Improved CRISPR/Cas9 off-target prediction with DNABERT and epigenetic features
Bibliographic record
Abstract
CRISPR/Cas9 is a powerful genome editing tool, but its clinical application is hindered by off-target effects. Accurate computational prediction of these unintended edits is crucial for ensuring the safety and efficacy of therapeutic applications. While various deep learning models have been developed, most are trained only on task-specific data, failing to leverage the vast knowledge embedded in entire genomes. To address this limitation, we introduce a novel approach that integrates DNABERT, a deep learning model pre-trained on the human genome, with epigenetic features (H3K4me3, H3K27ac, and ATAC-seq). We conducted a comprehensive benchmark of our model, DNABERT-Epi, against five state-of-the-art methods across seven distinct off-target datasets. Our results demonstrate that the pre-trained DNABERT-based models achieve competitive or even superior performance. Rigorous ablation studies quantitatively confirmed that both genomic pre-training and the integration of epigenetic features are critical factors that significantly enhance predictive accuracy. Furthermore, by applying advanced interpretability techniques (SHAP and Integrated Gradients), we identified the specific epigenetic marks and sequence-level patterns that influence the model's predictions, offering insights into its decision-making process. This study is the first to establish the significant potential of a pre-trained DNA foundation model for CRISPR/Cas9 off-target prediction. Our findings underscore that leveraging both large-scale genomic knowledge and multi-modal data is a key strategy for advancing the development of safer genome editing tools.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".