piCRISPR: Physically informed deep learning models for CRISPR/Cas9 off-target cleavage prediction

Störtz, Florian; Mak, Jeffrey; Mináry, Péter

doi:10.1016/j.ailsci.2023.100075

Cited by 7 publications

(9 citation statements)

References 42 publications

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“…Integrating them, either by data combination or by a meta-predictor that considers the predictions of models trained on these data may improve prediction performance. Second, the efficiency of assays for measuring the genome-wide activity of CRISPR/Cas9 nucleases in cellula is limited by chromatin accessibility and other epigenetic factors (31; 44; 45). Therefore, including such information in our models may provide more insights regarding their potential for future improvements.…”

Section: Discussionmentioning

confidence: 99%

“…Deep neural network architectures. We trained three deep-neural-network architectures (inspired by pi-CRISPR (44) and DeepHF (45) architectures) for offtarget classification and regression tasks, leveraging multilayer-perceptron (MLP), gated-recurrent-unit (GRU), and embedding techniques. The first architecture, which we denote by MLP-Emb, begins with an embedding layer of size 44, which transforms each 25-long one-hot-encoded aligned nucleotide pair into a 44-long real vector representation.…”

Section: B Our Deep Neural Network To Predict Otsmentioning

confidence: 99%

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Generating, modeling, and evaluating a large-scale set of CRISPR/Cas9 off-target sites with bulges

Yaish,

Orenstein

2023

Preprint

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CRISPR/Cas9 system is widely used in a broad range of gene-editing applications. While the CRISPR editing technique is quite accurate in the target region, there may be many unplanned offtarget sites (OTS). Consequently, a plethora of highthroughput experimental assays have been developed to measure OTS in a genome-wide manner. Based on these experimental data, computational methods have been developed to predict OTS given a guide RNA and a reference genome. However, these methods are highly inaccurate when considering OTS with bulges due to limited data compared to OTS without bulges. Recently, CHANGE-seq, a newin vitroexperimental technique to detect OTS, was used to produce a dataset of unprecedented scale and quality (more than 200,000 OTS over 110 guide RNAs). In addition, the same study includedin cellulaGUIDE-seq experiments for 58 of the guide RNAs. But, while the CHANGE-seq data included more than 20,000 OTS with bulges, the GUIDE-seq data did not include any OTS with bulges. Here, we fill this gap by generating the most comprehensive GUIDE-seq dataset with bulges, and training and evaluating state-of-the-art machine-learning models that consider OTS with bulges. We first reprocessed the publicly available experimental raw data of the CHANGE-seq study to generate 20 new GUIDE-seq datasets, and more than 450 OTS with bulges among the original and new GUIDE-seq experiments. We then trained various machinelearning models, evaluated their performance on multiple datasets, and demonstrated their state-of-the-art performance bothin vitroandin cellula. Last, we visualized the key features learned by our models on OTS with bulges. Our data and models will be instrumental to any future off-target study considering bulges.

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Section: Discussionmentioning

confidence: 99%

Section: B Our Deep Neural Network To Predict Otsmentioning

confidence: 99%

Generating, modeling, and evaluating a large-scale set of CRISPR/Cas9 off-target sites with bulges

Yaish,

Orenstein

2023

Preprint

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“…Physical attributes of the genome such as chromatin accessibility and DNA methylation pattern features, currently underutilized in AI models, provide valuable insights into the three-dimensional structure and packaging of DNA in the cell, which can impact the accessibility of specific genomic regions for gene editing. In their study, Störtz et al (2023) developed a method known as piCRISPR, which considers a combination of sequence-based attributes and physically informed features, including factors like chromatin accessibility and DNA methylation. Through an extensive assessment using a substantial dataset of CRISPR/Cas9 editing occurrences, piCRISPR exhibited superior performance compared to all other existing prediction methods for off-target cleavage activity.…”

Section: Ai In Grna Design For Crispr/cas-based Genome Editingmentioning

confidence: 99%

Section: Integration Into Healthcare Systemsmentioning

confidence: 99%

Advancing genome editing with artificial intelligence: opportunities, challenges, and future directions

Dixit,

Kumar,

Srinivasan

et al. 2024

Front. Bioeng. Biotechnol.

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Clustered regularly interspaced short palindromic repeat (CRISPR)-based genome editing (GED) technologies have unlocked exciting possibilities for understanding genes and improving medical treatments. On the other hand, Artificial intelligence (AI) helps genome editing achieve more precision, efficiency, and affordability in tackling various diseases, like Sickle cell anemia or Thalassemia. AI models have been in use for designing guide RNAs (gRNAs) for CRISPR-Cas systems. Tools like DeepCRISPR, CRISTA, and DeepHF have the capability to predict optimal guide RNAs (gRNAs) for a specified target sequence. These predictions take into account multiple factors, including genomic context, Cas protein type, desired mutation type, on-target/off-target scores, potential off-target sites, and the potential impacts of genome editing on gene function and cell phenotype. These models aid in optimizing different genome editing technologies, such as base, prime, and epigenome editing, which are advanced techniques to introduce precise and programmable changes to DNA sequences without relying on the homology-directed repair pathway or donor DNA templates. Furthermore, AI, in collaboration with genome editing and precision medicine, enables personalized treatments based on genetic profiles. AI analyzes patients’ genomic data to identify mutations, variations, and biomarkers associated with different diseases like Cancer, Diabetes, Alzheimer’s, etc. However, several challenges persist, including high costs, off-target editing, suitable delivery methods for CRISPR cargoes, improving editing efficiency, and ensuring safety in clinical applications. This review explores AI’s contribution to improving CRISPR-based genome editing technologies and addresses existing challenges. It also discusses potential areas for future research in AI-driven CRISPR-based genome editing technologies. The integration of AI and genome editing opens up new possibilities for genetics, biomedicine, and healthcare, with significant implications for human health.

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“…CRISPR-GE是一个集合多种基因编辑设计工具的网页平台, 其offTarget模块可用于快速预测目的物种基因组中的脱靶位点 [59] . Stortz等人 [127] 使用深度学习的方法, 对CRISPR/Cas系统的大型脱靶切割…”

Section: 预测脱靶位点的软件工具unclassified

基因编辑设计和分析辅助工具的研究进展

Xie,

Li,

Zhang

et al. 2024

Chin. Sci. Bull.

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piCRISPR: Physically informed deep learning models for CRISPR/Cas9 off-target cleavage prediction

Cited by 7 publications

References 42 publications

Generating, modeling, and evaluating a large-scale set of CRISPR/Cas9 off-target sites with bulges

Generating, modeling, and evaluating a large-scale set of CRISPR/Cas9 off-target sites with bulges

Advancing genome editing with artificial intelligence: opportunities, challenges, and future directions

基因编辑设计和分析辅助工具的研究进展

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