Systematic Identification of Core Regulatory Circuitry from ENCODE Data

虽然在生成高质量的染色质状态和可访问性数据方面取得了很大进展 编码和路线图联盟，从这些数据中准确地识别细胞类型的特定增强子仍然是一个 这是一个巨大的挑战。我们最近开发了一种计算方法(GkmSVM)来预测监管 DNA序列中的元素，我们已经证明，当对来自每个元素的DHS数据进行gkmSVM训练时 在人类和小鼠的Encode和Roadmap细胞和组织中，它可以预测这两种细胞特异性增强子 与其他方法相比，更准确地反映了活动和调控变量(增量支持向量机)的影响。 GkmSVM模型封装了一组描述调节绑定的细胞类型特定权重 控制每种细胞类型中染色质可及性的现场词汇。一个令人震惊的观察是，显著的 GKM支持向量机权重通常可用随细胞类型变化的一小组(~20)Tf结合位点来识别， 与细胞类型特异性表达程序受小集合控制的假设一致 在相互作用的调节电路中紧密耦合的核心因素。这些核心调节器的扰动 能够在这种遗传回路的稳定分化细胞类型状态之间进行转换。在这里，我们将使用 GkmSVM系统地识别所有现有ENCODE和路线图人类中的核心调节电路 和小鼠细胞系和组织，并产生基于DNA序列的基因组调控图和精细尺度 预测在预测的调控区域内的核心调控结合位点。我们将生成绑定 每种细胞类型的核心调节器的现场模型，通过直接 实验验证。这张地图的价值很大程度上取决于它的精确度，所以我们证明了 GkmSVM预测在大规模并行增强报告和 荧光素酶验证分析，在调节元件预测的盲目社区评估(CAGI)中，以及在 预测有效的因果疾病相关变异。相反，我们展示了使用PWM的方法 对转铁蛋白结合位点的描述明显不那么准确。我们将产生碱基对分辨预测 多个ENCODE检测到的更广泛调控区域内的细胞特异性转铁蛋白结合位点(TFBS) 表观基因组图谱数据集，并与Function合作测试这些TFBS预测 表征中心(FCC)。我们的规则性地图将帮助设计和提供有针对性的实验 探索调控机制，并帮助解释疾病相关的非编码变体。

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.