包含 DNA 序列以外内源因素的深度学习模型提高了碱基编辑结果的预测准确性。

IF 13 1区 生物学 Q1 CELL BIOLOGY
Tanglong Yuan, Leilei Wu, Shiyan Li, Jitan Zheng, Nana Li, Xiao Xiao, Haihang Zhang, Tianyi Fei, Long Xie, Zhenrui Zuo, Di Li, Pinzheng Huang, Hu Feng, Yaqi Cao, Nana Yan, Xinming Wei, Lei Shi, Yongsen Sun, Wu Wei, Yidi Sun, Erwei Zuo
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引用次数: 0

摘要

腺嘌呤碱基编辑器(ABE)和胞嘧啶碱基编辑器(CBE)能对目标DNA位点进行单核苷酸编辑,避免产生双链断裂,然而影响体内碱基编辑结果的基因组特征仍有待确定。人们利用慢病毒整合文库的高通量数据集来研究影响碱基编辑结果的序列特征,但DNA序列之外的内源因素的影响在很大程度上仍是未知的。本文评估了在哺乳动物细胞中对 5012 个内源基因组位点和 11,868 个基因组整合靶序列进行 ABE 和 CBE 碱基编辑的结果,其中 4654 个基因组位点共享相同的靶序列。对比分析表明,ABE 和 CBE 在内源性位点的编辑结果与使用基因组整合序列获得的结果有很大不同。我们发现,ABE和CBE在内源性目标位点的碱基编辑效率受到内源性因素的影响,包括表观遗传修饰和转录活性。基于内源因素和基因组数据集的序列信息,我们开发了一种深度学习算法,即 BE_Endo,它在预测碱基编辑结果方面取得了前所未有的准确性。这些发现以及所开发的计算算法可能会促进 BE 在科学研究和临床基因治疗中的未来应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Deep learning models incorporating endogenous factors beyond DNA sequences improve the prediction accuracy of base editing outcomes.

Deep learning models incorporating endogenous factors beyond DNA sequences improve the prediction accuracy of base editing outcomes.

Adenine base editors (ABEs) and cytosine base editors (CBEs) enable the single nucleotide editing of targeted DNA sites avoiding generation of double strand breaks, however, the genomic features that influence the outcomes of base editing in vivo still remain to be characterized. High-throughput datasets from lentiviral integrated libraries were used to investigate the sequence features affecting base editing outcomes, but the effects of endogenous factors beyond the DNA sequences are still largely unknown. Here the base editing outcomes of ABE and CBE were evaluated in mammalian cells for 5012 endogenous genomic sites and 11,868 genome-integrated target sequences, with 4654 genomic sites sharing the same target sequences. The comparative analyses revealed that the editing outcomes of ABE and CBE at endogenous sites were substantially different from those obtained using genome-integrated sequences. We found that the base editing efficiency at endogenous target sites of both ABE and CBE was influenced by endogenous factors, including epigenetic modifications and transcriptional activity. A deep-learning algorithm referred as BE_Endo, was developed based on the endogenous factors and sequence information from our genomic datasets, and it yielded unprecedented accuracy in predicting the base editing outcomes. These findings along with the developed computational algorithms may facilitate future application of BEs for scientific research and clinical gene therapy.

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来源期刊
Cell Discovery
Cell Discovery Biochemistry, Genetics and Molecular Biology-Molecular Biology
CiteScore
24.20
自引率
0.60%
发文量
120
审稿时长
20 weeks
期刊介绍: Cell Discovery is a cutting-edge, open access journal published by Springer Nature in collaboration with the Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences (CAS). Our aim is to provide a dynamic and accessible platform for scientists to showcase their exceptional original research. Cell Discovery covers a wide range of topics within the fields of molecular and cell biology. We eagerly publish results of great significance and that are of broad interest to the scientific community. With an international authorship and a focus on basic life sciences, our journal is a valued member of Springer Nature's prestigious Molecular Cell Biology journals. In summary, Cell Discovery offers a fresh approach to scholarly publishing, enabling scientists from around the world to share their exceptional findings in molecular and cell biology.
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