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 我们对基因组如何功能以及基因组变异如何影响的我们快速发展的理解 疾病的发展和发展推动了我们发展新型诊断和治疗的能力。 基因组数据科学在此过程中起着至关重要的作用，依靠计算工具的可用性 大规模和复杂数据集的统计和视觉分析。不断增长的基因组劳动力依赖 这些工具包括具有广泛专业知识和需求的科学家。实验科学家使用工具 图形用户界面以解释其数据；计算生物学家为临时撰写管道或代码 交互式环境中的分析，软件开发人员构建了复杂的数据门户和其他 基于Web的工具。虽然存在这些受众的大量基因组数据可视化工具，但有一个 缺乏一种统一的方法，该方法将使更多的受众设计和实施自己的互动 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，用于对基因组映射数据的可视化可视化。可视化 使用此语法定义将具有互动性，响应性和可扩展性。这些功能将由 使用Higlass的扩展来渲染可视化。 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 以基因组可视化和视觉分析任务的分类学为指导，这些任务全面描述 目前正在用于基因组数据的交互式可视化空间。语法将支持 使用不同的基因组布局，数据的视觉编码和灵活的配置来创建可视化 多个链接视图。此外，我们将纳入元数据可视化的分类法，用于 例如，表型数据经常与基因组数据相关联。基于 拟议的语法，JavaScript库，Python软件包，R软件包和交互式可视化 编辑将开发。该编辑器将基于Web，并具有用于数据和数据的拖放接口 可视化组件。除了基因组可视化语法外，我们的框架还将包含 基因组可视化推荐系统，可以基于一个 数据集的描述以及用户打算完成的分析任务。这将使小说能够 在没有可视化设计的情况下创建有效的可视化。推荐系统将 还可以为经验丰富的用户加速视觉分析，因为可视化设计可以自动化，并且 定制。推荐系统将通过R和Python包以及 交互式可视化编辑器。除了使用我们建议的语法生产可视化设计外， 该建议系统也可以用于推荐实施特定的现有工具 可视化功能。