Recently, LIU Feng, distinguished associate Professor at the College of Medical Informatics of CQMU, collaborated with the team led by Prof. WANG Xiaolong from Northwest A&F University, and the team led by Prof. WANG Shengqi and Dr. Shu Wenjie from the Academy of Military Medical Sciences (AMMS) of the People's Liberation Army, in order to publish a research paper titled "Design of prime-editing guide RNAs with deep transfer learning" in the prestigious journal Nature Machine Intelligence.
The researchers have devised and developed OPED (Optimized Prime Editing Design), an intelligible nucleotide language model, aimed at forecasting the efficiency of lead editing and facilitating the design of pegRNAs. Comprehensive validation with a variety of publicly available datasets demonstrated the extensive applications of OPED in various scenarios, all of which significantly enhanced editing efficiency (2.2-82.9x). The versatility and effectiveness of OPED were demonstrated by the installation of various pathogenic genetic variants in the ClinVar database using different editing systems such as PE2, PE3/PE3b, and ePE. In fact, the average editing efficiency surpassed that of existing PE design tools. Researchers constructed an optimized OPEDVar design database comprising over 2 billion potential designs, and subsequently created the OPED website (http://bicdb.ncpsb.org.cn/OPED) to facilitate gene editing for any desired target genes.
Original Link:
https://www.nature.com/articles/s42256-023-00739-w
