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基于深度学习的 SpCas9 活性预测模型 DeepSpCas9,具有出色的泛化性能。

SpCas9 activity prediction by DeepSpCas9, a deep learning-based model with high generalization performance.

机构信息

Department of Pharmacology, Yonsei University College of Medicine, Seoul, Republic of Korea.

Brain Korea 21 Plus Project for Medical Sciences, Yonsei University College of Medicine, Seoul, Republic of Korea.

出版信息

Sci Adv. 2019 Nov 6;5(11):eaax9249. doi: 10.1126/sciadv.aax9249. eCollection 2019 Nov.

Abstract

We evaluated SpCas9 activities at 12,832 target sequences using a high-throughput approach based on a human cell library containing single-guide RNA-encoding and target sequence pairs. Deep learning-based training on this large dataset of SpCas9-induced indel frequencies led to the development of a SpCas9 activity-predicting model named DeepSpCas9. When tested against independently generated datasets (our own and those published by other groups), DeepSpCas9 showed high generalization performance. DeepSpCas9 is available at http://deepcrispr.info/DeepSpCas9.

摘要

我们使用基于包含单指导 RNA 编码和靶序列对的人类细胞文库的高通量方法,评估了 SpCas9 在 12832 个靶序列上的活性。在这个包含大量 SpCas9 诱导的插入缺失频率的数据集上进行基于深度学习的训练,导致了一种名为 DeepSpCas9 的 SpCas9 活性预测模型的开发。当在独立生成的数据集(我们自己的和其他小组发表的数据集)上进行测试时,DeepSpCas9 表现出了很高的泛化性能。DeepSpCas9 可在 http://deepcrispr.info/DeepSpCas9 获得。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2275/6834390/3920d99b00df/aax9249-F1.jpg

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