Enhancing CRISPR-Cas9 gRNA efficiency prediction by data integration and deep learning.
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ABSTRACT:
SUBMITTER: Xiang X
PROVIDER: S-EPMC8163799 | biostudies-literature | 2021 May
REPOSITORIES: biostudies-literature
Publications
Enhancing CRISPR-Cas9 gRNA efficiency prediction by data integration and deep learning.
Xiang Xi X Corsi Giulia I GI Anthon Christian C Qu Kunli K Pan Xiaoguang X Liang Xue X Han Peng P Dong Zhanying Z Liu Lijun L Zhong Jiayan J Ma Tao T Wang Jinbao J Zhang Xiuqing X Jiang Hui H Xu Fengping F Liu Xin X Xu Xun X Wang Jian J Yang Huanming H Bolund Lars L Church George M GM Lin Lin L Gorodkin Jan J Luo Yonglun Y
Nature communications 20210528 1
The design of CRISPR gRNAs requires accurate on-target efficiency predictions, which demand high-quality gRNA activity data and efficient modeling. To advance, we here report on the generation of on-target gRNA activity data for 10,592 SpCas9 gRNAs. Integrating these with complementary published data, we train a deep learning model, CRISPRon, on 23,902 gRNAs. Compared to existing tools, CRISPRon exhibits significantly higher prediction performances on four test datasets not overlapping with trai ...[more]
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Project description:The clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated protein 9 (Cas9) system has become a successful and promising technology for gene-editing. To facilitate its effective application, various computational tools have been developed. These tools can assist researchers in the guide RNA (gRNA) design process by predicting cleavage efficiency and specificity and excluding undesirable targets. However, while many tools are available, assessment of their application scenarios and performance benchmarks are limited. Moreover, new deep learning tools have been explored lately for gRNA efficiency prediction, but have not been systematically evaluated. Here, we discuss the approaches that pertain to the on-target activity problem, focusing mainly on the features and computational methods they utilize. Furthermore, we evaluate these tools on independent datasets and give some suggestions for their usage. We conclude with some challenges and perspectives about future directions for CRISPR-Cas9 guide design.
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Project description:MotivationThe CRISPR/Cas9 system is widely used for genome editing. The editing efficiency of CRISPR/Cas9 is mainly determined by the guide RNA (gRNA). Although many computational algorithms have been developed in recent years, it is still a challenge to select optimal bioinformatics tools for gRNA design in different experimental settings.ResultsWe performed a comprehensive comparison analysis of 15 public algorithms for gRNA design, using 16 experimental gRNA datasets. Based on this analysis, we identified the top-performing algorithms, with which we further implemented various computational strategies to build ensemble models for performance improvement. Validation analysis indicates that the new ensemble model had improved performance over any individual algorithm alone at predicting gRNA efficacy under various experimental conditions.Availability and implementationThe new sgRNA design tool is freely accessible as a web application via https://crisprdb.org. The source code and stand-alone version is available at Figshare (https://doi.org/10.6084/m9.figshare.21295863) and Github (https://github.com/wang-lab/CRISPRDB).Supplementary informationSupplementary data are available at Bioinformatics online.
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Project description:Life science studies involving clustered regularly interspaced short palindromic repeat (CRISPR) editing generally apply the best-performing guide RNA (gRNA) for a gene of interest. Computational models are combined with massive experimental quantification on synthetic gRNA-target libraries to accurately predict gRNA activity and mutational patterns. However, the measurements are inconsistent between studies due to differences in the designs of the gRNA-target pair constructs, and there has not yet been an integrated investigation that concurrently focuses on multiple facets of gRNA capacity. In this study, we analyzed the DNA double-strand break (DSB)-induced repair outcomes and measured SpCas9/gRNA activities at both matched and mismatched locations using 926,476 gRNAs covering 19,111 protein-coding genes and 20,268 non-coding genes. We developed machine learning models to forecast the on-target cleavage efficiency (AIdit_ON), off-target cleavage specificity (AIdit_OFF), and mutational profiles (AIdit_DSB) of SpCas9/gRNA from a uniformly collected and processed dataset by deep sampling and massively quantifying gRNA capabilities in K562 cells. Each of these models exhibited superlative performance in predicting SpCas9/gRNA activities on independent datasets when benchmarked with previous models. A previous unknown parameter was also empirically determined regarding the "sweet spot" in the size of datasets used to establish an effective model to predict gRNA capabilities at a manageable experimental scale. In addition, we observed cell type-specific mutational profiles and were able to link nucleotidylexotransferase as the key factor driving these outcomes. These massive datasets and deep learning algorithms have been implemented into the user-friendly web service http://crispr-aidit.com to evaluate and rank gRNAs for life science studies.
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Project description: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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Project description:MotivationCRISPR/Cas9 technology has been revolutionizing the field of gene editing in recent years. Guide RNAs (gRNAs) enable Cas9 proteins to target specific genomic loci for editing. However, editing efficiency varies between gRNAs. Thus, computational methods were developed to predict editing efficiency for any gRNA of interest. High-throughput datasets of Cas9 editing efficiencies were produced to train machine-learning models to predict editing efficiency. However, these high-throughput datasets have low correlation with functional and endogenous editing. Another difficulty arises from the fact that functional and endogenous editing efficiency is more difficult to measure, and as a result, functional and endogenous datasets are too small to train accurate machine-learning models on.ResultsWe developed DeepCRISTL, a deep-learning model to predict the on-target efficiency given a gRNA sequence. DeepCRISTL takes advantage of high-throughput datasets to learn general patterns of gRNA on-target editing efficiency, and then uses transfer learning (TL) to fine-tune the model and fit it to the functional and endogenous prediction task. We pre-trained the DeepCRISTL model on more than 150 000 gRNAs, produced through the DeepHF study as a high-throughput dataset of three Cas9 enzymes. We improved the DeepHF model by multi-task and ensemble techniques and achieved state-of-the-art results over each of the three enzymes: up to 0.89 in Spearman correlation between predicted and measured on-target efficiencies. To fine-tune model weights to predict on-target efficiency of functional or endogenous datasets, we tested several TL approaches, with gradual learning being the overall best performer, both when pre-trained on DeepHF and when pre-trained on CRISPROn, another high-throughput dataset. DeepCRISTL outperformed state-of-the-art methods on all functional and endogenous datasets. Using saliency maps, we identified and compared the important features learned by the model in each dataset. We believe DeepCRISTL will improve prediction performance in many other CRISPR/Cas9 editing contexts by leveraging TL to utilize both high-throughput datasets, and smaller and more biologically relevant datasets, such as functional and endogenous datasets.Availability and implementationDeepCRISTL is available via github.com/OrensteinLab/DeepCRISTL.Supplementary informationSupplementary data are available at Bioinformatics online.
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Project description:MotivationCRISPR/Cas9 technology has been revolutionizing the field of gene editing. Guide RNAs (gRNAs) enable Cas9 proteins to target specific genomic loci for editing. However, editing efficiency varies between gRNAs and so computational methods were developed to predict editing efficiency for any gRNA of interest. High-throughput datasets of Cas9 editing efficiencies were produced to train machine-learning models to predict editing efficiency. However, these high-throughput datasets have a low correlation with functional and endogenous datasets, which are too small to train accurate machine-learning models on.ResultsWe developed DeepCRISTL, a deep-learning model to predict the editing efficiency in a specific cellular context. DeepCRISTL takes advantage of high-throughput datasets to learn general patterns of gRNA editing efficiency and then fine-tunes the model on functional or endogenous data to fit a specific cellular context. We tested two state-of-the-art models trained on high-throughput datasets for editing efficiency prediction, our newly improved DeepHF and CRISPRon, combined with various transfer-learning approaches. The combination of CRISPRon and fine-tuning all model weights was the overall best performer. DeepCRISTL outperformed state-of-the-art methods in predicting editing efficiency in a specific cellular context on functional and endogenous datasets. Using saliency maps, we identified and compared the important features learned by DeepCRISTL across cellular contexts. We believe DeepCRISTL will improve prediction performance in many other CRISPR/Cas9 editing contexts by leveraging transfer learning to utilize both high-throughput datasets and smaller and more biologically relevant datasets.Availability and implementationDeepCRISTL is available via https://github.com/OrensteinLab/DeepCRISTL.
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Project description:The CRISPR/Cas9 system is a powerful, revolutionary tool for genome editing. However, it is not without limitations. There are PAM-free and CRISPR-tolerant regions that cannot be modified by the standard CRISPR/Cas9 system, and off-target activity impedes its broader applications. To avoid these drawbacks, we developed a very simple CRISPR/Cas9-assisted gRNA-free one-step (CAGO) genome editing technique which does not require the construction of a plasmid to express a specific gRNA. Instead, a universal N20 sequence with a very high targeting efficiency is inserted into the E. coli chromosome by homologous recombination, which in turn undergoes a double-stranded break by CRISPR/Cas9 and induces an intra-chromosomal recombination event to accomplish the editing process. This technique was shown to be able to edit PAM-free and CRISPR-tolerant regions with no off-target effects in Escherichia coli. When applied to multi-locus editing, CAGO was able to modify one locus in two days with a near 100% editing efficiency. Furthermore, modified CAGO was used to edit large regions of up to 100 kbp with at least 75% efficiency. Finally, genome editing by CAGO only requires a transformation procedure and the construction of a linear donor DNA cassette, which was further simplified by applying a modular design strategy. Although the technique was established in E. coli, it should be applicable to other organisms with only minor modifications.
| S-EPMC5709385 | biostudies-literature
Project description:The CRISPR/Cas9 system is an RNA guided nuclease system that evolved as a mechanism of adaptive immunity in bacteria. This system has been adopted for numerous genome engineering applications in research and recently, therapeutics. The CRISPR/Cas9 system has been largely implemented by delivery of Cas9 as protein, RNA, or plasmid along with a chimeric crRNA-tracrRNA guide RNA (gRNA) under the expression of a pol III promoter, such as U6. Using this approach, multiplex genome engineering has been achieved by delivering several U6-gRNA plasmids targeting multiple loci. However, this approach is limited due to the efficiently of delivering multiple plasmids to a single cell at one time. To augment the capability and accessibility of multiplexed genome engineering, we developed an efficient golden gate based method to assemble gRNAs linked by optimal Csy4 ribonuclease sequences to deliver up to 10 gRNAs as a single gRNA array transcript. Here we report the optimal expression of our guide RNA array under a strong pol II promoter. This system can be implemented alongside the myriad of CRISPR applications, allowing users to model complex biological processes requiring numerous gRNAs.
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