logo
ResearchBunny Logo
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

Related Publications

Explore these studies to deepen your understanding of the subject.

Machine learning-based prediction of in-hospital death for patients with takotsubo syndrome: The InterTAK-ML model
Medicine and Health

Machine learning-based prediction of in-hospital death for patients with takotsubo syndrome: The InterTAK-ML model

O. D. Filippo, V. L. Cammann, et al.

An integrated high-throughput robotic platform and active learning approach for accelerated discovery of optimal electrolyte formulations
Chemistry

An integrated high-throughput robotic platform and active learning approach for accelerated discovery of optimal electrolyte formulations

J. Noh, H. A. Doan, et al.

A humanistic model of corporate social responsibility in e-commerce with high-tech support in the artificial intelligence economy
Business

A humanistic model of corporate social responsibility in e-commerce with high-tech support in the artificial intelligence economy

E. B. Zavyalova, V. A. Volokhina, et al.

A type I interferon footprint in pre-operative biopsies is an independent biomarker that in combination with CD8+ T cell quantification can improve the prediction of response to neoadjuvant treatment of rectal adenocarcinoma
Medicine and Health

A type I interferon footprint in pre-operative biopsies is an independent biomarker that in combination with CD8+ T cell quantification can improve the prediction of response to neoadjuvant treatment of rectal adenocarcinoma

A. Rezapour, D. Rydbeck, et al.

Listen, Learn & Level Up
Over 10,000 hours of research content in 25+ fields, available in 12+ languages.
No more digging through PDFs—just hit play and absorb the world's latest research in your language, on your time.
listen to research audio papers with researchbunny