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生都涉及到复杂的免疫系统的许多分子水平的变化。 相互作用的基因和途径，在特定的细胞谱系，途径和治疗环境中发挥不同的功能。 这个系统是由数以千计的功能基因组学和定量遗传学研究探索的， 对这些数据的分析可以产生可验证的假设，识别因果遗传变异，并将它们与 细胞网络水平的变化与疾病表型的关系。这可以使人们更深入地了解分子水平， 病理生理学，为基于基因组的精准医学铺平道路。 该项目的长期目标是通过对高- 在疾病背景下的生物数据吞吐量。在之前的融资期间，我们开发了准确的数据， 集成方法，创建了通过特定环境和 机制网络模型和定量遗传学数据的分析，并提出了新的见解，重要的 生物过程和疾病。我们进一步通过建立公共的生物学发现实验， 交互式系统能够实时用户驱动的集成，是流行的实验生物学家。 我们现在建议将这些基因水平的功能网络方法与潜在的基因组 通过破译基因组变异如何导致特定的转录和转录后效应来研究变异。我们 建议开发从头序列水平模型，能够预测任何基因组的生化效应， 变异（包括罕见或从未观察到的）对染色质状态和RNA调控的影响，然后将这些影响与 基因水平的调控后果（包括组织特异性转录和RNA剪接），最后把 通过统计方法检测基因和网络， 疾病突变负担显著升高的社区。我们的关键交付成果将是用户- 一个友好的、交互式的基于网络的框架，能够在网络环境中进行系统级的变异影响分析 和一个面向计算科学家的开源库。除了跨背景的系统分析， 我们将与实验学家合作，将我们的方法应用于阿尔茨海默氏症，自闭症谱系， 疾病，慢性肾脏疾病，免疫性疾病和先天性心脏缺陷作为案例研究， 迭代改进我们的方法，并直接有助于更好地了解这些疾病。