ArticleBMC bioinformatics2019
Prediction of sgRNA on-target activity in bacteria by deep learning.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Deep learning-driven prediction of on-target activity, off-target risk, and repair outcomes in CRISPR/Cas9: current landscape and multi-scale perspectives.Journal of translational medicine · 2026Review
- Better data for better predictions: data curation improves deep learning for sgRNA/Cas9 prediction.PeerJ · 2026Article
- A review of flavonoids at the crossroads of plant defense: integrating biotic and abiotic stress tolerance through AI- and CRISPR/Cas-guided metabolic reprogramming.Frontiers in plant science · 2026Review
- From Code to Life: The AI-Driven Revolution in Genome Editing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- DeepMEns: an ensemble model for predicting sgRNA on-target activity based on multiple features.Briefings in functional genomics · 2025Article
- The Evolution of Nucleic Acid-Based Diagnosis Methods from the (pre-)CRISPR to CRISPR era and the Associated Machine/Deep Learning Approaches in Relevant RNA Design.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Multiplex CRISPR-Cas Genome Editing: Next-Generation Microbial Strain Engineering.Journal of agricultural and food chemistry · 2024Review
- CRISPR interference screens reveal growth-robustness tradeoffs in Synechocystis sp. PCC 6803 across growth conditions.The Plant cell · 2023Article
- A generalizable Cas9/sgRNA prediction model using machine transfer learning with small high-quality datasets.Nature communications · 2023Article
- Using traditional machine learning and deep learning methods for on- and off-target prediction in CRISPR/Cas9: a review.Briefings in bioinformatics · 2023Review
- Modeling CRISPR-Cas13d on-target and off-target effects using machine learning approaches.Nature communications · 2023Article
- Computational design of CRISPR guide RNAs to enable strain-specific control of microbial consortia.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Deep learning in CRISPR-Cas systems: a review of recent studies.Frontiers in bioengineering and biotechnology · 2023Review
- Advancing genome editing with artificial intelligence: opportunities, challenges, and future directions.Frontiers in bioengineering and biotechnology · 2023Review
- CNN-XG: A Hybrid Framework for sgRNA On-Target Prediction.Biomolecules · 2022Article
- A systematic mapping study on machine learning techniques for the prediction of CRISPR/Cas9 sgRNA target cleavage.Computational and structural biotechnology journal · 2022Review
- Prediction of sgRNA Off-Target Activity in CRISPR/Cas9 Gene Editing Using Graph Convolution Network.Entropy (Basel, Switzerland) · 2021Article
- Prediction of CRISPR/Cas9 single guide RNA cleavage efficiency and specificity by attention-based convolutional neural networks.Computational and structural biotechnology journal · 2021Article
- Recent advances of genome editing and related technologies in China.Gene therapy · 2020Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOne of the main challenges for the CRISPR-Cas9 system is selecting optimal single-guide RNAs (sgRNAs). Recently, deep learning has enhanced sgRNA prediction in eukaryotes. However, the prokaryotic chromatin structure is different from eukaryotes, so models trained on eukaryotes may not apply to prokaryotes.
resultsWe designed and implemented a convolutional neural network to predict sgRNA activity in Escherichia coli. The network was trained and tested on the recently-released sgRNA activity dataset. Our convolutional neural network achieved excellent performance, yielding average Spearman correlation coefficients of 0.5817, 0.7105, and 0.3602, respectively for Cas9, eSpCas9 and Cas9 with a recA coding region deletion. We confirmed that the sgRNA prediction models trained on prokaryotes do not apply to eukaryotes and vice versa. We adopted perturbation-based approaches to analyze distinct biological patterns between prokaryotic and eukaryotic editing. Then, we improved the predictive performance of the prokaryotic Cas9 system by transfer learning. Finally, we determined that potential off-target scores accumulated on a genome-wide scale affect on-target activity, which could slightly improve on-target predictive performance.
conclusionsWe developed convolutional neural networks to predict sgRNA activity for wild type and mutant Cas9 in prokaryotes. Our results show that the prediction accuracy of our method is improved over state-of-the-art models.
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