Modeling in vivo Protein-DNA Interactions from High-Throughput Data MP1/1

描述（由申请人提供）： 基因表达的控制是任何细胞生命中最基本的过程，它主要由转录因子（DMA结合调节蛋白）介导（在单基因水平）。据报道，DMA的目标识别在体内有时不同于在体外为基础的模型。 理解在细胞环境中控制特定DMA识别的机制将深刻地增强我们对转录因子功能机制的理解，并且也将在生物医学研究中产生重大影响。此外，很明显，需要开发专门用于高通量蛋白质-DNA体内相互作用数据的新的基序发现算法。 该工作的近期目标是开发有效分析高通量体内蛋白质-DNA关联数据（如ChIP芯片）的方法和工具，并识别生物学上重要的顺式调控元件。更远的目标是了解控制转录因子与其基因组DNA靶点相互作用的规则。拟议的活动的目的，首先是开发这样一个新的基序寻找软件，扩大和测试各种方法和战略。测试将基于人工和“真实的”数据，并将评估各种方法的优缺点。最好的方法将用于分析现有和新的ChIP芯片数据，并预测顺式调控基序，随后将用生物化学方法进行确认。示例转录因子将用于研究特定顺式调控模块对基因表达的影响，其目标是开发允许构建基因调控的完整计算模型的方法。最后，将围绕我们将产生的工具和数据开发一个数据库和网络界面，以便有效地传播、分析和挖掘数据。 为了实现这些目标，需要生物化学实验和计算算法开发的组合。染色质免疫沉淀实验将与启动子微阵列杂交（ChIP-芯片）结合，以鉴定原代肺细胞中TGF β诱导的转录因子的可能靶点。将对数据进行统计分析，以推断转录因子结合的适当定量模型。还将对公开获得的和新生成的基因表达数据进行统计分析，以评估某些顺式调控模块在下游基因表达中的作用。

DESCRIPTION (provided by applicant): The control of gene expression is the most fundamental process in the life of any cell and it is primarily mediated (at the single gene level) by transcription factors, the DMA-binding regulatory proteins. It has been reported that the DMA target recognition in vivo sometimes differs from the in vitro-based models. Understanding the mechanisms that govern the specific DMA recognition in a cellular environment will profoundly augment our understanding of the mechanisms of transcription factor function and will also have a major impact in biomedical research. Furthermore, it becomes apparent that new motif finding algorithms need to be developed that specifically for high-throughput protein-DNA in vivo interaction data. The immediate goal of the proposed work is to develop the methodologies and tools to efficiently analyze high-throughput in vivo protein-DNA association data (like ChIP on chip) and identify the biologically important cis-regulatory elements. The more distant goal is to understand the rules that govern the interactions of transcription factors with their genomic DMA targets. The proposed activity aims, initially, to develop such a new motif finding software by expanding and testing various methods and strategies. Tests will be based on artificial and "real" data and the strengths and weaknesses of the various methods will be assessed. The best performing methods will be used to analyze existing and new ChIP on chip data, and predict the cis-regulatory motifs, which they will be subsequently confirmed with biochemical methods. Example transcription factors will be used to study the effect of particular cis-regulatory modules on gene expression with a goal of developing the methodology that will allow for complete computational models of gene regulation to be built. Finally, a database and web-interface will be developed on and around the tools and the data we will produce that ill allow for efficient data dissemination, analysis and mining. To accomplish these goals a combination of biochemical experimentation and computational algorithmic development is needed. Chromatin immunoprecipitation experiments will be coupled with promoter microarray hybridization (ChlP-on-chip) to identify possible targets for TGFbetal-induced transcription factors in primary lung cells. The data will be analyzed statistically to infer the appropriate quantitative models of the transcription factor binding. Publicly available and newly generated gene expression data will also be analyzed statistically to assess the effect of certain cis-regulatory modules in the expression of the downstream genes.