Predict transcriptional enhancers using epigenetic signatures

描述（由申请人提供）：真核基因转录的激活涉及大量转录因子和辅助因子在调控DNA序列（如启动子和增强子）以及包含这些元件的染色质结构上的协调。因此，鉴定这些调控DNA元件对于理解健康和病变细胞中的基因调控至关重要。先前的研究表明，许多特征性的表观遗传修饰发生在调控DNA元件上，例如，在基因启动子和许多增强子上的高水平组蛋白乙酰化。此外，众所周知，许多调控元件仅在特定的细胞/组织类型中或根据环境条件携带这些表观遗传修饰。近年来，利用染色质免疫沉淀结合微阵列芯片（chip -chip）或下一代测序技术（chip - seq）产生了大量全基因组组蛋白修饰数据。目前，迫切需要计算方法来分析全基因组组蛋白修饰数据，以识别功能DNA元件。提出的研究目标是开发一种新的计算方法，以识别基于其表观遗传特征的转录调控元件。在机器学习领域，从原始数据中提取有意义的统计特征可以避免更多的相关信息，提高分类器的预测精度。我们假设，通过在分类前引入有效的数据转换和特征提取程序，我们可以提高我们识别转录调控元件的方法的整体预测精度。我们建议通过以下三个目标来检验上述假设：(1)我们将采用信号处理方面的完善措施来设计和测试一组统计特征，这些特征可以更好地表示组蛋白修饰数据中的信号。(2)我们将评估几种常用统计分类器在预测增强器方面的性能。然后，我们将开发一个软件工具，将Aim 1中最具信息量的特征与最佳分类器相结合。(3)我们将运用我们的计算方法，利用小鼠胚胎干细胞和人类T细胞的全基因组组蛋白修饰图谱，预测这两种细胞类型中的新型增强子。我们将使用计算和实验两种方法来评估我们预测的准确性。虽然在这个项目中，我们专注于增强子，但我们开发的方法可以很容易地扩展到使用不同生物体和细胞类型中的组蛋白修饰数据来发现其他类型的功能性DNA元件。)

DESCRIPTION (provided by applicant): Activation of eukaryotic gene transcription involves the coordination of a multitude of transcription factors and cofactors on regulatory DNA sequences such as promoters and enhancers and on the chromatin structure containing these elements. Therefore, identification of these regulatory DNA elements is of utmost importance for understanding gene regulation in both healthy and diseased cells. Previous studies have demonstrated many characteristic epigenetic modifications occur at regulatory DNA elements, e.g., high levels of histone acetylation at gene promoters and at many enhancers. In addition, it is known that many regulatory elements carry these epigenetic modifications only in specific cell/tissue types or according to environmental conditions. In recent years, a vast amount of genome-wide histone modification data has been generated using chromatin immunoprecipitation coupled with microarray chip (ChIP-chip) or with next-generation sequencing technologies (ChIP-Seq). Currently, there is a pressing need for computational methods to analyze genome-wide histone modification data in order to identify functional DNA elements. The goal of the proposed research is to develop a novel computational method to identify transcriptional regulatory elements on the basis of their epigenetic characteristics. In the field of machine learning, it is well established that meaningful statistical features extracted from raw data can elude more relevant information and increase the prediction accuracy of a classifier. We hypothesize that by introducing efficient data transformation and feature extraction procedures before classification, we can increase the overall prediction accuracy of our method for identifying transcriptional regulatory elements. We propose to test the aforementioned hypothesis by pursuing the following three aims: (1) We will adopt well-established measures from signal processing to design and test a set of statistical features that could give us a better representation of signals in histone modification data. (2) We will evaluate the performance of several commonly used statistical classifiers in predicting enhancers. We will then develop a software tool combining the most informative features from Aim 1 with the optimal classifier. (3) We will apply our computational method to predict novel enhancers in mouse embryonic stem cell and human T cell using genome-wide histone modification maps in these two cell types. We will use both computational and experimental approaches to evaluate the accuracy of our predictions. Although in this project we focus on enhancers, the approach we develop can be readily extended to discover other types of functional DNA elements using histone modification data in different organisms and cell types. ) PUBLIC HEALTH RELEVANCE: Transcriptional enhancers play an essential role in establishing tissue and developmental stage specific gene expression patterns that are essential for understanding development, cellular responses to environmental and genetic perturbations as well as the molecular basis of many diseases. The proposed research will lead to the development of a novel computational tool to discover enhancer elements using genome-wide chromatin signatures. Successful completion of the project will also uncover novel enhancers in two biomedically important cell types, embryonic stem cell and T lymphocyte, which could generate new insights into the regulatory networks controlling stem cell phenotype and T cell development and activation.