Accelerated BAC Library Construction And Analysis
DESCRIPTION (provided by applicant): Large insert DMA libraries are essential resources for the molecular analysis of the human genome and other genomes important to our well-being. The identification of susceptibility genes for complex disorders is critically dependent on a complete and accurate sequence assembly. Comparative genomics reveals new information about metabolic pathways, cancer, horizontal gene transfer, evolution, protein families, and the genetic repertoire of numerous species. It has also increased the demand for additional genomic BAG libraries. The construction of this type of genetic resource is time consuming, costly and challenging. Numerous gaps in the human genome are difficult or impossible to close with current vector, host systems. The goal of the proposed work is the development of new BAG cloning tools, strains and methods that circumvent traditional bottlenecks and substantially improve the fidelity of molecular cloning and transformation efficiency of high molecular weight DMA. Under Phase I, a simple method for randomly shearing high molecular weight DMA and a copy number inducible transcription-free vector that eliminates several forms of cloning bias was developed. Multiple examples of difficult to clone genomes are shown to be stable in the new transcription free BAG vector. The proposed Phase II work will extend the development of the BAG vector to include additional improvements in functionality and host range, which will allow for the expression of antimicrobial small molecules in numerous gram negative microorganisms. The development of a new E. coli transformation system to significantly improve large DNA uptake and increase average insert size will impact numerous aspects of molecular medicine. A long-term objective of this proposal is to access clone gaps in the human genome and compress the rate of constructing large BAG libraries from months to a few weeks. The quality of these libraries will surpass the current standards for bias, randomness, fidelity, and contamination.
Small Business Information at Submission:
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