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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.

虽然已经取得了很大进展，但从染色质组生成高质量的染色质状态和可及性数据， ENCODE和Roadmap联盟，从这些数据中准确识别细胞类型特异性增强子仍然是一个重要的研究方向。重大挑战。我们最近开发了一种计算方法（gkmSVM）来预测调控我们已经证明，当gkmSVM在来自每个DNA序列的DHS数据上训练时，人和小鼠的ENCODE和Roadmap细胞和组织，它可以预测细胞特异性增强子活性和调节变体（deltaSVM）的影响，具有比替代方法更高的精度。 gkmSVM模型封装了一组描述调控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位点词汇。一个惊人的观察是， gkmSVM权重通常可通过一小组（~20）TF结合位点识别，所述TF结合位点随细胞类型而变化，这与细胞类型特异性表达程序由一小部分细胞控制的假设一致核心因素紧密耦合在相互作用的调节回路中。这些核心调节器的扰动使这种遗传电路的稳定分化细胞类型状态之间的转换成为可能。在这里，我们将使用 gkmSVM系统地识别所有现有的ENCODE和Roadmap人体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谱和精细尺度预测的调控区域内的核心调控结合位点。我们将生成绑定每个细胞类型中核心调节子的位点模型，通过直接实验验证这张地图的价值主要取决于它的准确性，所以我们证明， gkmSVM预测在大规模并行增强子报告基因和荧光素酶验证试验，调节元件预测的盲态社区评估（CAGI），以及预测经验证的致病性疾病相关变异。相反，我们表明，使用PWM的方法 TF结合位点的描述明显不准确。我们将产生碱基对分辨率预测在更广泛的调控区域内的细胞特异性TF结合位点（TFBS），表观基因组图谱数据集，并与Functional 表征中心（FCC）。我们的监管地图将有助于设计和告知重点实验探索调控机制，并有助于解释疾病相关的非编码变体。