Integration and visualization of diverse biological data

多种生物数据的整合和可视化

• 批准号: 8209212
• 负责人: OLGA G TROYANSKAYA
• 金额: $ 39.32万
• 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/8209212
• 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; public health relevance; 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启用了对整个分子调节事件的全面监测，导致疾病。他们的综合分析有望产生特定的实验测试假设，为复杂疾病的系统级分子视图铺平了道路。但是，新生生物学的系统级建模必须应对以下挑战：1。生物复杂性，包括单个细胞谱系和组织类型，2。较高生物体中越来越大的数据和3。生物分子的多样性和细胞中的相互作用机制。这项研究的长期目标是通过开发复杂生物系统中基因功能和调节的生物信息学框架来应对这些挑战，从而有助于对人类疾病有更多了解。在最初的资金期间，我们开发了精确的方法，用于在酿酒酵母中整合和可视化多样化的功能基因组学数据，并在基于Interactive Web的系统中为生物学社区实施它们。我们的方法导致了新型生物学的实验发现，被酵母社区广泛使用，并与SGD模型有机体数据库集成在一起。现在，我们建议利用我们以前的工作来开发新颖的数据集成和分析方法，并在人类数据的公共系统中实施它们。在拟议的研究期间，我们将创建适合于健康和疾病中特定于组织和细胞限制的特定方式集成后生数据的算法。我们还将开发新的分层方法来预测特定的分子相互作用机制，并将扩展我们整合其他生物分子的方法。这些方法将指导肾小球肾过滤器的实验，肾小球肾脏是人血管系统的关键和复杂成分，其功能障碍直接导致微血管疾病。这些细胞限制特定功能网络的预测将提高对肾小球功能及其在微血管疾病中的作用的理解，从而提供更好的临床预测因子，诊断和治疗。从技术角度来看，肾小球生物学的应用将基于实验反馈的迭代改进。这项研究的最终产品将是一个通用，健壮，互动且自动更新的人类数据集成和分析系统，该系统将免费提供给生物医学界。我们将利用并行处理技术（受Google-Type Cloud Computing解决方案的启发）来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，实现对生物网络的准确预测，并了解其细胞训练的特异性和在疾病中的作用。这种综合分析将提供实验检验的假设，从而更深入地了解复杂疾病，并为分子定义的组织靶向疗法和药物发育铺平道路。 公共卫生相关性：我们的通用系统将以细胞和以疾病为中心的方式对人类功能基因组学数据进行综合分析，从而使生物医学研究人员能够识别潜在的临床生物标志物，并制定特定的假设，以阐明各种复杂疾病的原因和发展。我们对该系统的应用来生成细胞限制特定功能网络将使人们更好地了解肾小球功能，并通过开发改善微血管疾病的预测因素，诊断和治疗方法直接使人类健康受益。