Computational epigenetics modeling of cell identity genes

细胞身份基因是一组功能相关的基因，它们共同实现特定细胞的表型 类型.细胞身份研究的一个主要制约因素是缺乏一个强大的方法来定义身份目录 基因，并鉴定调节细胞表达网络的主转录因子。 身份基因和驱动细胞身份特化。 我们最近的发现引起了我们的兴趣，我们假设细胞身份基因可以通过表观遗传学来识别。 这一特征体现了它们独特的转录调控机制。我们和其他几个组织 发现细胞身份基因显示出独特的表观遗传特征，例如，宽H3 K4 me 3（Chen，等， Nature Genetics，2015）和超级增强子。我们说明了这些特征与强的和 在其相关细胞类型中而不是在其它细胞中细胞身份基因的稳定转录激活信号 类型生物学家使用超级增强子或广泛的H3 K4 me 3作为标记来命名细胞身份基因 最近然而，对于大多数生物学家来说，使用这种方法仍然具有挑战性，因为所需的生物信息学 工具尚未提供。 我们的总体目标是在这个建议是扩大我们的细胞计算表观遗传方法的发展， 身份基因发现利用我们的生物信息学算法DANPOS的早期成功（Chen等人， Genome Research，2013）和DANPOS 2（Chen，et al，Nature Genetics，2015），我们将开发一系列的 新算法（1）定义细胞身份基因的表观遗传特征，（2）自定义ChIP-Seq参数 表观遗传学特征分析;（3）在广泛文献检索的基础上，收集已知的细胞身份基因 随后进行人工检查，（4）系统鉴定未知细胞身份基因，和（5）确定主 调节细胞身份基因网络并驱动细胞身份特化的转录因子。作为 原则证明，我们将应用我们的新方法来研究细胞身份决定因素的EC在 与John P. Cooke，Longhou Fang和Qi Cao博士合作，这三位专家在EC生物学，血管生成， 和表观遗传学 本研究的成功完成有望对细胞身份的研究产生广泛的积极影响 决定，转录调控和染色质表观遗传学。科学界将能够 使用本提案中开发的生物信息学工具来定义组蛋白修饰特征， 准确性，并预测身份基因及其主转录因子系统地为给定的细胞类型 在许多生物系统或疾病模型中。我们对内皮细胞新的身份基因的功能检测， 提高对内皮细胞分化、发育和表型的机制理解， 指导发现治疗血管疾病的治疗靶点。虽然我们关注组蛋白 EC身份基因的修饰特征，我们提出的生物信息学方法可以很容易地适应 研究所有细胞类型中的许多其他染色质标记和基因类别。

Cell identity genes are a group of functionally linked genes that jointly implement the phenotype of a given cell type. A major constraint on cell identity study is the lack of a robust method to define the catalogue of identity genes for a cell type, and to identify master transcription factors that regulate the expression network of cell identity genes and drive cell identity specification. Intrigued by our recent discoveries, we hypothesize that cell identity genes can be identified using epigenetic feature that manifests their distinct transcriptional regulation mechanism. We and several other groups discovered that cell identity genes display unique epigenetic features, e.g., broad H3K4me3 (Chen, et al, Nature Genetics, 2015) and super-enhancers. We illustrated that these features are associated with strong and stable transcription activation signals for cell identity genes in their associated cell type, but not in other cell types. Biologists have used super enhancers or broad H3K4me3 as makers to nominate cell identity genes recently. However, it is still challenging for most biologists to use this method, as the required bioinformatics tools are not yet available. Our overall goal in this proposal is to extend the development of our computational epigenetic methods for cell identity gene discovery. Leveraging the early success of our bioinformatics algorithms DANPOS (Chen, et al, Genome Research, 2013) and DANPOS2 (Chen, et al, Nature Genetics, 2015), we will develop a series of new algorithms to (1) define epigenetic features for cell identity genes, (2) customize parameters for ChIP-Seq analysis of epigenetic feature, (3) collect known cell identity genes on the basis of thorough literature search followed by manual inspection, (4) systematically identify unknown cell identity genes, and (5) define master transcription factors that regulate the network of cell identity genes and drive cell identity specification. As a proof of principle, we will apply our novel methods to study cell identity determinants for the ECs in collaboration with Drs. John P. Cooke, Longhou Fang, and Qi Cao, three experts in EC biology, angiogenesis, and epigenetics. Successful completion of this study is expected to have broad positive impact on the study of cell identity determination, transcriptional regulation, and chromatin epigenetics. The scientific community will be able to use the bioinformatics tools developed in this proposal to define histone modification features with improved accuracy, and to predict identity genes and their master transcription factors systematically for given cell types in numerous biological systems or disease models. Our functional assay for new identity genes of ECs will improve mechanistic understanding of endothelial differentiation, development, and phenotypes, and will better guide discovery of therapeutic targets for treatment of vascular diseases. Although we focus on histone modification features for EC identity genes, our proposed bioinformatics methods can be easily adapted to investigate many other chromatin marks and gene categories in all cell types.