Empowering real-time, web-based genomic big data analysis at commercial scale.
Empowering real-time, web-based genomic big data analysis at commercial scale.
批准号:
9379482
负责人:
Gabor T Marth
金额:
$5.0万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2016
资助国家:
美国
项目状态:
已结题
起止时间:
2016-09-22 至 2017-08-31
关键词:
BackBig DataBioinformaticsChromosomesCommunitiesComputer AnalysisComputer HardwareComputer softwareComputersCore FacilityCustomDNADNA Sequencing FacilityDNA sequencingDataData AnalysesData QualityData SetDatabasesDevelopmentDiseaseDropsEffectivenessEnsureEquilibriumFamilyFeesFundingGalaxyGenerationsGenesGeneticGenomeGenomicsGoalsGrantHuman GenomeImageryInstitutionInternetIntuitionLarge-Scale SequencingLeadLibrariesLicensingMetagenomicsMethodsModelingOnline SystemsPerformancePhasePopulationPositioning AttributePreparationQuality ControlReportingResearchResearch InfrastructureResolutionResourcesSamplingSequence AlignmentSoftware ToolsTechniquesTextTimeVariantWorkbasecommercial applicationcomputer infrastructurecomputing resourcescostempoweredexperiencegenetic counselorgenetic pedigreegenetic variantgenome wide association studygenome-widegenomic dataimprovedinfrastructure developmentinterestnext generation sequencingopen sourceoperationpublic health relevancescale upstatisticstask analysistooltrendtumorweb appwhole genome
中文摘要
描述(由申请人提供):学术iobio项目提供基于Web的应用程序,这些应用程序可以可视化并提供与多个不同数据的实时交互分析
作为下一代测序项目的一部分生成的类型。这些应用程序是开源的,并且是免费提供的。这项提案旨在开发关键的计算基础设施,使iobio项目能够提供商业应用,提供比学术项目更多的能力和功能。特别是,有必要构建多个组件,以按需、可扩展和健壮的方式促进对大型计算资源的管理,并提供远程文件存储机制。此外,将作为学术发展支柱的实时分析与商业应用进行的大规模分析无缝结合的功能,将使从临床医生/遗传顾问到大规模测序中心和机构的所有测序数据用户都能使用。该提案的长期目标是定位FrameShift实验室,以便快速和轻松地建立新的商业上可行的基于Web的应用程序,以解决当前的生物信息学分析问题。该提案还包括开发一个名为Multibam.iobio的商业应用程序,该应用程序将为大规模测序研究的生产者和消费者提供评估其海量数据集质量的手段。为了进行群体水平的全基因组关联研究或更专注于小家系的孟德尔研究,在花费大量时间和资源进行分析之前,必须了解数据的质量。Multibam.iobio应用程序将可视化高级统计信息,从而快速识别异常样本或数据趋势。所有样品都可以进行更有针对性的实时分析。测序项目的有效性,从肿瘤/正常对中聚焦的体细胞变异识别到种群规模的GWAs,都需要始终如一的高数据质量,无论这是潜在的序列比对还是遗传变异。Multibam.iobio将确保所有感兴趣的各方,无论其计算经验和资源限制如何,都可以询问和充分理解他们的数据。
英文摘要
DESCRIPTION (provided by applicant): The academic iobio project delivers web-based applications that visualize and provide real-time interactive analysis with multiple different data
types generated as part of next-generation sequencing projects. These applications are open-source and provided for free. This proposal aims to develop critical computational infrastructure to enable the iobio project to offer commercial applications, providing more power and functionality than is possible for the academic project. In particular, it is necessary to build multiple components that will facilitate management of large computational resources in an on-demand, scalable and robust manner and provide mechanisms for remote file storage. Additionally, functionality to seamlessly combine real-time analysis which is the mainstay of the academic developments with large scale analysis undertaken by commercial applications will empower all users of sequencing data from clinicians / genetic counsellors to large scale sequencing centres and institutions. The long-term objective of the proposal is to position Frameshift labs to rapidly and easily build new commercially viable web-based applications to tackle current bioinformatics analysis problems. This proposal also includes the development of a commercial application called multibam.iobio that will provide producers and consumers of large scale sequencing studies the means to evaluate the quality of their massive data sets. In order to perform population level genome wide association studies (GWAS) or more focused Mendelian studies on small family pedigrees, the quality of the data must be understood prior to expending large amounts of time and resources on analysis. The multibam.iobio application will visualize high-level statistics allowing outlier samples or data trends to be rapidly identified. More focused real-time analysis will be accessible for all samples. The effectiveness of sequencing projects, from focused somatic variant identification in tumor / normal pairs to population scale GWAS demands consistently high data quality, whether this is the underlying sequence alignments or genetic variants. Multibam.iobio will ensure that all interested parties, regardless of computational experience and resource limitations, can interrogate and fully understand their data.
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国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis
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批准号:--
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项目类别:合作创新研究团队
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资助金额:--
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批准年份:2024
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负责人:姚韬
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依托单位: