DeepCRISPR: optimized CRISPR guide RNA design by deep learning

Abstract A major challenge for effective application of CRISPR systems is to accurately predict the single guide RNA (sgRNA) on-target knockout efficacy and off-target profile, which would facilitate the optimized design of sgRNAs with high sensitivity and specificity. Here we present DeepCRISPR, a comprehensive computational platform to unify sgRNA on-target and off-target site prediction into one framework with deep learning, surpassing available state-of-the-art in silico tools. In addition, DeepCRISPR fully automates the identification of sequence and epigenetic features that may affect sgRNA knockout efficacy in a data-driven manner. DeepCRISPR is available at http://www.deepcrispr.net/

Additional file 8: of DeepCRISPR: optimized CRISPR guide RNA design by deep learning

Data augmentation for on-target dataset. (XLSX 2939 kb

Additional file 1: of DeepCRISPR: optimized CRISPR guide RNA design by deep learning

A comprehensive list of hypothesis-based and learning-based sgRNA on-target design tools and the selected candidates in our comparison study. (XLSX 14 kb

Additional file 3: of DeepCRISPR: optimized CRISPR guide RNA design by deep learning

Comparison of sgRNA on-target efficacy predictions in an independent dataset with Spearman correlation. (XLSX 48 kb

Additional file 2: of DeepCRISPR: optimized CRISPR guide RNA design by deep learning

Detailed comparison results for sgRNA on-target efficacy prediction and off-target site prediction. (XLSX 16 kb

Additional file 7: of DeepCRISPR: optimized CRISPR guide RNA design by deep learning

The dataset from the human K562 cell type used for the study of sgRNA off-target profile prediction. (XLSX 19 kb