Compressive Genomics for Large Omics Data Sets: Algorithms, Applications and Tools
Compressive Genomics for Large Omics Data Sets: Algorithms, Applications and Tools
批准号:
9546755
负责人:
BONNIE BERGER
金额:
$35.02万
依托单位国家:
美国
项目类别:
财政年份:
2013
资助国家:
美国
项目状态:
已结题
起止时间:
2013-09-05 至 2020-08-31
关键词:
AccelerationAddressAdoptionAgeAlgorithmsAutistic DisorderAutoimmune DiseasesBioinformaticsBiologicalBiological ProcessBiologyBiomedical ResearchClinicalCloud ComputingCohort StudiesCollaborationsCommunitiesComplexComputer softwareComputersComputing MethodologiesDNA sequencingDataData CompressionData FilesData SetDevelopmentDimensionsDiseaseEnsureExhibitsFoundationsFractalsGenetic VariationGenomeGenomicsGoalsGrantHealthHumanIndianaIndividualIndustryInformaticsIntuitionInvestigationMainstreamingMalignant NeoplasmsMapsMetagenomicsMethodologyMolecularNew EnglandPatientsPatternPharmacogenomicsPrivacyProcessProgress ReportsResearchResearch PersonnelSavingsSecureSecuritySequence AnalysisStructureTechniquesTechnologyThe Cancer Genome AtlasTherapeuticTimeTranscriptUniversitiesVariantWorkautism spectrum disorderbasecloud platformcohortcomputer frameworkcomputerized toolscryptographydata exchangedata structuredesignencryptionfile formatgenomic datahuman DNAhuman RNA sequencinghuman datahuman diseaseimprovedinnovationinsightinterestmicrobiomemicrobiome therapeuticsmonomethoxypolyethylene glycolnext generation sequencingnovelsoftware developmenttooltransmission processwhole genome
中文摘要
项目总结
英文摘要
Project Summary
High-throughput experimental technologies are generating increasingly massive and complex genomic
sequence data sets. While these data hold the promise of uncovering entirely new biology, their sheer
enormity threatens to make their interpretation computationally infeasible. The continued goal of this
project is to design and develop innovative compression-based algorithmic techniques for efficiently
processing massive biological data. We will branch out beyond compressive search to address the
imminent need to securely store and process large-scale genomic data in the cloud, as well as to gain
insights from massive metagenomic data.
The key underlying observation is that genomic data is highly structured, exhibiting high degrees of
self-similarity. In our previous granting period, we exploited its high redundancy and low fractal
dimension to enable scalable compressive storage and acceleration for search of sequence data as
well as other biological data types relevant to structural bioinformatics and chemogenomics. In this
renewal, we will continue to capitalize on the structure (i.e., compressibility) of genomic data to: (i)
overcome privacy concerns that arise in sharing sensitive human data (e.g. on the cloud); (ii) address
new challenges, beyond search, with metagenomic data; and (iii) seek to widen the adoption of the
previous and newly-proposed compressive algorithms for industry, research, and clinical use. We will
demonstrate the utility of our compressive techniques to the characterization of human genomic and
metagenomic variation.
We will collaborate with co-I Sahinalp's lab (Indiana University, Bloomington) on developing and
applying these tools to high-throughput data sets including autism spectrum disorder (with Isaac
Kohane and Evan Eichler) and cancer (with PCAWG, Pan Cancer Analysis of Whole Genomes), the
microbiome (with Eric Alm and Jian Peng), as well as human variation analysis (GATK, with Eric
Lander and Eric Banks). The broad, long-term goal is to apply our compressive approach to
massive biological data sets to elucidate the still obscure molecular landscape of diseases.
Successful completion of these aims will result in computational methods and tools that will significantly
increase our ability to securely store, access and analyze massive data sets and will reveal
fundamental aspects of genetic variation, as well as testable hypotheses for experimental
investigations. Not only will all developed software be made publicly available, but as part of our
integration aim, we will also ensure that the research community can make use of our innovations with
minimal effort. Through our research collaborations, we will both build these tools and demonstrate
their relevance to the characterization of human health and disease.
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专著(0)
科研奖励(0)
会议论文
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依托单位:
Privacy-preserving genomic medicine at scale
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批准号:10266081
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资助金额:$67.49万
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财政年份:2020
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依托单位:
Privacy-preserving genomic medicine at scale
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项目类别:
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资助金额:$66.28万
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财政年份:2020
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负责人:BONNIE BERGER
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依托单位:
Privacy-preserving genomic medicine at scale
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批准号:10662349
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项目类别:
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资助金额:$66.75万
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财政年份:2020
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负责人:BONNIE BERGER
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依托单位:
Developing high-throughput genetic perturbation strategies for single cells in cancer organoids
-
批准号:10212991
-
项目类别:
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资助金额:$92.22万
-
财政年份:2020
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负责人:BONNIE BERGER
-
依托单位:
Compressive genomics for large omics data sets: Algorithms applications & tools
-
批准号:8849927
-
项目类别:
-
资助金额:$20.94万
-
财政年份:2013
-
负责人:BONNIE BERGER
-
依托单位:
Compressive genomics for large omics data sets: Algorithms applications & tools
-
批准号:8599836
-
项目类别:
-
资助金额:$21.79万
-
财政年份:2013
-
负责人:BONNIE BERGER
-
依托单位:
Compressive Genomics for Large Omics Data Sets: Algorithms, Applications and Tools
-
批准号:9247325
-
项目类别:
-
资助金额:$37.2万
-
财政年份:2013
-
负责人:BONNIE BERGER
-
依托单位:
Compressive genomics for large omics data sets: Algorithms applications & tools
-
批准号:8730209
-
项目类别:
-
资助金额:$21.32万
-
财政年份:2013
-
负责人:BONNIE BERGER
-
依托单位:
MIT/Whitehead/Broad Computational Genetics Training Program
-
批准号:8132612
-
项目类别:
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资助金额:$17.29万
-
财政年份:2009
-
负责人:BONNIE BERGER
-
依托单位:
Structure-Based Prediction of the Interactome
-
批准号:7895360
-
项目类别:
-
资助金额:$28.99万
-
财政年份:2009
-
负责人:BONNIE BERGER
-
依托单位:
Structure-Based Prediction of the Interactome
-
批准号:8054929
-
项目类别:
-
资助金额:$28.99万
-
财政年份:2008
-
负责人:BONNIE BERGER
-
依托单位:
Structure based prediction of the interactome
-
批准号:8439763
-
项目类别:
-
资助金额:$32.03万
-
财政年份:2008
-
负责人:BONNIE BERGER
-
依托单位:
Structure based prediction of the interactome
-
批准号:8848384
-
项目类别:
-
资助金额:$32.08万
-
财政年份:2008
-
负责人:BONNIE BERGER
-
依托单位:
Structure-Based Prediction of the Interactome
-
批准号:7464375
-
项目类别:
-
资助金额:$28.09万
-
财政年份:2008
-
负责人:BONNIE BERGER
-
依托单位:
Structure based Prediction of the interactome
-
批准号:9549093
-
项目类别:
-
资助金额:$34.65万
-
财政年份:2008
-
负责人:BONNIE BERGER
-
依托单位:
Structure-Based Prediction of the Interactome
-
批准号:7797574
-
项目类别:
-
资助金额:$30.12万
-
财政年份:2008
-
负责人:BONNIE BERGER
-
依托单位:
海外基金