lntegration and Visualization of Diverse Biological Data

多种生物数据的整合和可视化

• 批准号: 9902503
• 负责人: OLGA G TROYANSKAYA
• 金额: $ 44.83万
• 依托单位: PRINCETON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-04-01 至 2023-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9902503
• 关键词: Algorithms; Alzheimer' s Disease; Architecture; Area; Base Sequence; Binding; Biochemical; Biological; Biological Process; Case Study; Cell Lineage; Cells; Chromatin; Chronic Kidney Failure; Collaborations; Communities; Complex; Computing Methodologies; Congenital Heart Defects; DNA; Data; Data Analyses; Deoxyribonucleases; Disease; Enhancers; Exons; Feedback; Functional disorder; Funding; Genes; Genetic Transcription; Genetic study; Genome; Genomics; Goals; Grant; Histones; Hypersensitivity; Immune System Diseases; Immunology; Knowledge; Laboratories; Lead; Letters; Libraries; Link; Measurement; Methods; Modeling; Molecular; Mutation; Neighborhoods; Nephrology; Network-based; Neurobiology; Neurodegenerative Disorders; Online Systems; Pathway Analysis; Pathway interactions; Post-Transcriptional Regulation; Process; Proteins; Quantitative Genetics; RNA; RNA Processing; RNA Splicing; RNA-Binding Proteins; Regulation; Research; Research Personnel; Scientist; System; Time; Tissue-Specific Gene Expression; Tissues; Untranslated RNA; Variant; Visualization; autism spectrum disorder; base; biomedical scientist; causal variant; cell type; congenital heart disorder; crosslinking and immunoprecipitation sequencing; data integration; deep learning; disease phenotype; epigenomics; functional genomics; gene interaction; genetic variant; genome wide association study; genomic variation; high throughput analysis; human disease; improved; in vivo; insight; network models; novel; open source; precision medicine; prediction algorithm; predictive modeling; transcription factor; user-friendly

PROJECT SUMMARY The onset of most human disease involves numerous molecular-level changes to the complex system of interacting genes and pathways that function differently in specific cell-lineage, pathway, and treatment contexts. This system is probed by thousands of functional genomics and quantitative genetic studies, and integrative analysis of these data can generate testable hypotheses identifying causal genetic variants and linking them to network level changes in cells to disease phenotypes. This can enable deeper molecular-level understanding of pathophysiology, paving the way to genome-based precision medicine. The long term goal of this project is to enable such discoveries through integrative analysis of high- throughput biological data in a disease context. In the previous funding periods, we developed accurate data integration methods, created algorithms for the prediction of disease genes through context-specific and mechanistic network models and analysis of quantitative genetics data, and made novel insights into important biological processes and diseases. We further enabled experimental biological discovery by building public interactive systems capable of real-time user-driven integration that are popular among experimental biologists. We now propose to connect these gene-level functional network approaches with the underlying genomic variation by deciphering how genomic variants lead to specific transcriptional and posttranscriptional effects. We propose to develop ab initio sequence-level models capable of predicting biochemical effects of any genomic variant (including rare or never observed) on chromatin state and RNA regulation, then link these effects with gene-level regulatory consequences (including tissue-specific transcription and RNA splicing), and finally put genomic sequence directly into the network context via a statistical approach for detecting genes and network neighborhoods with a significantly elevated mutational burden in disease. Our key deliverable will be a user- friendly, interactive web-based framework enabling systems-level variant impact analysis in a network context and an open source library for computational scientists. In addition to systematic analysis across contexts and diseases, we will collaborate with experimentalists to apply our methods to Alzheimer’s, autism spectrum disorders, chronic kidney disease, immune diseases, and congenital heart defects as case studies for the iterative improvement of our methods and to directly contribute to better understanding of these diseases.

项目摘要 大多数人类疾病的发作涉及对复杂系统的许多分子级变化 相互作用的基因和途径在特定的细胞细胞，途径和治疗环境中的作用不同。 该系统由数千种功能基因组学和定量遗传研究探测，并整合 对这些数据的分析可以产生可检验的假设，以识别因果遗传变异，并将其链接到 网络水平的细胞变化为疾病表型。这可以使对分子级的更深层次的理解 病理生理学，铺平了基于基因组的精确医学的道路。 该项目的长期目标是通过对高级的整合分析来实现此类发现 疾病中的吞吐量生物数据。在前面的资金期间，我们开发了准确的数据 整合方法，创建了通过上下文特异性和 机械网络模型和定量遗传学数据的分析，并对重要的见解进行了新的见解 生物过程和疾病。我们通过建立公众进一步启用了实验生物学发现 在实验生物学家中流行的实时用户驱动集成的交互式系统。 现在，我们建议将这些基因级功能网络方法与潜在的基因组联系起来 通过解读基因组变异的变化如何导致特定的转录和转录后效应。我们 提议开发从头算序列级模型，能够预测任何基因组的生化效应 在染色质状态和RNA调控上的变体（包括罕见或从未观察到），然后将这些效果与 基因级调节后果（包括组织特异性转录和RNA剪接），最后放置 基因组序列通过检测基因和网络的统计方法直接进入网络环境 疾病突变烧伤的邻里。我们的关键可交付方式将是用户 - 友好的，互动的基于网络的框架使系统级别的变体影响分析在网络环境中 以及用于计算科学家的开源库。除了跨环境和 疾病，我们将与实验者合作，将我们的方法应用于阿尔茨海默氏症的自闭症谱系 疾病，慢性肾脏疾病，免疫疾病和先天性心脏缺陷作为案例研究 迭代的改进方法，直接有助于更好地理解这些疾病。