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 基因组规模的 DNA 测序彻底改变了精准医疗的实践，大大降低了精准医疗的成本。 然而，今天可以在大约一天内对整个人类基因组进行测序； 分析通常需要几天或几周的时间，并且已成为成功利用的主要瓶颈 时间紧迫的应用中的基因组测序，例如识别患者基因组的脆弱性 在临床相关的时间范围内合理选择癌症治疗的总体目标。 提案是通过异构计算显着加速基因组分析算法 在这里，我们将重点关注基因组分析的一个关键方面，即变异调用，并设置 雄心勃勃的目标是在不到 60 倍的时间内完成 Illumina 全基因组测序数据集的分析 10 分钟，远远快于当前的技术水平，尽管这里仅适用于一项分析任务， 实现如此高的加速度将证明我们正在开发的技术 该建议也适用于许多其他基因组分析任务。我们的方法是首先。 加速最广泛可重用的软件组件，最大限度地提高基因组分析工具的价值 开发人员社区，然后他们将能够将这些组件集成到他们自己的工具中。 借助这些可重用的软件组件，我们将加速 FreeeBayes 变体调用程序工具。 广泛使用的种系变异和体细胞突变检测工具，因此加速将受益匪浅 该软件是在我们自己的实验室开发的，因此我们非常熟悉。 它的算法和代码库使我们能够在这个探索性项目中取得成功如果成功的话，我们的技术。 将适用于加速许多当前耗时的分析任务，因此分析师将能够。 能够在几分钟内完成复杂的数据处理任务，作为交互式分析会话的一部分 而不是批处理的后台进程，并在渲染后立即完成手动结果审核； 足够快地完成分析过程，适合时间紧迫的临床应用。