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.

项目总结