Modeling the Dynamics of Genome-Scale Data Across Trees

跨树基因组规模数据的动态建模

基本信息

批准号：
9117563
负责人：
John Anthony Capra
金额：
$ 34.98万
依托单位：
UNIVERSITY OF PITTSBURGH AT PITTSBURGH
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-08-01 至 2018-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9117563
关键词：
Accounting Address Adoption Age Algorithmic Software Algorithms Big Data Binding Biochemical Biological Biological Assay Biological Markers Blood Blood Cells Cancer Biology Cell Differentiation process Cells Cereals ChIP-seq Collection Communities Complex Computer software Computing Methodologies Congenital Abnormality Crete DNA Methylation DNA Modification Process Data Data Set Dependency Development Developmental Biology Diagnostic Disease Ensure Epigenetic Process Event Exhibits Formulation Gene Expression Genes Genetic Transcription Genome Genomic Segment Genomics Goals Human Genome Individual Knowledge Lead Link Maintenance Malignant Neoplasms Markov Chains Methodology Methods Modeling Modification Molecular Molecular Evolution Organ Organism Pattern Phylogenetic Analysis Play Process Production Recording of previous events Regulation Regulator Genes Regulatory Element Research Research Personnel Signal Transduction Staging Statistical Methods Statistical Models Stem cells Structure Techniques Technology Testing Time Tissues Trees Tweens Ursidae Family Validation Work base biochemical model cell type chromatin modification developmental disease discrete data genome-wide genomic data histone modification human tissue open source programs public health relevance sound spatiotemporal tool transcription factor transcriptome sequencing tumor

项目摘要

DESCRIPTION (provided by applicant): The human genome encodes the developmental programs that result in the creation and maintenance of a complex organism with hundreds of tissues and trillions of cells. Precise, cell type specific control of gene expression is crucial t these processes. Transcription factor (TF) binding, DNA methylation, and histone modifications at gene regulatory elements play key roles in regulating RNA expression. However, the interplay be- tween these factors is poorly understood for most human tissues, and disruption of these processes can cause birth defects, cancer, and other disease. Recent advances in experimental technology have resulted in the production of thousands of genome- wide profiles of DNA methylation, histone modifications, and TF binding across hundreds of cellular contexts. These data hold the promise of revealing the dynamic genomic changes that drive proper development, but sound statistical and computational methods for integrating and testing hypotheses about these large, complex, and highly interdependent data are needed. Different cellular contexts are related through their differentiation histories, and the goal of this projectis to develop analysis tools that leverage these dependencies be- tween developmentally related cell types. This will facilitate the identification of significant changes in DNA and chromatin modifications within developing lineages, and it will highlight when and how these modifications impact gene expression. The approaches developed in this project will enable researchers to address the following biomedically important questions: Which DNA and chromatin modifications drive different transitions in a cellular differentiation? Which genomic regions are influenced by these modifications? What genes are influenced by these dynamic regulatory modifications in different lineages? Software will be developed, tested, and validated on several recent detailed characterizations of blood cell differentiation. This work will provide the developmental and cancer biology communities with open-source tools for characterizing the genomic basis of normal and abnormal development. In addition, with erroneous patterns of DNA methylation and histone modification now being used as diagnostic hallmarks for specific cancers, given the right data, this framework may open up avenues towards a better understanding of the biological underpinnings of such biomarkers.

描述（由应用程序提供）：人类基因组编码的发展程序，这些程序会导致创建和维护具有数百个组织和数万亿个细胞的复杂组织。精确的，基因表达的细胞类型特异性控制是这些过程至关重要的。基因调节元件上的转录因子（TF）结合，DNA甲基化和组蛋白修饰在调节RNA表达中起关键作用。但是，对于大多数人体组织而言，这些因素对这些因素的了解很少，而这些过程的破坏会导致出生缺陷，癌症和其他疾病。实验技术的最新进展导致在数百个细胞环境中生产了数千种DNA甲基化，组蛋白修饰和TF结合的全基因组谱。这些数据具有揭示动态基因组变化的希望，这些变化可以推动适当的开发，但是需要进行统计和计算方法，用于整合和测试有关这些大型，复杂且高度相互依存的数据的假设。不同的蜂窝环境通过其分化历史以及该项目的目的相关，以开发分析工具，以利用这些依赖性与开发相关的细胞类型。这将有助于鉴定发育谱系中DNA和染色质修饰的显着变化，并且将突出显示这些修饰何时以及如何影响基因表达。该项目中开发的方法将使研究人员能够解决以下重要的重要问题：哪种DNA和染色质修饰可以在细胞分化中驱动不同的过渡？哪些基因组区域受这些修饰影响？哪些基因受不同谱系中这些动态调节修饰的影响？软件将在几个最近的血细胞分化的详细字符上开发，测试和验证。这项工作将为发育和癌症生物学社区提供开源工具，以表征正常和异常发育的基因组基础。此外，现在将错误的DNA甲基化和组蛋白修饰的模式用作特定癌症的诊断标志，鉴于正确的数据，该框架可能会为更好地理解这种生物标志物的生物学基础开辟途径。