piCRISPR:用于CRISPR/Cas9脱靶切割预测的物理信息深度学习模型

Florian Störtz, Jeffrey K. Mak, Peter Minary
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引用次数: 0

摘要

CRISPR/Cas可编程核酸酶系统在基因编辑领域已经变得无处不在。随着开发的进展,体内治疗性基因编辑的应用越来越触手可及,但受到不必要编辑可能产生的副作用的限制。因此,近年来,在从永生化细胞系获得的体外切割测定数据上训练的脱靶预测算法不断发展。研究表明,与实验表观遗传学特征相比,尽管计算的物理信息特征与切割活性具有相当大的相关性,但迄今为止尚未得到充分利用。在这里,我们实现了最先进的深度学习算法和特征编码,用于脱靶预测,重点是捕捉切割位点生物环境的物理信息特征,从而确定了我们的方法piCRISPR。特征是从大型、多样化的crisprSQL脱靶切割数据集中获得的。我们发现,我们表现最好的模型强调了序列上下文和染色质可及性对切割预测的重要性,并与文献标准预测性能相比较。我们进一步表明,我们新颖的环境敏感特征对于准确预测序列相同的基因座对至关重要,使其与临床指南设计高度相关。源代码和经过训练的模型可以在github.com/florians/picrispr上找到。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
piCRISPR: Physically informed deep learning models for CRISPR/Cas9 off-target cleavage prediction

CRISPR/Cas programmable nuclease systems have become ubiquitous in the field of gene editing. With progressing development, applications in in vivo therapeutic gene editing are increasingly within reach, yet limited by possible adverse side effects from unwanted edits. Recent years have thus seen continuous development of off-target prediction algorithms trained on in vitro cleavage assay data gained from immortalised cell lines. It has been shown that in contrast to experimental epigenetic features, computed physically informed features are so far underutilised despite bearing considerably larger correlation with cleavage activity. Here, we implement state-of-the-art deep learning algorithms and feature encodings for off-target prediction with emphasis on physically informed features that capture the biological environment of the cleavage site, hence terming our approach piCRISPR. Features were gained from the large, diverse crisprSQL off-target cleavage dataset. We find that our best-performing models highlight the importance of sequence context and chromatin accessibility for cleavage prediction and compare favourably with literature standard prediction performance. We further show that our novel, environmentally sensitive features are crucial to accurate prediction on sequence-identical locus pairs, making them highly relevant for clinical guide design. The source code and trained models can be found ready to use at github.com/florianst/picrispr.

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来源期刊
Artificial intelligence in the life sciences
Artificial intelligence in the life sciences Pharmacology, Biochemistry, Genetics and Molecular Biology (General), Computer Science Applications, Health Informatics, Drug Discovery, Veterinary Science and Veterinary Medicine (General)
CiteScore
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