The Variant Explorer: a cloud-based data integration and visualization system for improving clinical interpretation of sequenced genetic variants.

Variant Explorer：基于云的数据集成和可视化系统，用于改进测序遗传变异的临床解释。

• 批准号: 9045271
• 负责人: Xing Xu
• 金额: $ 22.32万
• 依托单位: SOLVE, INC.
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-06-01 至 2016-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9045271
• 关键词: Adoption; Algorithms; Area; BRCA2 gene; Base Sequence; Bioinformatics; Cancer Patient; Classification; Clinic; Clinical; Complex; Computer software; Data; Data Analyses; Data Set; Databases; Development; Diagnosis; Diagnostic; Discipline; Dreams; Gene Mutation; Genes; Genetic; Genetic Variation; Genetic screening method; Genomics; Goals; Hereditary Disease; Hospitals; Hour; Human; Human Genome; Imagery; Individual; Knowledge; Literature; Manuals; Marketing; Medical; Mind; Modeling; Mutation; Outcome; Patients; Phase; Provider; Rare Diseases; Reporting; Research; Research Infrastructure; Research Personnel; Sequence Analysis; Small Business Innovation Research Grant; Specific qualifier value; Speed; System; Technology; Time; Variant; Work; base; clinical decision-making; clinical practice; clinically significant; cloud based; commercial application; cost; cost efficient; data integration; data visualization; design; exome; exome sequencing; experience; genetic disorder diagnosis; genetic variant; graphical user interface; improved; innovation; interest; next generation sequencing; precision medicine; prevent; public health relevance; satisfaction; software systems; stem; technological innovation; tool; tumor; usability; user-friendly

 DESCRIPTION (provided by applicant): The goal of this proposal is to dramatically improve speed and accuracy in clinical decision-making for patients requiring a genetic diagnosis. Next generation sequencing (NGS) technology has revolutionized the clinical practice of diagnosing rare genetic diseases and has the potential to become standard practice across many medical disciplines. The single most intractable challenge is that current practices for the analysis of sequenced genetic variants require an exorbitant amount of manual analysis by skilled biomedical professionals; this human time requirement is incapable of scaling with NGS volume. The proposed product, SolveBio's Variant Explorer (VE), is a cloud- based graphical software system that assists in variant interpretation, or the ascertaining of the clinical significance of sequenced genetic variant. The VE aims to alleviate the analysis problem by guiding and removing manual analysis steps. The technical innovation lies in SolveBio's proprietary data infrastructure and a prioritization on usability. SolveBio's core technology is a programmatic and scalable data pipeline that performs the parsing, normalizing, and versioning of genomic reference data. SolveBio also prioritizes user interaction and experience, a focus that is commonly lacking in bioinformatics tools. The long-term goal of this project is to exponentially improve speed, accuracy, and efficiency in genetic variant analysis so that patients suffering from genetic diseases suitable for NGS-based analyses receive timely, cost-efficient, and accurate results. Our Phase I hypothesis is that SolveBio's reference data infrastructure and user-oriented design will systematically reduce and streamline the manual analysis steps in variant interpretation. Our aims are to algorithmically bring together all known and possible notations for a specific variant and to build and design a relevant literature collation and rankin system. Our Phase II objectives will consist of building out the VE into a modular and complete data analysis solution with a variant classifier capable of assigning a preliminary clinical significance to each variant. NGS-based diagnostics are projected to grow tenfold in market size over the next 5-10 years. SolveBio's Variant Explorer will help unclog the analysis bottleneck and pave the way for widespread adoption of NGS-based technology and the realization of precision medicine.

 描述(由申请人提供)：这项建议的目标是为需要基因诊断的患者显著提高临床决策的速度和准确性。下一代测序(NGS)技术已经彻底改变了诊断罕见遗传病的临床实践，并有可能成为许多医学学科的标准实践。一个最棘手的挑战是，目前对已测序基因变异的分析实践需要熟练的生物医学专业人员进行过多的人工分析；这种人力时间需求无法与NGS的数量相适应。建议的产品SolveBio的Variant Explorer(VE)是一个基于云的图形软件系统，它帮助解释变异，或确定已测序基因变异的临床意义。VE旨在通过指导和删除手动分析步骤来缓解分析问题。SolveBio的技术创新在于其专有的数据基础设施和对可用性的优先考虑。SolveBio的核心技术是一个可编程的、可伸缩的数据管道，它执行基因组参考数据的解析、标准化和版本化。SolveBio还优先考虑用户交互和体验，这是生物信息学工具通常缺乏的重点。该项目的长期目标是成倍提高基因变异分析的速度、准确性和效率，以便患有适合于基于NGS的分析的遗传病患者获得及时、经济高效和准确的结果。我们的第一阶段假设是，SolveBio的参考数据基础设施和面向用户的设计将系统地减少和简化变体解释中的手动分析步骤。我们的目标是通过算法将特定变体的所有已知和可能的符号聚集在一起，并建立和设计相关的文献整理和Rankin系统。我们的第二阶段目标将包括将VE构建成一个模块化和完整的数据分析解决方案，并使用一个能够为每个变量分配初步临床意义的变量分类器。基于NGS的诊断预计在未来5-10年内市场规模将增长10倍。SolveBio的Variant Explorer将帮助消除分析瓶颈，为广泛采用基于NGS的技术和实现精确医学铺平道路。