PT  - JOURNAL ARTICLE
AU  - Chengdong Zhang
AU  - Daqi Wang
AU  - Tao Qi
AU  - Yuening Zhang
AU  - Linghui Hou
AU  - Feng Lan
AU  - Jingcheng Yang
AU  - Leming Shi
AU  - Sang-Ging Ong
AU  - Hongyan Wang
AU  - Yongming Wang
TI  - BEdeepon: an <em>in silico</em> tool for prediction of base editor efficiencies and outcomes
AID  - 10.1101/2021.03.14.435303
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.03.14.435303
4099  - http://biorxiv.org/content/early/2021/03/15/2021.03.14.435303.short
4100  - http://biorxiv.org/content/early/2021/03/15/2021.03.14.435303.full
AB  - Base editors enable direct conversion of one target base into another in a programmable manner, but conversion efficiencies vary dramatically among different targets. Here, we performed a high-throughput gRNA-target library screening to measure conversion efficiencies and outcome product frequencies at integrated genomic targets and obtained datasets of 60,615 and 73,303 targets for ABE and CBE, respectively. We used the datasets to train deep learning models, resulting in ABEdeepon and CBEdeepon which can predict on-target efficiencies and outcome sequence frequencies. The software is freely accessible via online web server http://www.deephf.com/#/bedeep.Competing Interest StatementThe authors have declared no competing interest.