Supervisor

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Abstract

At present, the most versatile and widely used gene-editing tool is the CRISPR/Cas9 system, which is composed of a Cas9 nuclease and a short oligonucleotide guide RNA (or guide) that guides the Cas9 nuclease to the targeted DNA sequence (on-target) through complementary binding.  There are a large number of computational tools to design highly specific and efficient CRISPR-Cas9 guides but there is a great variation in performance and lack of consensus among the tools.  We aim to use ensemble learning to combine the benefits of a selected set of guide design tools to reach superior performance compared to any single method in predicting the efficiency of guides (for which experimental data on their efficiency is available) correctly.

Recommended for students who has done the Machine Learning and the Probability and Computing courses.