Modeling CRISPR-Cas13d on-target and off-target effects using machine learning approaches.
Modeling CRISPR-Cas13d on-target and off-target effects using machine learning approaches.
复制标题
使用机器学习方法对 CRISPR-Cas13d 的靶向和脱靶效应进行建模
DOI：
10.1038/s41467-023-36316-3
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发表时间：
2023-02-10
影响因子：
16.6
通讯作者：
Li, Wei
中科院分区：
文献类型：
--
作者：
Cheng, Xiaolong;Li, Zexu;Shan, Ruocheng;Li, Zihan;Wang, Shengnan;Zhao, Wenchang;Zhang, Han;Chao, Lumen;Peng, Jian;Fei, Teng;Li, Wei
CRISPR-Cas 13 d系统应用中的一个主要挑战是准确预测其依赖于引导物的中靶和脱靶效应。在这里，我们进行了CRISPR-Cas 13 d增殖筛选，并设计了一个名为DeepCas 13的深度学习模型，以预测指导序列和二级结构的靶向活性。DeepCas 13在预测靶向蛋白质编码和非编码RNA的向导的效率方面优于现有方法。靶向非必需基因的指导物显示出脱靶活力效应，这与它们的靶向效率密切相关。在标准化过程中选择适当的阴性对照指南可减轻增殖筛选中的相关假阳性。我们将DeepCas 13应用于靶向lncRNA的指导，并在多个细胞系中鉴定影响细胞活力和增殖的lncRNA。DeepCas 13比现有方法更高的预测准确性通过二次CRISPR-Cas 13 d筛选和定量RT-PCR实验得到了广泛证实。DeepCas 13可通过http://deepcas13.weililab.org免费访问。
A major challenge in the application of the CRISPR-Cas13d system is to accurately predict its guide-dependent on-target and off-target effect. Here, we perform CRISPR-Cas13d proliferation screens and design a deep learning model, named DeepCas13, to predict the on-target activity from guide sequences and secondary structures. DeepCas13 outperforms existing methods to predict the efficiency of guides targeting both protein-coding and non-coding RNAs. Guides targeting non-essential genes display off-target viability effects, which are closely related to their on-target efficiencies. Choosing proper negative control guides during normalization mitigates the associated false positives in proliferation screens. We apply DeepCas13 to the guides targeting lncRNAs, and identify lncRNAs that affect cell viability and proliferation in multiple cell lines. The higher prediction accuracy of DeepCas13 over existing methods is extensively confirmed via a secondary CRISPR-Cas13d screen and quantitative RT-PCR experiments. DeepCas13 is freely accessible via http://deepcas13.weililab.org.
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DOI：
10.1126/science.aaf5573
发表时间：
2016-08-05
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Abudayyeh OO;Gootenberg JS;Konermann S;Joung J;Slaymaker IM;Cox DB;Shmakov S;Makarova KS;Semenova E;Minakhin L;Severinov K;Regev A;Lander ES;Koonin EV;Zhang F
通讯作者：
Zhang F
影响因子：
46.9
作者：
Doench JG;Fusi N;Sullender M;Hegde M;Vaimberg EW;Donovan KF;Smith I;Tothova Z;Wilen C;Orchard R;Virgin HW;Listgarten J;Root DE
通讯作者：
Root DE
影响因子：
48
作者：
Crosetto, Nicola;Mitra, Abhishek;Silva, Maria Joao;Bienko, Magda;Dojer, Norbert;Wang, Qi;Karaca, Elif;Chiarle, Roberto;Skrzypczak, Magdalena;Ginalski, Krzysztof;Pasero, Philippe;Rowicka, Maga;Dikic, Ivan
通讯作者：
Dikic, Ivan
影响因子：
64.8
作者：
Ghandi, Mahmoud;Huang, Franklin W.;Sellers, William R.
通讯作者：
Sellers, William R.
影响因子：
64.8
作者：
Ackerman, Cheri M.;Myhrvold, Cameron;Sabeti, Pardis C.
通讯作者：
Sabeti, Pardis C.