Integration and Visualization of Diverse Biological Data

 描述（由申请人提供）：大多数人类疾病的发作涉及相互作用的基因和途径的复杂系统的多重分子水平变化，这些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。虽然这个系统已经被成千上万的功能基因组学和定量遗传学研究所探索，但仔细提取与这些特定背景相关的信号是一个具有挑战性的问题。这些异质数据的一般整合是检测信号的重要第一步，这些信号可用于构建网络以生成可实验验证的假设。然而，只有通过处理疾病发生在多种背景交叉点的事实，并将功能基因组学与定量遗传学相结合，我们才能对人类病理生理学进行分子水平的理解，这将为新疗法和药物开发铺平道路。 该项目的长期目标是通过开发创新的生物信息学框架，对各种功能基因组数据进行综合分析，从而实现这些发现。在之前的资助期间，我们为最常见的模型生物和人类开发了准确的数据集成和可视化方法，创建了组织特异性数据分析方法，并应用这些方法对重要的生物过程进行了新的见解。我们进一步使实验生物学的发现，通过实施这些方法在公共访问的交互式系统，是流行的实验生物学家。 利用我们以前的工作，我们现在将通过开发新的半监督和多任务机器学习方法，并在能够预测基因组规模的功能和机制的实时集成系统中实现它们，直接解决对人类疾病潜在分子机制进行数据驱动研究的挑战。这将允许任何生物医学研究人员快速做出关于肾小球高血压相关基因的功能，相互作用和调控的数据驱动假设，或预测与帕金森病相关的影响黑质泛素化途径的新的调控相互作用。此外，我们将开发疾病基因发现的方法，利用这些高度特异性的网络进行定量遗传学数据的功能分析。 我们的交付将是一个通用的，强大的，用户友好的，自动更新的系统，用于用户驱动的功能基因组数据集成和定量遗传学数据的功能分析。在这项工作中，我们（与我们密切的实验和临床合作者）还将我们的方法应用于慢性肾脏疾病，心血管疾病/高血压和自闭症谱系障碍，作为我们方法迭代改进的案例研究，并为更好地理解这些疾病做出直接贡献。

 DESCRIPTION (provided by applicant): The onset of most human disease involves multiple, molecular-level changes to the complex system of interacting genes and pathways that function differently in specific cell-lineage, pathway and treatment contexts. While this system has been probed by the thousands of functional genomics and quantitative genetic studies, careful extraction of signals relevant to these specific contexts is a challenging problem. General integration of these heterogeneous data was an important first step in detecting signals that be used to build networks to generate experimentally-testable hypotheses. However, only by dealing with the fact that disease happens at the intersection of multiple contexts and by integrating functional genomics with quantitative genetics will we be able to move toward a molecular-level understanding of human pathophysiology, which will pave the way to new therapy and drug development. The long-term goal of this project is to enable such discoveries through the development of innovative bioinformatics frameworks for integrative analysis of diverse functional genomic data. In the previous funding periods, we developed accurate data integration and visualization methodologies for most common model organisms and human, created methods for tissue-specific data analysis, and applied these methods to make novel insights about important biological processes. We further enabled experimental biological discovery by implementing these methods in publicly accessible interactive systems that are popular with experimental biologists. Leveraging our prior work, we now will directly address the challenge of enabling data-driven study of molecular mechanisms underlying human disease by developing novel semi-supervised and multi-task machine learning approaches and implementing them in a real-time integration system capable of predicting genome-scale functional and mechanism-specific networks focused on any biological context of interest. This will allow any biomedical researcher to quickly make data-driven hypotheses about function, interactions, and regulation of genes involved in hypertension in the kidney glomerulus or to predict new regulatory interactions relevant to Parkinson's disease that affect the ubiquitination pathway in Substantia nigra. Furthermore, we will develop methods for disease gene discovery that leverage these highly specific networks for functional analysis of quantitative genetics data. Our deliverable will be a general, robust, user-friendly, and automatically updated system for user-driven functional genomic data integration and functional analysis of quantitative genetics data. Throughout this work, we (with our close experimental and clinical collaborators) will also apply our methods to chronic kidney disease, cardiovascular disease/hypertension, and autism spectrum disorders both as case studies for the iterative improvement of our methods and to make direct contribution to better understanding of these diseases.