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

项目摘要 本申请旨在补充我们的母基金R 01 HG 012286中的软件开发，标题为“Calypso：一个网络 软件系统支持团队为基础的，纵向基因组诊断护理”。我们开发卡里普索是为了满足 诊断分析需要在临床环境中，其中很大一部分患者在很长一段时间内保持非诊断状态 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。我们基于云 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; a 一套直观的IOBIO网络工具将实现诊断分析;以及以案例为中心的沟通和协作 接口将协调诊断团队。然而，即使是最好的诊断变异分析过程， 如果致病变异没有被检测到，就不能成功。而建立的计算管道存在， 高度准确和灵敏的检测遗传变异，目前的工具仍然不足以检测新发疾病- 导致突变，特别是结构变异事件。为了解决这个瓶颈，我们开发了一个基于kmer的 突变检测软件工具，RUFUS，并证明了其能力，大大提高检测致病性 DNM在各种疾病中的作用。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，我们建议加强目前研究级RUFUS工具的影响力 通过改进其实施和云就绪性，加速其被更广泛的基因组医学采用 社区首先，我们将重新设计核心RUFUS代码库，以生成一个健壮的，生产就绪的， 维护软件包，而不改变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库进行临时输入/输出处理;重新构造日志记录以生成 提供信息的运行时消息;并实现自动化代码测试（单元和集成测试），以简化未来 发展其次，我们将启用RUFUS包的云原生采用，该包最初旨在 在Linux环境中运行。我们将通过使RUFUS适应分布式计算来提高可伸缩性， 实现了比当前的，多线程的， 实现;并建立容器化，使RUFUS能够并入云原生运行时环境 和基于工作流语言的管道。最后，第三，我们将加强用户和开发人员社区的参与， 采用标准版本管理实践，以提供纳入临床的先决软件来源 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;以及 扩展当前的骨架工具文档以便于用户采用。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。

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.