相关的非编码变体。