Enhancing CRISPR-Cas9 gRNA efficiency prediction by data integration and deep learning

Enhancing CRISPR-Cas9 gRNA efficiency prediction by data integration and deep learning

High-quality gRNA activity data is needed for accurate on-target efficiency predictions. Here the authors generate activity data for over 10,000 gRNA and build a deep learning model CRISPRon for improved performance predictions.

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Détails bibliographiques
Auteurs principaux: Xi Xiang, Giulia I. Corsi, Christian Anthon, Kunli Qu, Xiaoguang Pan, Xue Liang, Peng Han, Zhanying Dong, Lijun Liu, Jiayan Zhong, Tao Ma, Jinbao Wang, Xiuqing Zhang, Hui Jiang, Fengping Xu, Xin Liu, Xun Xu, Jian Wang, Huanming Yang, Lars Bolund, George M. Church, Lin Lin, Jan Gorodkin, Yonglun Luo
Format: article
Langue:EN
Publié: Nature Portfolio 2021
Sujets:
Science
Q
Accès en ligne:https://doaj.org/article/d2630a00271e4fa5a1f663c1d03d658b
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