Integration and visualization of diverse biological data

多种生物数据的整合和可视化

• 批准号: 8041717
• 负责人: OLGA G TROYANSKAYA
• 金额: $ 43.3万
• 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 2014-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8041717
• 关键词: Address; Algorithms; Binding; Bioinformatics; Biological; Biological Markers; Biological Models; Biological Process; Biology; Cardiovascular Diseases; Case Study; Cell Lineage; Cells; Clinical; Collaborations; Communities; Complex; Computer software; Computing Methodologies; Data; Data Analyses; Data Set; Databases; Development; Diabetic Angiopathies; Diagnosis; Disease; Endothelium; Ensure; Event; Feedback; Functional disorder; Funding; Gene Expression; Gene Expression Regulation; Gene Proteins; Generations; Genes; Genome; Genomics; Goals; Gold; Grant; Health; Histocompatibility Testing; Human; Human Biology; Imagery; Individual; Joints; Kidney; Kidney Diseases; Kidney Glomerulus; Lead; Machine Learning; Medicine; Methodology; Methods; Modeling; Molecular; Monitor; Mus; Online Systems; Organism; Participant; Plasma; Progress Reports; Proteins; Publications; Research; Research Personnel; Role; Saccharomyces; Saccharomyces cerevisiae; Sampling; Solutions; Specificity; Speed; Structure; Structure of glomerular mesangium; System; Systems Biology; Systems Integration; Techniques; Technology; Time; Tissues; Update; Urine; Vascular System; Work; Yeasts; base; biological research; biological systems; cell type; complex biological systems; data integration; drug development; functional genomics; gene function; genome database; human data; human disease; human tissue; improved; model organisms databases; novel; parallel processing; podocyte; research study; therapy development; transcriptomics

DESCRIPTION (provided by applicant): Modern genome-scale experimental techniques enable for the first time in biological research the comprehensive monitoring of the entire molecular regulatory events leading to disease. Their integrative analyses hold the promise of generating specific, experimentally testable hypotheses, paving the way for a systems-level molecular view of complex disease. However, systems-level modeling of metazoan biology must address the challenges of: 1. biological complexity, including individual cell lineages and tissue types, 2. the increasingly large scale of data in higher organisms, and 3. the diversity of biomolecules and interaction mechanisms in the cell. The long-term goal of this research is to address these challenges through the development of bioinformatics frameworks for the study of gene function and regulation in complex biological systems thereby contributing to a greater understanding of human disease. In the initial funding period, we have developed accurate methods for integrating and visualizing diverse functional genomics data in S. cerevisiae and implemented them in interactive web-based systems for the biology community. Our methods have led to experimental discoveries of novel biology, are widely used by the yeast community, and are integrated with the SGD model organism database. We now propose to leverage our previous work to develop novel data integration and analysis methods and implement them in a public system for human data. In the proposed research period, we will create algorithms appropriate for integrating metazoan data in a tissue- and cell-lineage specific manner in health and disease. We will also develop novel hierarchical methods for predicting specific molecular interaction mechanisms and will extend our methods for integrating additional biomolecules. These methods will direct experiments focused on the glomerular kidney filter, a critical and complex component of the human vascular system whose dysfunction directly contributes to microvascular disease. Prediction of these cell-lineage specific functional networks will advance the understanding of the glomerulus function and its role in microvascular disease, leading to better clinical predictors, diagnoses, and treatments. From a technical perspective, application to glomerular biology will enable iterative improvement of the proposed methods based on experimental feedback. The end product of this research will be a general, robust, interactive, and automatically updated system for human data integration and analysis that will be freely available to the biomedical community. We will leverage parallel processing technologies (inspired by Google- type cloud computing solutions) to ensure interactive-analysis speed on the system. This system will allow biomedical researchers to synthesize, analyze, and visualize diverse data in human biology, enabling accurate predictions of biological networks and understanding their cell-lineage specificity and role in disease. Such integrative analyses will provide experimentally testable hypotheses, leading to a deeper understanding of complex disorders and paving the way to molecular-defined tissue targeted therapies and drug development. PUBLIC HEALTH RELEVANCE: Our general system will enable integrative analysis of human functional genomics data in a cell-lineage and disease-focused manner, allowing biomedical researchers to identify potential clinical biomarkers and to formulate specific hypotheses elucidating the cause and development of a variety of complex disorders. Our application of this system to generate cell-lineage specific functional networks will lead to a better understanding of the glomerulus function and will directly benefit human health through the development of improved predictors, diagnoses, and treatments for microvascular disease.

描述（由申请人提供）：现代基因组规模的实验技术首次在生物学研究中实现了对导致疾病的整个分子调控事件的全面监测。他们的综合分析有望产生具体的、实验可检验的假设，为复杂疾病的系统水平分子观点铺平道路。然而，系统级建模的后生动物生物学必须解决的挑战：1。生物复杂性，包括单个细胞谱系和组织类型，2.高等生物体中数据的规模越来越大，以及3。细胞内生物分子的多样性和相互作用机制。这项研究的长期目标是通过发展生物信息学框架来应对这些挑战，以研究复杂生物系统中的基因功能和调控，从而有助于更好地了解人类疾病。在最初的资助期间，我们已经开发出准确的方法来整合和可视化S.酿酒和实现它们在交互式的基于网络的系统，为生物界。我们的方法导致了新生物学的实验发现，被酵母界广泛使用，并与SGD模式生物数据库相结合。我们现在建议利用我们以前的工作来开发新的数据集成和分析方法，并在人类数据的公共系统中实现它们。在拟议的研究期间，我们将创建适合于在健康和疾病中以组织和细胞谱系特定方式整合后生动物数据的算法。我们还将开发用于预测特定分子相互作用机制的新型分层方法，并将扩展我们整合其他生物分子的方法。这些方法将指导专注于肾小球肾过滤器的实验，肾小球肾过滤器是人类血管系统的关键和复杂组成部分，其功能障碍直接导致微血管疾病。这些细胞谱系特异性功能网络的预测将促进对肾小球功能及其在微血管疾病中的作用的理解，从而导致更好的临床预测，诊断和治疗。从技术的角度来看，肾小球生物学的应用将使迭代改进的基础上提出的方法的实验反馈。这项研究的最终产品将是一个通用的，强大的，交互式的，自动更新的系统，用于人体数据集成和分析，将免费提供给生物医学界。我们将利用并行处理技术（灵感来自谷歌类型的云计算解决方案），以确保系统上的交互式分析速度。该系统将允许生物医学研究人员合成，分析和可视化人类生物学中的各种数据，从而能够准确预测生物网络并了解其细胞谱系特异性和在疾病中的作用。这种综合分析将提供实验可检验的假设，从而更深入地了解复杂的疾病，并为分子定义的组织靶向治疗和药物开发铺平道路。 公共卫生相关性：我们的通用系统将以细胞谱系和疾病为重点的方式对人类功能基因组学数据进行综合分析，使生物医学研究人员能够识别潜在的临床生物标志物，并制定具体的假设，阐明各种复杂疾病的原因和发展。我们应用该系统来产生细胞谱系特异性功能网络将导致对肾小球功能的更好理解，并将通过开发改进的微血管疾病的预测、诊断和治疗来直接有益于人类健康。