Accelerating genomic analysis for time critical clinical applications

加速时间紧迫的临床应用的基因组分析

• 批准号: 10593480
• 负责人: Gabor T Marth
• 金额: $ 21.56万
• 依托单位: UNIVERSITY OF UTAH
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-10 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10593480
• 关键词: Acceleration; Adopted; Adoption; Advanced Malignant Neoplasm; Algorithmic Analysis; Algorithms; Area; Artificial Intelligence; Bioinformatics; Biopsy; Cancer Patient; Characteristics; Childhood; Clinic; Clinical; Code; Collaborations; Collection; Communities; Computer software; Consumption; Critically ill children; DNA sequencing; Data; Data Analyses; Data Set; Democracy; Development; Diagnostic; Drops; Exclusion; Funding Opportunities; Future; Generations; Genome; Genomics; Germ-Line Mutation; Goals; Hour; Human Genome; Informatics; Laboratories; Libraries; Manuals; Mathematics; Measures; Modality; Mutation Detection; Neonatal Intensive Care Units; Patients; Performance; Positioning Attribute; Process; Research; Selection for Treatments; Sequence Alignment; Somatic Mutation; Speed; Techniques; Time; Variant; Work; analysis pipeline; base; cancer therapy; clinical application; clinically relevant; computerized data processing; cost; design; experience; genome sequencing; genome-wide; gigabyte; informatics tool; model development; next generation sequencing; open source; optimal treatments; parallelization; precision medicine; precision oncology; programs; success; task analysis; tool; tumor; whole genome

SUMMARY / ABSTRACT Genome-scale DNA sequencing has revolutionized the practice of precision medicine, at dramatically reduced cost. It is possible today to sequence an entire human genome in roughly one day; however, bioinformatic analysis typically takes days or weeks, and has emerged as the major bottleneck for successfully utilizing genome sequencing in time-critical applications, e.g. for identifying the genomic vulnerabilities of a patient’s tumor for rational cancer treatment selection within a clinically relevant timeframe. The overarching goal of this proposal is to dramatically speed up genomic analysis algorithms via heterogeneous computing techniques. Here we will focus on one critical aspect of genomic analysis, i.e. variant calling, and set the ambitious goal of completing the analysis of a 60X-coverage Illumina whole genome sequencing dataset in under 10 minutes, far faster than the current state of the art. Although here applied to only one analysis task, accomplishing such a high degree of acceleration would demonstrate that the techniques we are developing in this proposal are also generalizable across many other genomic analysis tasks. Our approach is to first accelerate the most widely reusable software components, to maximize value for the genomic analysis tool developer community, who will then be able to integrate these components into their own tools. With these reusable software components, we will accelerate the FreeBayes variant caller tool. FreeBayes is a widely used germline variant and somatic mutation detection tool, and therefore acceleration will benefit a large user audience. This software was developed in our own laboratory, and therefore we are intimately familiar with its algorithms and code base, positioning us for success in this exploratory project. If successful, our technique will be applicable for accelerating many, currently time-consuming analysis tasks. As a result, analysts will be able to finish sophisticated data processing tasks within minutes, as part of their interactive analysis session rather than a batched background process, and complete manual result review immediately after; rendering the complete analysis process sufficiently fast for time-critical clinical applications.

摘要/摘要