Improved data driven sgRNA design for use in bacteria

CRISPR/Cas9 is a promising tool for genome engineering in bacteria, but it's limited by inconsistent accuracy. Though some studies have been conducted to understand why this inconsistency occurs, many important biological features have not been explored. Moreover, computer based attempts to predict accuracy have suffered from these knowledge gaps. This is due mainly to the fact that the mathematical equations that these predictions are based on, do not take these biological features into account. This project will use new laboratory data to develop new mathematical equations and ultimately improve the reliability of these CRISPR/Cas9 systems.

Intern: 
Dalton Ham;Tyler Browne
Faculty Supervisor: 
David Edgell
Province: 
Ontario
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