Identification of Transcriptional Factor Binding Sites in Human Promoters

人类启动子中转录因子结合位点的鉴定

• 批准号: 7615950
• 负责人: Zhiping Weng
• 金额: $ 49.56万
• 依托单位: UNIV OF MASSACHUSETTS MED SCH WORCESTER
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-28 至 2010-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7615950
• 关键词: Accounting; Algorithms; Amino Acid Motifs; Antibodies; Base Pairing; Base Sequence; Binding; Binding Sites; Biological Assay; Cell Line; Cells; Chromatin; Classification; Collection; DNA; DNA Sequence; Data; Data Set; Databases; Elements; Event; Genome; Genomics; Goals; Human; Human Genome; Individual; Informatics; Life; Localized; Machine Learning; Measurement; Measures; Methods; Mutagenesis; Mutation; Oligonucleotides; Policies; Positioning Attribute; Process; Promoter Regions; Protein Binding; Proteins; Range; Relative (related person); Reporter; Resolution; Rest; Score; Sensitivity and Specificity; Single Nucleotide Polymorphism; Site; Techniques; Testing; Transcription Initiation Site; Transcriptional Regulation; Transfection; base; chromatin immunoprecipitation; computer based statistical methods; cost; forest; member; novel strategies; promoter; research study; transcription factor

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.

描述（由申请人提供）： 转录调控是人类基因组中高度协调的过程。转录调控的一个重要组成部分是转录因子蛋白（TF）与顺式调控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。该项目的结果将是在碱基对分辨率的序列基序的直接功能测量。这将产生非常有价值的信息，以评估可以立即应用于整个基因组的算法的灵敏度和特异性。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。我们的项目的三个目标是：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）来确定一个子集的已知转录因子结合基序是最预测的启动子活动。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位点的位置。目标3：然后，我们将在目标2中鉴定的900个位点内进行信息位置的诱变，并通过使用瞬时转染测定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，以确定我们算法的灵敏度和特异性。我们还将开发一种寡核苷酸竞争测定法，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通量。本项目中产生的数据将是第一个在碱基对分辨率下对TF结合位点进行系统功能分析的数据。