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.

项目摘要 此申请提案以补充我们的父母赠款R01HG012286中的软件开发，标题为“ Calypso：一个网络 软件系统支持基于团队的纵向基因组诊断护理”。我们正在开发Calypso以满足 临床环境中的诊断分析需求，其中很大一部分患者在长时间保持非诊断 时间，即未经诊断的疾病诊所，新生儿重症监护病房和小儿专科诊所。我们的基于云的 平台将为患者的基因组数据的长期存储和定期自动重新分析提供能力；一个 直观的IOBIO WebTools套件将实现诊断分析；以及以案例为中心的沟通和协作 接口将协调诊断团队合作。但是，即使是最好的诊断变体分析过程 如果未被发现的引起疾病的变体仍然无法成功。而已建立的计算管道存在 高度准确和敏感的检测遗传变化，目前的工具仍然表现不佳，用于检测从头疾病 - 引起突变，尤其是结构变体事件。为了解决这个瓶颈，我们开发了一个基于kmer的瓶颈 突变检测软件工具，rufus，并证明了其基本改善因果关系检测的能力 多种疾病中的DNM。根据资金机会的目的不是-OD-22-068“增强软件 开放科学和云的工具”，我们建议在这里提高当前研究级Rufus工具的影响 通过改善其实施和云准备，以加速其通过更广泛的基因组医学的采用 社区。首先，我们将重新设计核心Rufus代码基础，以产生强大的，生产的准备和轻松 可维护的软件包，而无需更改其已经有效的算法行为。我们将取代鲁弗斯的 目前使用事实上的社区标准HTSLIB库进行了临时输入/输出处理；约束伐木以生产 内容丰富的运行时消息；并实施自动代码测试（单位和集成测试）以简化未来 发展。其次，我们将启用最初设计的rufus软件包的云原状采用 在Linux环境中运行。我们将通过调整RUFU进行分布式计算来提高可伸缩性，从而提高可伸缩性 使用当前的多线程，实现比超过可能的并行速度和执行速度 执行;并建立了容器化，以使Rufus的行业进入云本地运行时环境 和工作流语言基础管道。最后，第三，我们将通过 采用标准版本措施，为公司提供前提软件出处 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；和 扩展当前的骨骼工具文档，以简化用户的采用。重要的是，我们将提供示例NextFlow Rufus常见用例的工作流以及每个用例的代表性数据集。