Technology for large-scale genetic interaction discovery in S. cerevisiae
Technology for large-scale genetic interaction discovery in S. cerevisiae
批准号:
7676162
负责人:
Frederick P Roth
金额:
$25.43万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2008
资助国家:
美国
项目状态:
已结题
起止时间:
2008-08-20 至 2011-06-30
关键词:
AllelesAnabolismAnimal ModelBar CodesBiologicalCell WallCellsChromatinChromosome MappingCollectionComplexCoupledCytokinesisDNADNA DamageDNA RepairDNA Repair GeneDNA SequenceDeletion MutationDiabetes MellitusDiploidyElectrophoresisEnvironmentFutureGene DeletionGenesGeneticGenetic TranscriptionGenetic screening methodGrowthHaploidyHuman Genome ProjectInvestmentsLibrariesMalignant NeoplasmsMapsMeasurementMeasuresMethodsMutationPathway interactionsPhenotypePilot ProjectsProcessRNA Polymerase IIRelative (related person)Saccharomyces cerevisiaeShapesTechnologyTranscription ElongationTranscriptional RegulationVariantYeastsbasecostgenetic technologyhuman diseaseimprovedmutantnew technologynext generationnovel
中文摘要
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英文摘要
DESCRIPTION (provided by applicant): When two genes are perturbed simultaneously, a surprising phenotype often emerges. Genetic interaction - defined by this phenomenon - suggests that the interacting genes have related functions. Here we propose to develop a new technology ("bar-code fusion genetics" or BFG) for detecting genetic interactions in S. cerevisiae. The BFG method exploits existing libraries of strains carrying bar-coded gene deletions, and harnesses the throughput and economy of next-generation sequencing technology. If successful, the BFG technology has the potential to allow a single technician in a single year to generate a map of genetic interactions amongst all 18 million S. cerevisiae gene pairs in any given growth condition. In the context of this two-year pilot proposal, we propose to develop and optimize the BFG technology, and assess its sensitivity and potential value by applying it to the processes of DNA repair and RNA polymerase II transcription elongation.
Two genes are defined to have a genetic interaction if the perturbation of both genes together yields a surprising phenotype. Complex human diseases such as cancer or diabetes require multiple mutations and are therefore the results of genetic interaction. Here we propose a technology in the model organism S. cerevisiae that could economically map genetic interactions amongst all genes in a given growth environment, and apply the approach in a pilot study of DNA repair and transcription genes.
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