GPU-accelerated Peptide Search
GPU-accelerated Peptide Search
批准号:
8201122
负责人:
Brian Pratt
金额:
$20.36万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2011
资助国家:
美国
项目状态:
已结题
起止时间:
2011-08-12 至 2013-07-31
关键词:
Amino Acid SequenceAreaBiologicalComputer AnalysisComputer softwareComputersData SetDevicesDisciplineElementsEnvironmentFutureLaboratoriesMass Spectrum AnalysisMethodsPeptide HydrolasesPeptide Sequence DeterminationPeptidesPerformancePost-Translational Protein ProcessingProcessProteinsProteomeProteomicsPublic HealthQualifyingResearchResearch PersonnelResolutionResourcesRunningShotgunsSolutionsSpeedTechniquesTrypsinWorkcluster computingcomputer clustercostexperiencefallsimprovedinstrumentinterestlaptopopen sourceoperationprogramsprototyperesearch studysupercomputer
中文摘要
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英文摘要
DESCRIPTION (provided by applicant): The use of Graphics Processing Unit (GPU) devices is proposed to increase the speed of peptide search by as much as an order of magnitude. Peptide search engines perform the most computation-intensive task in shotgun proteomics. This proposal is to speed up peptide search using the increasingly popular approach of general-purpose computation on highly-parallel GPU devices. Computation using GPUs to gain "desktop supercomputer" performance at low cost is an active and fruitful area of research. This project will obtain these benefits for peptide search. As a result, researchers will be able to take experiments that today must be analyzed on a computer cluster, and instead analyze them on their desktop computer.
PUBLIC HEALTH RELEVANCE: A enhancement to the popular peptide search engine X!Tandem is proposed, to allow it to run in parallel on graphics processing unit (GPU) cards with dramatically faster performance. This will allow much faster and less-expensive operation, allowing proteomics researchers to analyze large proteomics experiments or search for post- translational modifications (PTMs) using their existing desktop computer. Proteomics has led to many important advances in biological understanding. Yet, many valuable data sets are not searched for PTMs, simply because the computer power necessary to conduct the searches is not available. With this project, we plan to substantially reduce the computational cost of proteomics experiments, via a peptide search engine making use of inexpensive, highly parallel GPU hardware. Thus, this project, if successful, will allow proteomics to be more successfully exploited for public health. The research team is well-qualified to undertake this research, having extensive, direct experience in all the scientific disciplines and specific software elements necessary. The research team includes experts in proteomics, mass spectrometry, peptide search, and GPU computing.
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