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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：
9306885
负责人：
John Anthony Capra
金额：
$ 35.15万
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 2019-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9306885
关键词：
Address Adoption Age Algorithmic Analysis Algorithmic Software Algorithms Awareness 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 DNA Methylation DNA Modification Process Data Data Set Dependency Development Developmental Biology Diagnostic Disease Ensure Epigenetic Process Event Exhibits Formulation Gene Expression Genes Genetic Fingerprintings 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 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 overexpression 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（由申请人提供）：人类基因组编码了发育程序，这些程序导致了具有数百个组织和数万亿个细胞的复杂生物体的创建和维持。精确的、细胞类型特异性的基因表达控制对于这些过程至关重要。基因调控元件上的转录因子 (TF) 结合、DNA 甲基化和组蛋白修饰在调节 RNA 表达中发挥着关键作用。然而，对于大多数人体组织来说，这些因素之间的相互作用却知之甚少，这些过程的破坏可能导致出生缺陷、癌症和其他疾病。实验技术的最新进展已经产生了跨数百个细胞环境的数千个全基因组 DNA 甲基化、组蛋白修饰和 TF 结合图谱。这些数据有望揭示驱动适当发育的动态基因组变化，但需要健全的统计和计算方法来整合和测试有关这些大型、复杂和高度相互依赖的数据的假设。不同的细胞环境通过其分化历史相互关联，该项目的目标是开发利用发育相关细胞类型之间的依赖性的分析工具。这将有助于识别发育中的谱系中 DNA 和染色质修饰的显着变化，并将突出显示这些修饰何时以及如何影响基因表达。该项目开发的方法将使研究人员能够解决以下生物医学上的重要问题：哪些 DNA 和染色质修饰驱动细胞分化中的不同转变？哪些基因组区域受到这些修饰的影响？不同谱系中哪些基因受到这些动态调控修饰的影响？软件将根据最近血细胞分化的几个详细特征进行开发、测试和验证。这项工作将为发育和癌症生物学界提供开源工具，用于表征正常和异常发育的基因组基础。此外，由于 DNA 甲基化和组蛋白修饰的错误模式现在被用作特定癌症的诊断标志，如果有正确的数据，该框架可能会为更好地理解此类生物标志物的生物学基础开辟途径。