ArticleBMC bioinformatics2020
Deep learning improves the ability of sgRNA off-target propensity prediction.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed, 76 citations in OpenAlex.
- CRISPR-MBTF: a multi-branch transformer fusion framework for CRISPR-Cas9 off-target prediction.Briefings in bioinformatics · 2026Article
- CrisprPr: a hybrid-driven framework for CRISPR/Cas9 off-target prediction with analysis of prior-information updates.Briefings in bioinformatics · 2026Article
- An overview of CRISPR-artificial intelligence theranostics: Current and emerging applications.Biomaterials translational · 2026Review
- CRISPR-GATE: a one-stop repository and guide to computational resources for genome editing experimentation.Briefings in bioinformatics · 2025Review
- From Code to Life: The AI-Driven Revolution in Genome Editing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Deep Learning Based Models for CRISPR/Cas Off-Target Prediction.Small methods · 2025Review
- 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
- AI-driven advances in plant biotechnology: sharpening the edge of plant tissue culture and genome editing.Frontiers in plant science · 2025Review
- Predicting CRISPR-Cas9 off-target effects in human primary cells using bidirectional LSTM with BERT embedding.Bioinformatics advances · 2025Article
- Unraveling the future of genomics: CRISPR, single-cell omics, and the applications in cancer and immunology.Frontiers in genome editing · 2025Review
- DTMP-prime: A deep transformer-based model for predicting prime editing efficiency and PegRNA activity.Molecular therapy. Nucleic acids · 2024Article
- Learning to quantify uncertainty in off-target activity for CRISPR guide RNAs.Nucleic acids research · 2024Article
- Generating, modeling and evaluating a large-scale set of CRISPR/Cas9 off-target sites with bulges.Nucleic acids research · 2024Article
- Review
- CRISPR-DIPOFF: an interpretable deep learning approach for CRISPR Cas-9 off-target prediction.Briefings in bioinformatics · 2024Article
- Using traditional machine learning and deep learning methods for on- and off-target prediction in CRISPR/Cas9: a review.Briefings in bioinformatics · 2023Review
- Hybrid Multitask Learning Reveals Sequence Features Driving Specificity in the CRISPR/Cas9 System.Biomolecules · 2023Article
- The application and progression of CRISPR/Cas9 technology in ophthalmological diseases.Eye (London, England) · 2023Review
- Extru-seq: a method for predicting genome-wide Cas9 off-target sites with advantages of both cell-based and in vitro approaches.Genome biology · 2023Article
- Deep learning in CRISPR-Cas systems: a review of recent studies.Frontiers in bioengineering and biotechnology · 2023Review
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundCRISPR/Cas9 system, as the third-generation genome editing technology, has been widely applied in target gene repair and gene expression regulation. Selection of appropriate sgRNA can improve the on-target knockout efficacy of CRISPR/Cas9 system with high sensitivity and specificity. However, when CRISPR/Cas9 system is operating, unexpected cleavage may occur at some sites, known as off-target. Presently, a number of prediction methods have been developed to predict the off-target propensity of sgRNA at specific DNA fragments. Most of them use artificial feature extraction operations and machine learning techniques to obtain off-target scores. With the rapid expansion of off-target data and the rapid development of deep learning theory, the existing prediction methods can no longer satisfy the prediction accuracy at the clinical level.
resultsHere, we propose a prediction method named CnnCrispr to predict the off-target propensity of sgRNA at specific DNA fragments. CnnCrispr automatically trains the sequence features of sgRNA-DNA pairs with GloVe model, and embeds the trained word vector matrix into the deep learning model including biLSTM and CNN with five hidden layers. We conducted performance verification on the data set provided by DeepCrispr, and found that the auROC and auPRC in the "leave-one-sgRNA-out" cross validation could reach 0.957 and 0.429 respectively (the Pearson value and spearman value could reach 0.495 and 0.151 respectively under the same settings).
conclusionOur results show that CnnCrispr has better classification and regression performance than the existing states-of-art models. The code for CnnCrispr can be freely downloaded from https://github.com/LQYoLH/CnnCrispr.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.