Computational Modeling of Mammalian Promoters

哺乳动物启动子的计算模型

基本信息

批准号：
8088440
负责人：
MICHAEL Q ZHANG
金额：
$ 64.35万
依托单位：
UNIVERSITY OF TEXAS DALLAS
依托单位国家：
美国
项目类别：
财政年份：
2010
资助国家：
美国
起止时间：
2010-04-01 至 2013-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8088440
关键词：
Adipocytes Architecture Area Brain Case Study Cells Characteristics Chromatin Chromatin Structure Classification Code Collaborations Computer Simulation Coupled CpG Islands Data Detection Development Diagnostic Disease Drug Design Elements Enhancers Epigenetic Process Evolution Funding Gene Expression Gene Expression Regulation Genes Genetic Genetic Transcription Genome Genomics Goals Grant Heart Human Human Genome Information Dissemination Intervention Studies Kidney Laboratories Liver Location Lung Malignant Neoplasms Mammals Maps Messenger RNA Modeling Molecular Mus Nature Ontology Pathway interactions Pattern Positioning Attribute Property Proteins Protocols documentation RNA library Race Regulation Regulatory Element Relative (related person)Reporting Research Sampling Scientist Signal Transduction Staging System T-Lymphocyte Techniques Technology Testing Time TimeLine Tissue Sample Tissues Training Transcript Transcriptional Regulation Validation Work biological systems cell type embryonic stem cell epigenomics forest functional group improved insight knowledge of results macrophage mathematical model novel predictive modeling programs promoter response tool

项目摘要

DESCRIPTION (provided by applicant): The long-term goal is to understand how human gene transcription is controlled and regulated. The hypothesis is that such an understanding may be achieved by developing mathematical models that are predictive of promoter position and tissue-specific activity by using local genetic and epigenetic information. Recently, large- scale experimental technologies have mapped a great number of active promoters in a genome and while powerful, their rates of false positives (due to aberrant, likely nonfunctional mRNA transcripts), false negatives (due to incomplete sampling of tissues and developmental stages), and other errors (due to protocol biases) remain uncertain. Consequently, it is important to have additional approaches that incorporate more comprehensive or stringent criteria, and to examine sequence characteristics that, in addition to illuminating molecular mechanisms, may permit computational prediction and direct experimental detection of additional promoters. Even when all human promoters are mapped, merely documenting their positions will not tell us how they are recognized and deployed for transcription. Therefore, as more experimental mapping data become available, the more essential it becomes to develop mathematical models to understand promoter architecture, function and evolution. Now with the complete sequencing of the human genome and localization of almost all of the protein coding genes, understanding how each of these genes are controlled and regulated has become a major challenge in the genome research. Since a gene can often produce multiple transcripts through alternative promoter usage in different cells, at different developmental stages and/or in response to different signals, understanding key elements that define and regulate alternative promoters will be a crucial task before more comprehensive gene regulation networks can be constructed Powered by the ENCODE project, new high throughput genomics technologies for attacking such problems are being developed at a rapid pace. Advanced computational approaches coupled with experimental validations are essential for the ultimate understanding of the regulatory mechanisms of gene expression. The new specific aims are: A1. Extract, compare and classify tissue-specific promoters in mammals so that they may be grouped into different (not necessarily mutually exclusive) expressional and/or epigenetical classes; A2. Identify cis-regulatory motifs/modules as promoter architecture features and their relation to tissue-specific chromatin and expression patterns; A3. Build mathematical models for tissue-specific promoter and expression predictions; A4. Conduct case studies in real regulation pathways in selected tissues. The proposed research will combine experimental and computational approaches and technologies in order to better understand mammalian promoters in terms of genetic and epigenetic cis-regulatory codes. Such models are likely to offer new insights into mechanisms of gene regulation or mis-regulation, and will generate many hypotheses for further functional studies on global regulation of gene expression.

描述（由申请人提供）：长期目标是了解人类基因转录是如何控制和调节的。假设是，这种理解可以通过开发数学模型来实现，该模型利用局部遗传和表观遗传信息来预测启动子位置和组织特异性活性。最近，大规模实验技术已经在基因组中绘制了大量的活性启动子，虽然功能强大，但它们的假阳性率（由于异常的、可能无功能的 mRNA 转录本）、假阴性率（由于组织和发育阶段的采样不完整）），而其他错误（由于协议偏差）仍然不确定。因此，重要的是要有包含更全面或更严格标准的其他方法，并检查序列特征，除了阐明分子机制之外，还可以允许对其他启动子进行计算预测和直接实验检测。即使所有人类启动子都被定位，仅仅记录它们的位置并不能告诉我们它们是如何被识别和部署用于转录的。因此，随着越来越多的实验图谱数据的出现，开发数学模型来理解启动子的结构、功能和进化就变得更加重要。现在，随着人类基因组的完整测序和几乎所有蛋白质编码基因的定位，了解每个基因是如何控制和调节的已成为基因组研究的重大挑战。由于一个基因通常可以通过在不同细胞、不同发育阶段和/或响应不同信号时使用替代启动子产生多个转录本，因此在更全面的基因调控网络能够建立之前，了解定义和调节替代启动子的关键元件将是一项至关重要的任务。在 ENCODE 项目的支持下，用于解决此类问题的新的高通量基因组学技术正在快速开发。先进的计算方法与实验验证相结合对于最终理解基因表达的调控机制至关重要。新的具体目标是：A1。提取、比较和分类哺乳动物中的组织特异性启动子，以便将它们分为不同的（不一定相互排斥的）表达和/或表观遗传类别； A2。识别顺式调控基序/模块作为启动子结构特征及其与组织特异性染色质和表达模式的关系； A3。建立组织特异性启动子和表达预测的数学模型； A4。对选定组织的真实调节途径进行案例研究。拟议的研究将结合实验和计算方法和技术，以便更好地了解哺乳动物启动子的遗传和表观遗传顺式调控密码。这些模型可能为基因调控或错误调控机制提供新的见解，并将为基因表达全局调控的进一步功能研究产生许多假设。