Abstract A045: Development of automated deep learning-based off-target distribution prediction system for CRISPR-Cas13 system

Abstract

Abstract CRISPR-Cas13 RNA editing system is a new member of the CRISPR-Cas gene editing system family, which can perform guided editing on RNA sequence. CRISPR-Cas gene editing system is currently one of the breakthrough technologies in life science, which is easy to operate, has a high editing efficiency and theoretically can perform guided editing on genome of any species. CRISPR-Cas9 system is the most commonly used CRISPR-Cas system, but it is mainly applied to DNA. Hence, fornon-destructive gene therapy and RNA virus detection, CRISPR-Cas13 system has its unique advantage. However, there are severe issues limiting its application, the most important one of which is off-target effect, namely the guide RNA points system to the wrong position causing non-targeted region edited, which leading to wrong expression spectrum of the cell. Therefore, based on automated deep learning and transfer learning techniques, this project aims to establishing an off-target distribution prediction system of CRISPR-Cas13 RNA editing system, in order to make high-specificity RNA editing possible. Citation Format: Guohui Chuai, Yanjing Zhu. Development of automated deep learning-based off-target distribution prediction system for CRISPR-Cas13 system [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor Immunology and Immunotherapy; 2023 Oct 1-4; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2023;11(12 Suppl):Abstract nr A045.