Identification of Transcriptional Factor Binding Sites in Human Promoters

描述(申请人提供)：转录调控是人类基因组中一个高度协调的过程。转录调控的一个重要组成部分是转录因子蛋白和顺式调控DNA元件之间的相互作用。这个项目的目标是通过计算预测和实验验证解释启动子功能的DNA序列基序。该项目的结果将是碱基对分辨率下的序列基序的直接功能测量。这将产生极其有价值的信息，以评估算法的敏感性和特异性，这些算法可以立即应用于整个基因组。这些结果也将有助于确定功能相关的转录因子结合事件的比例。我们项目的三个目标是：目标1：我们将使用两种机器学习算法(支持向量机和随机森林)来确定最能预测启动子活性的已知转录因子结合基序的子集。目标2：然后我们将使用贝叶斯网络来选择最具预测性的基序特征。这些特征是使用PSSM的基序的强度，以及各个位点相对于彼此和转录起始点的位置。目的3：然后我们将对目标2中确定的900个位点中的信息位置进行突变，并通过瞬时转染试验来测量它们的启动子活性。我们还计划测试100个排名较低的网站，以确定我们算法的敏感性和特异性。我们还将开发一种寡核苷酸竞争分析作为一种新的方法，以增加对基因组其余部分的实验基序分析的吞吐量。这个项目中产生的数据将是第一次在碱基对分辨率下对TF结合位点进行系统的功能分析。

DESCRIPTION (provided by applicant): Transcriptional regulation is a highly coordinated process in the human genome. A significant component of transcriptional regulation is the interaction between transcriptional factor proteins (TFs) and cis-regulatory DNA elements. The goal of this project is to computationally predict and experimentally validate DNA sequence motifs that explain promoter function. The results of this project will be direct functional measurements of sequence motifs at base-pair resolution. This will yield extremely valuable information to assess the sensitivity and specificity of algorithms that can be immediately applied to the whole genome. These results will also help to identify the proportion of functionally relevant transcription factor binding events. The three aims of our project are: Aim 1: We will use two machine learning algorithms (support vector machines and random forest) to determine a subset of known transcription factor binding motifs that are the most predictive of promoter activities. Aim 2: We will then use Bayesian networks to select the most predictive motif features. These features are the strengths of the motif using PSSM and the positions of individual sites relative to each other and the transcription start site. Aim 3: We will then perform mutagenesis of the informative positions within the 900 sites identified in Aim 2, and measure their promoter activities by using transient transfection assays. We also plan to test 100 lower ranking sites to determine the sensitivity and specificity of our algorithms. We will also develop an oligo competition assay as a new approach to increase the throughput of experimental motif analysis for the rest of the genome. The data generated in this project will be the first systematic functional analysis of TF binding sites at base-pair resolution.