Grammar-Driven Genomic Data Visualization

语法驱动的基因组数据可视化

• 批准号: 10452031
• 负责人: Nils Gehlenborg
• 金额: $ 60.72万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-15 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10452031
• 关键词: Address; Basic Science; Biological Assay; Cells; Clinical; Code; Communication; Communities; Complement; Complex; Computer software; Custom; Data; Data Analyses; Data Science; Data Set; Development; Diagnostic; Disease; Disease Progression; Drops; Ensure; Experimental Designs; Felis catus; Future; Genome; Genomic medicine; Genomics; Health; Human; Knowledge; Libraries; Link; Maps; Metadata; Online Systems; Play; Population; Process; Pythons; Recommendation; Research; Research Design; Research Personnel; Role; Scientist; Software Engineering; System; Taxonomy; Techniques; Therapeutic; Training; Visual; Visualization; Visualization software; Work; Writing; base; collaborative environment; complex data; computerized tools; data portal; data visualization; design; experience; flexibility; genomic data; genomic tools; genomic variation; graphical user interface; improved; innovation; insight; multiscale data; novel; novel diagnostics; novel therapeutics; phenotypic data; skills; success; task analysis; tool; web-based tool

Project Summary Our rapidly evolving understanding of how genomes function and how genomic variation influences the development and progression of diseases drive our ability to develop novel diagnostics and therapeutics. Genomic data science plays a critical role in this process, relying on the availability of computational tools for statistical and visual analysis of large-scale and complex data sets. The growing genomics workforce that relies on these tools includes scientists with a broad set of expertise and needs. Experimental scientists use tools with graphical user interfaces to interpret their data; computational biologists write pipelines or code for ad hoc analysis in interactive environments, and software developers build sophisticated data portals and other web-based tools. While a large number of genomic data visualization tools for these audiences exist, there is a lack of a unified approach that would allow a larger audience to design and implement their own interactive data visualization tools for genomic data. To address this gap, we will develop a visualization framework based on a novel grammar for interactive, scalable visualization of genome-mapped data. The visualizations defined using this grammar will be interactive, responsive, and scalable. These features will be enabled by rendering the visualizations using an extension of HiGlass. HiGlass is our framework for genomic data visualization that supports multi-scale data visualization, and multiple linked views. The grammar design will be guided by a taxonomy of genomic visualizations and visual analysis tasks that comprehensively describe the space of interactive visualizations currently in use for genomic data. The grammar will support the creation of visualizations with different genome layouts, visual encodings of data, and flexible configurations of multiple linked views. Furthermore, we will incorporate a taxonomy of metadata visualizations, for example, of phenotypic data, that are frequently linked to genomic data. To create visualizations based on the proposed grammar, a JavaScript library, a Python package, an R package, and an interactive visualization editor will be developed. This editor will be web-based and have a drag-and-drop interface for data and visualization components. In addition to the genomic visualization grammar, our framework will also contain a genomic visualization recommendation system that can generate interactive visualizations based on a description of a data set and the analysis tasks that the user intends to accomplish. This will enable novices to create effective visualizations without knowledge of visualization design. The recommendation system will also accelerate visual analysis for more experienced users, as the visualization design can be automated and customized. The recommendation system will be available through the R and Python packages and the interactive visualization editor. In addition to producing visualization designs using our proposed grammar, this recommendation system can also be used to recommend existing tools that implement specific visualization capabilities.

项目摘要 我们对基因组如何发挥作用以及基因组变异如何影响基因组功能的快速理解， 疾病的发展和进展驱动我们开发新的诊断和治疗方法的能力。 基因组数据科学在这一过程中发挥着关键作用，依赖于计算工具的可用性， 大规模和复杂数据集的统计和可视化分析。不断增长的基因组学工作人员， 在这些工具上，包括具有广泛专门知识和需求的科学家。实验科学家使用工具， 图形用户界面来解释他们的数据;计算生物学家编写管道或代码， 交互式环境中的分析，软件开发人员构建复杂的数据门户和其他 基于Web的工具虽然存在大量针对这些受众的基因组数据可视化工具，但 缺乏一个统一的艾德方法，允许更多的受众设计和实现他们自己的互动 用于基因组数据的数据可视化工具。为了解决这一差距，我们将开发一个可视化框架 基于一种新的语法，用于基因组映射数据的交互式、可扩展的可视化。可视化 使用这种语法定义的应用程序将是交互式的、响应式的和可扩展的。这些功能将通过 使用HiGlass的扩展渲染可视化。HiGlass是我们的基因组数据框架 可视化，支持多尺度数据可视化和多个链接视图。语法设计将 以基因组可视化和可视化分析任务的分类为指导， 目前用于基因组数据的交互式可视化空间。语法将支持 创建具有不同基因组布局、数据可视化编码和灵活配置的可视化 多个链接的视图。此外，我们将合并元数据可视化的分类， 例如，表型数据，经常与基因组数据相关联。要基于 建议的语法、JavaScript库、Python包、R包和交互式可视化 编辑将被开发。这个编辑器将是基于网络的，并有一个拖放界面的数据和 可视化组件。除了基因组可视化语法，我们的框架还将包含 一种基因组可视化推荐系统，其可以基于 数据集和用户打算完成的分析任务的描述。这将使新手能够 创建有效的可视化，而无需可视化设计的知识。推荐系统将 还可以为更有经验的用户加速可视化分析，因为可视化设计可以自动化， 定制的推荐系统将通过R和Python包以及 交互式可视化编辑器。除了使用我们提出的语法生成可视化设计之外， 此推荐系统还可用于推荐实现特定 可视化能力。