Enhancing clinical diagnostic analysis with a robust de novo mutation detection tool

使用强大的从头突变检测工具增强临床诊断分析

• 批准号: 10608743
• 负责人: Gabor T Marth
• 金额: $ 23.02万
• 依托单位: UNIVERSITY OF UTAH
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-01 至 2023-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10608743
• 关键词: Address; Adopted; Adoption; Algorithms; Awareness; Behavior; Caring; Childhood; Clinic; Clinical; Cloud Computing; Cloud Service; Code; Collaborations; Communication; Communities; Community Medicine; Computer software; Custom; Data Set; Detection; Development; Diagnostic; Disease; Docking; Documentation; Engineering; Enrollment; Environment; Event; Family; FarGo; Funding Opportunities; Future; Genome; Genomic medicine; Genomics; Goals; Heart; Hemorrhage; Individual; Inherited; Institutes; Internet; Intuition; Language; Libraries; Linux; Minor; Mosaicism; Mutation; Mutation Detection; National Human Genome Research Institute; Neonatal Intensive Care Units; Neurologic; Output; Parents; Patients; Periodicity; Process; Production; Readiness; Registries; Reporting; Research; Resources; Respiratory Diaphragm; Retinal blind spot; Sampling; Semantics; Services; Severities; Software Tools; Speed; Structure; Support System; System; Testing; Time; Tissues; Universities; Utah; Variant; analysis pipeline; base; clinical diagnostics; cloud based; cluster computing; community engagement; computational pipelines; critically ill newborn; de novo mutation; design; disease-causing mutation; genetic variant; genome sciences; genome sequencing; genomic data; improved; model development; multithreading; open data; open source; parallelization; parent grant; proband; programs; rapid diagnosis; response; skeletal; software development; software systems; symposium; tool; variant detection

PROJECT SUMMARY This application proposes to supplement software development in our parent grant R01HG012286, entitled “Calypso: a web software system supporting team-based, longitudinal genomic diagnostic care”. We are developing Calypso to meet diagnostic analysis needs in clinical settings where a large fraction of patients remain non-diagnostic for an extended period of time, i.e. undiagnosed disease clinics, neonatal intensive care units, and pediatric subspecialty clinics. Our cloud-based platform will provide the capacity for long-term storage and periodic automated reanalysis of the patient’s genomic data; a suite of intuitive IOBIO webtools will enable diagnostic analysis; and a case-focused communication and collaboration interface will coordinate diagnostic teamwork. However, even the best-orchestrated diagnostic variant analysis process cannot succeed if the disease-causing variant remains undetected. Whereas established computational pipelines exist for highly accurate and sensitive detection of inherited variations, current tools still underperform for detecting de novo disease- causing mutations, especially structural variant events. To address this bottleneck, we have developed a kmer-based mutation detection software tool, RUFUS, and demonstrated its ability to substantially improve the detection of causative DNMs in a variety of diseases. In accordance with the aims of funding opportunity NOT-OD-22-068 “Enhancing Software Tools for Open Science and the Cloud”, here we propose to enhance the impact of the currently research-grade RUFUS tool by improving its implementation and cloud-readiness to accelerate its adoption by the broader genomic medicine community. First, we will re-engineer the core RUFUS code base to produce a robust, production-ready, and easily maintainable software package, without altering its already effective algorithmic behavior. We will replace RUFUS’s currently ad hoc input/output handling with the de facto community standard HTSlib library; restructure logging to produce informative runtime messages; and implement automated code testing (both unit and integration testing) to ease future development. Second, we will enable cloud-native adoption of the RUFUS package which was originally designed to operate in a Linux environment. We will improve scalability by adapting RUFUS for distributed computing, and thereby achieving a higher level of parallelization and execution speed than possible with the current, multi-threaded, implementation; and institute containerization to enable RUFUS’s incorporation into cloud-native runtime environments and workflow language-base pipelines. Finally, third, we will enhance user and developer community engagement, by adopting standard versioning practices to provide the prerequisite software provenance for incorporation into clinical diagnostic pipelines; enrolling our software into standard container registry services so users can easily find our tool; and expanding currently skeletal tool documentation to ease user adoption. Importantly, we will provide example nextflow workflows for RUFUS’s common use cases, together with representative datasets for each use case.

项目概要 该申请旨在补充我们母基金 R01HG012286 中的软件开发，题为“Calypso：一个网络 支持基于团队的纵向基因组诊断护理的软件系统”，我们正在开发 Calypso 来满足这一需求。 大部分患者长时间处于非诊断状态的临床环境中的诊断分析需求 时间，即未确诊疾病诊所、新生儿重症监护病房和儿科专科诊所。 平台将提供长期存储和定期自动重新分析患者基因组数据的能力； 直观的 IOBIO 网络工具套件将实现诊断分析以及以案例为中心的沟通和协作； 然而，即使是精心策划的诊断变异分析过程。 如果致病变异仍未被发现，则不可能成功。 对遗传变异的高度准确和灵敏的检测，目前的工具在检测新发疾病方面仍然表现不佳- 引起突变，特别是结构变异事件为了解决这个瓶颈，我们开发了基于 kmer 的方法。 突变检测软件工具 RUFUS，并展示了其显着改善致病原因检测的能力 根据资助机会 NOT-OD-22-068“增强软件”的目标。 “开放科学和云的工具”，在这里我们建议增强当前研究级 RUFUS 工具的影响 通过改进其实施和云就绪性，加速其在更广泛的基因组医学中的采用 首先，我们将重新设计核心 RUFUS 代码库，以生成强大的、可用于生产的且易于使用的代码库。 可维护的软件包，而不改变其已经有效的算法行为，我们将取代 RUFUS 的。 目前使用事实上的社区标准 HTSlib 库进行临时输入/输出处理以重构日志记录； 信息丰富的运行时消息；并实施自动化代码测试（单元测试和集成测试）以方便将来 其次，我们将启用 RUFUS 软件包的云原生采用，该软件包最初的设计目的是 我们将通过采用RUFUS进行分布式计算来提高可扩展性。 实现比当前多线程更高水平的并行化和执行速度 实施；并建立容器化，使 RUFUS 能够融入云原生运行时环境 最后，第三，我们将通过以下方式增强用户和开发人员社区的参与度。 采用标准版本控制实践，为纳入临床提供必要的软件来源 诊断管道；将我们的软件注册到标准容器注册表服务中，以便用户可以轻松找到我们的工具； 目前正在扩展骨架工具文档以方便用户采用，重要的是，我们将提供示例 nextflow。 RUFUS 常见用例的工作流程，以及每个用例的代表性数据集。