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Automated high-throughput genome editing platform with an AI learning in situ prediction model

Biology

Automated high-throughput genome editing platform with an AI learning in situ prediction model

S. Li, J. An, et al.

Discover the groundbreaking automated high-throughput platform for genome editing, which can edit thousands of samples in just a week! This innovative system integrates gRNA design and a machine learning model to predict base editing performance. Conducted by a collaborative team of leading researchers including Siwei Li, Jingjing An, and Meng Wang, this study accelerates the development of BE-based genetic therapies.

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Playback language: English
Abstract
This study presents an automated high-throughput platform for genome editing, capable of editing thousands of samples within a week. The platform integrates gRNA design, plasmid construction, base editing in mammalian cells, and a machine learning model (CAELM) to predict cytosine base editor (CBE) performance. CAELM utilizes both chromatin accessibility and sequence context to accurately predict in situ base editing results. This platform significantly accelerates the development of BE-based genetic therapies.
Publisher
Nature Communications
Published On
Nov 30, 2022
Authors
Siwei Li, Jingjing An, Yaqiu Li, Xiagu Zhu, Dongdong Zhao, Lixian Wang, Yonghui Sun, Yuanzhao Yang, Changhao Bi, Xueli Zhang, Meng Wang
Tags
genome editing
high-throughput platform
gRNA design
base editing
machine learning
genetic therapies

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