Optimising CRISPR Genome Editing using Machine Learning
Desktop Genetics is designing and testing CRISPR experiments to create training datasets for an AI that can be used for genome engineering applications in microbe, plant, animal and human cells. Decision rules are commonly used to design CRISPR genome editing procedures. Such rules use sequence-level and contextual features of the predicted genomic cut site locations to predict the activity, specificity, and outcome of using CRISPR in vitro and in vivo. We demonstrate significant improvements in designing genome editing procedures, which we have tested in an immortalized melanoma cell line and report on here.
As a biochemist turned software engineer, Riley recognized the enormous untapped potential for software innovation in the life sciences. An alum of Genentech, he brings ten years of laboratory-based genetic engineering experience to Desktop Genetics, where his team's research is focused on software systems for the rational design of CRISPR/Cas9 Genome Editing vectors and libraries. Riley is passionate about developing the next-generation of bioinformatics tools that drive science forward. He holds a Bachelor's of Engineering from Dartmouth College, a Bachelor’s of Arts in Biochemistry from Colby College, and and an MPhil in Bioscience Enterprise from the University of Cambridge.