High-Throughput Native Context Mapping and Modeling of Regulatory DNA
High-Throughput Native Context Mapping and Modeling of Regulatory DNA
批准号:
9350382
负责人:
David K Gifford
金额:
$63.04万
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:
2016-09-09 至 2020-06-30
关键词:
B-Cell DevelopmentBackBase PairingBenchmarkingBeta CellBindingBiological AssayBiological ProcessCatalogsCell physiologyCellsCellular biologyChromatinChromosomesClustered Regularly Interspaced Short Palindromic RepeatsComputer SimulationComputing MethodologiesDNADataDependenceDevelopmental GeneElementsEnhancersEnsureFoundationsGene ExpressionGenesGenomeGenomic SegmentGenomicsGenotypeGoalsGuide RNAHealthHistonesHumanHuman GenomeIn SituIndividualLengthLettersLibrariesMeasuresMethodsModelingMutateMutationNon-Insulin-Dependent Diabetes MellitusNucleic Acid Regulatory SequencesOligonucleotidesPerformancePhenotypePredictive FactorRegulationRegulator GenesRegulatory ElementReporterResolutionScanningSystemTestingTrainingUntranslated RNAVariantViralWorkbasecell typeexperimental studygene functiongenome editinggenome wide association studygenomic variationhigh throughput screeninghomologous recombinationimprovedloss of functionnovelpredictive modelingrepairedsuccesstranscriptometranscriptome sequencingwhole genome
中文摘要
项目总结
英文摘要
Project Summary
We propose to systematically investigate and characterize sequence elements that are
necessary for genome function in their native context in contrast to existing high-
throughput assays of genome function that detect sequence elements that are sufficient
for function. We will accomplish this goal with three specific aims. We will develop a
novel Multiplexed Editing Regulatory Assay (MERA) to test the effect of thousands of
targeted mutations in native regulatory regions in a single experiment (Aim 1). We will
use MERA to characterize the regulation of key developmental genes, the function of
selected regulatory elements, and the gene expression effects of SNPs that are
discovered in GWAS studies (Aim 2). Using these data we will build a model that will
allow us to predict the bases that comprise the necessary genome and estimate the
effect of non-coding genotype on gene expression (Aim 3). Through our new
experimental and computational method we will help lay the groundwork for a novel
paradigm in revealing the effect of human variation at base pair resolution.
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