Integration and visualization of diverse biological data

描述（申请人提供）：现代基因组尺度实验技术使生物学研究首次能够全面监测导致疾病的整个分子调控事件。他们的综合分析有望产生具体的、实验上可测试的假设，为复杂疾病的系统级分子观点铺平道路。然而，后生动物生物学的系统级建模必须解决以下挑战：生物复杂性，包括个体细胞谱系和组织类型；2 .高等生物的数据规模越来越大；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开发生物信息学框架来解决这些挑战，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。在最初的资助阶段，我们已经开发了准确的方法来整合和可视化酿酒酵母的各种功能基因组学数据，并将其应用于生物界的交互式网络系统中。我们的方法已经导致了新的生物学实验发现，被酵母群落广泛使用，并与SGD模式生物数据库集成。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施它们。在拟议的研究期间，我们将创建适合于以健康和疾病的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。这些方法将指导实验集中在肾小球肾滤过器上，肾小球肾滤过器是人体血管系统的一个重要而复杂的组成部分，其功能障碍直接导致微血管疾病。对这些细胞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。从技术角度来看，应用于肾小球生物学将使所提出的方法基于实验反馈进行迭代改进。这项研究的最终产品将是一个通用的、健壮的、交互式的、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（受谷歌型云计算解决方案的启发）来确保系统上的交互分析速度。该系统将使生物医学研究人员能够综合、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，并了解它们的细胞系特异性和疾病中的作用。这种综合分析将提供实验上可测试的假设，导致对复杂疾病的更深层次的理解，并为分子定义的组织靶向治疗和药物开发铺平道路。

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.