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
基本信息
- 批准号:10593480
- 负责人:
- 金额:$ 21.56万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 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:
- 关键词:AccelerationAdoptedAdoptionAdvanced Malignant NeoplasmAlgorithmic AnalysisAlgorithmsAreaArtificial IntelligenceBioinformaticsBiopsyCancer PatientCharacteristicsChildhoodClinicClinicalCodeCollaborationsCollectionCommunitiesComputer softwareConsumptionCritically ill childrenDNA sequencingDataData AnalysesData SetDemocracyDevelopmentDiagnosticDropsExclusionFunding OpportunitiesFutureGenerationsGenomeGenomicsGerm-Line MutationGoalsHourHuman GenomeInformaticsLaboratoriesLibrariesManualsMathematicsMeasuresModalityMutation DetectionNeonatal Intensive Care UnitsPatientsPerformancePositioning AttributeProcessResearchSelection for TreatmentsSequence AlignmentSomatic MutationSpeedTechniquesTimeVariantWorkanalysis pipelinebasecancer therapyclinical applicationclinically relevantcomputerized data processingcostdesignexperiencegenome sequencinggenome-widegigabyteinformatics toolmodel developmentnext generation sequencingopen sourceoptimal treatmentsparallelizationprecision medicineprecision oncologyprogramssuccesstask analysistooltumorwhole genome
项目摘要
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
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Gabor T Marth其他文献
Extending reference assembly models
- DOI:
10.1186/s13059-015-0587-3 - 发表时间:
2015-01-24 - 期刊:
- 影响因子:9.400
- 作者:
Deanna M Church;Valerie A Schneider;Karyn Meltz Steinberg;Michael C Schatz;Aaron R Quinlan;Chen-Shan Chin;Paul A Kitts;Bronwen Aken;Gabor T Marth;Michael M Hoffman;Javier Herrero;M Lisandra Zepeda Mendoza;Richard Durbin;Paul Flicek - 通讯作者:
Paul Flicek
Gabor T Marth的其他文献
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{{ truncateString('Gabor T Marth', 18)}}的其他基金
A reference-free computational algorithm for comprehensive somatic mosaic mutation detection
一种用于综合体细胞嵌合突变检测的无参考计算算法
- 批准号:
10662755 - 财政年份:2023
- 资助金额:
$ 21.56万 - 项目类别:
Calypso: a web software system supporting team-based, longitudinal genomic diagnostic care
Calypso:支持基于团队的纵向基因组诊断护理的网络软件系统
- 批准号:
10559599 - 财政年份:2022
- 资助金额:
$ 21.56万 - 项目类别:
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 - 财政年份:2022
- 资助金额:
$ 21.56万 - 项目类别:
Calypso: a web software system supporting team-based, longitudinal genomic diagnostic care
Calypso:支持基于团队的纵向基因组诊断护理的网络软件系统
- 批准号:
10376642 - 财政年份:2022
- 资助金额:
$ 21.56万 - 项目类别:
Cardiovascular Development Data Resource Center (CDDRC)
心血管发育数据资源中心 (CDDRC)
- 批准号:
10461828 - 财政年份:2020
- 资助金额:
$ 21.56万 - 项目类别:
Cardiovascular Development Data Resource Center (CDDRC)
心血管发育数据资源中心 (CDDRC)
- 批准号:
10027798 - 财政年份:2020
- 资助金额:
$ 21.56万 - 项目类别:
Cardiovascular Development Data Resource Center (CDDRC)
心血管发育数据资源中心 (CDDRC)
- 批准号:
10242178 - 财政年份:2020
- 资助金额:
$ 21.56万 - 项目类别:
Longitudinal models of breast cancer for studying mechanisms of therapy response and resistance
用于研究治疗反应和耐药机制的乳腺癌纵向模型
- 批准号:
10457293 - 财政年份:2018
- 资助金额:
$ 21.56万 - 项目类别:
Longitudinal models of breast cancer for studying mechanisms of therapy response and resistance
用于研究治疗反应和耐药机制的乳腺癌纵向模型
- 批准号:
10228719 - 财政年份:2018
- 资助金额:
$ 21.56万 - 项目类别:
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