OCT 03, 2018 1:30 PM PDT

Whole-Genome Identification of CRISPR Targets

Presented at: CRISPR 2018
C.E. Credits: P.A.C.E. CE Florida CE
Speakers
  • Senior Lecturer, Science and Engineering Faculty, Electrical Engineering, Computer Science, Data Science, Queensland University of Technology
    Biography
      Dr Dimitri Perrin is Lecturer in Data Science at Queensland University of Technology. Prior to joining QUT, he worked as a FPR Fellow in the Laboratory for Systems Biology (RIKEN, Japan) and as an IRCSET Marie-Curie Research Fellow with the Centre for Scientific Computing & Complex Systems Modelling (Dublin City University, Ireland) and the Department of Information Networking (Osaka University, Japan).

      His research interests are in developing new approaches to analyse, understand and optimise biomedical and social systems. His work therefore spans the areas of modelling and simulation, computational biology and bioinformatics, and data analysis. Recent projects include gene editing (CRISPR), high-resolution biomedical imaging (CUBIC), and mobile apps for health research.

      Dimitri Perrin holds a Master's Degree (Diplôme d'Ingénieur) in Computer Engineering from ISIMA (Aubière, France), a M.Sc. in Computing from Université Blaise Pascal (Clermont-Ferrand, France), and received his Ph.D. in Computing from Dublin City University (Dublin, Ireland).

    Abstract:

    Gene editing using CRISPR is a very promising technology, and it has already had a significant impact on a number of research fields. However, while CRISPR makes targeted modifications easier to achieve, designing suitable guides is not a trivial task. It is crucial to ensure both the efficiency and the safety of the selected target sites. In this presentation, we discuss how to implement relevant selection criteria into an algorithm for whole-genome detection and evaluation of candidate sites. We report on the impact of specific design rules and on results to date. We also outline outstanding computational challenges.

    Learning Objectives:

    1. Learn how to improve the efficiency of CRISPR knock-outs
    2. Understand how computational tools can accelerate gene editing projects
    3. Learn about outstanding challenges in making these tools more practical and more widely applicable


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