Unexpected CRISPR off-target mutation pattern in vivo are not typically germline-like [preprint]

A computationally evolutionary investigation was performed to re-analyze the WGS data of the two studies published in Nature Methods (2015, 2017) with opposite conclusions on CRISPR off-target mutations. Our analysis concluded that the so-called unexpected SNVs pattern obtained by the study of Schaefer et al. are not typically germline-like. Some of unusual and unidentified mutations may arise, but the real reasons remain to be explored. Based on the available data and a direct comparison of the two studies, we presented two possible reasons and future re-analysis directions that may contribute to such different conclusions. To characterize the authentic CRISPR-mediated mutations, we are required to have appropriate controls to rule out other sources of mutations, which will be needed for benchmarking of targeting safety of CRISPR-based gene therapy

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 1: of MetaTopics: an integration tool to analyze microbial community profile by topic model

Supplementary methods, figures and tables. (DOCX 799 kb

Additional file 4: of DeepCRISPR: optimized CRISPR guide RNA design by deep learning

Detailed learned weights for feature saliency map with filtering of those positions without statistical significant by Fisher’s exact test. (XLSX 6 kb

Additional file 6: of DeepCRISPR: optimized CRISPR guide RNA design by deep learning

The dataset from the human 293-related cell type used for the study of sgRNA off-target profile prediction. (XLSX 49 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 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 5: of DeepCRISPR: optimized CRISPR guide RNA design by deep learning

The datasets used for the study of sgRNA on-target efficacy prediction. (XLSX 959 kb