Locating Regulatory Elements in Genomes

定位基因组中的调控元件

基本信息

批准号：
7392219
负责人：
ANIRVAN M SENGUPTA
金额：
$ 29.43万
依托单位：
RUTGERS, THE STATE UNIV OF N.J.
依托单位国家：
美国
项目类别：
财政年份：
2006
资助国家：
美国
起止时间：
2006-04-13 至 2011-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7392219
关键词：
Affinity Algorithms Attention Bacteriophages Binding Binding Sites Biogenesis Bioinformatics Biological Assay Biology Class Complement Complex Computer Analysis DNA Data Data Analyses Detection Development Diabetes Mellitus Disease Early identification Electrophoretic Mobility Shift Assay Escherichia coli Evolution Gene Amplification Gene Expression Gene Expression Regulation Gene Targeting Genes Genome Goals Immunity In Vitro Inflammation Learning Machine Learning Malignant Neoplasms Mating Types Methods Modeling Mutation Organism Pathway interactions Phylogenetic Analysis Play Probability Protein Overexpression Proteins Regulatory Element Regulon Relative (related person)Research Research Personnel Rest Ribosomes Role Saccharomyces cerevisiae Score Sequence Analysis Shock Sigma Factor Site Source Specificity Symptoms Testing Work base beta-Galactosidase chromatin immunoprecipitation combinatorial improved in vivo programs promoter research study response transcription factor

项目摘要

DESCRIPTION (provided by applicant): Understanding regulatory networks controlling gene expression is one of the fundamental problems of modern biology. The proposed research focuses on the methods of locating regulatory elements in DNA. We have developed a new maximum likelihood method based on the physical DNA dependent binding probability of a transcription factor (TF) that correctly incorporates the protein concentration dependent saturation effect. The advantage of keeping the saturation effect is that the method automatically provides a score threshold for classifying candidate sites into binders and non-binders. Most conventional methods, based on the information score, merely provide a relative ordering of candidate sequences. The principled choice of a threshold is extremely useful for dealing with the highly variable sites typical of global regulatory factors. The simplest of our algorithms reduces to a one-class support vector machine. This classifier will be applied to detect large regulons in E. coli, as well as in phages, with special attention to targets of sigma factors. We also develop classifiers for regulatory targets that go beyond pure sequence analysis and combine it with information from additional sources, like microarray expression data or sequence similarity between phylogenetically closely related species. The proposed computational effort will be complemented by experiments verifying the predictions as well as providing in vitro and in vivo data needed to make predictions. Experimental efforts will involve a high throughput low stringency SELEX method applied to global transcriptional regulators from E. coli. It will also involve chromatin immunoprecipitation and beta- galactosidase assays performed in S. Cerevisiae that test the ability of bioinformatic algorithms to predict functionality of TF binding sites. A special feature of this proposal is the analysis of the effect of the rest of the promoter on the regulatory potential of a site. We apply the lessons learnt in simple organisms to the elucidation of distinctive specificity of different NFkB proteins involved in immunity, inflammation and cancer. Mutation, over-expression and amplification of genes encoding transcription factors play an important role in many diseases from diabetes to cancer. Understanding how a factor targets genes is crucial for discovering the pathways whose malfunction leads to the symptoms. This is an achievable goal, given the right way to analyze the plethora of genome-wide data available to us.

描述（由申请人提供）：了解控制基因表达的监管网络是现代生物学的基本问题之一。拟议的研究重点是在DNA中定位调节元件的方法。我们已经基于转录因子（TF）的物理DNA依赖性结合概率（TF）开发了一种新的最大似然方法，该方法正确纳入了蛋白质浓度依赖性饱和效应。保持饱和效应的优点是该方法自动提供了将候选位点分类为粘合剂和非限制者的得分阈值。大多数常规方法，基于信息分数，仅提供候选序列的相对顺序。原则上选择阈值对于处理典型的全球监管因素的高度可变站点非常有用。我们最简单的算法减少到一级支持向量机。该分类器将用于检测大肠杆菌和噬菌体中的大型调节，并特别注意Sigma因子的靶标。我们还开发了用于监管目标的分类器，这些分类器超出了纯序列分析，并将其与其他来源的信息相结合，例如微阵列表达数据或系统发育密切相关的物种之间的序列相似性。提出的计算工作将通过验证预测以及提供预测所需的体外和体内数据的实验来补充。实验努力将涉及高吞吐量低严格的SELEX方法，该方法应用于大肠杆菌的全局转录调节剂。它还将涉及在酿酒酵母中进行的染色质免疫沉淀和β-半乳糖苷酶测定，以测试生物信息学算法预测TF结合位点功能的能力。该提案的一个特征是分析其余启动子对场地调节潜力的影响。我们将在简单生物体中学到的经验教训应用于涉及免疫，炎症和癌症的不同NFKB蛋白的独特特异性。编码转录因子的基因的突变，过表达和扩增在从糖尿病到癌症的许多疾病中起重要作用。了解因子靶向基因的目标对于发现故障导致症状的途径至关重要。这是一个可实现的目标，鉴于正确的方法可以分析我们可获得的大量全基因组数据。