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Abstract Cas9 is an RNA-guided endonuclease in the bacterial CRISPR–Cas immune system and a popular tool for genome editing. The commonly used Streptococcus pyogenes Cas9 (SpCas9) is relatively non-specific and prone to off-target genome editing. Other Cas9 orthologs and engineered variants of SpCas9 have been reported to be more specific. However, previous studies have focused on specificity of double-strand break (DSB) or indel formation, potentially overlooking alternative cleavage activities of these Cas9 variants. In this study, we employed in vitro cleavage assays of target libraries coupled with high-throughput sequencing to systematically compare cleavage activities and specificities of two natural Cas9 variants (SpCas9 and Staphylococcus aureus Cas9) and three engineered SpCas9 variants (SpCas9 HF1, HypaCas9 and HiFi Cas9). We observed that all Cas9s tested could cleave target sequences with up to five mismatches. However, the rate of cleavage of both on-target and off-target sequences varied based on target sequence and Cas9 variant. In addition, SaCas9 and engineered SpCas9 variants nick targets with multiple mismatches but have a defect in generating a DSB, while SpCas9 creates DSBs at these targets. Overall, these differences in cleavage rates and DSB formation may contribute to varied specificities observed in genome editing studies. 

Abstract Motivation As the cost of sequencing decreases, the amount of data being deposited into public repositories is increasing rapidly. Public databases rely on the user to provide metadata for each submission that is prone to user error. Unfortunately, most public databases, such as non-redundant (NR), rely on user input and do not have methods for identifying errors in the provided metadata, leading to the potential for error propagation. Previous research on a small subset of the non-redundant (NR) database analyzed misclassification based on sequence similarity. To the best of our knowledge, the amount of misclassification in the entire database has not been quantified. We propose a heuristic method to detect potentially misclassified taxonomic assignments in the NR database. We applied a curation technique and quality control to find the most probable taxonomic assignment. Our method incorporates provenance and frequency of each annotation from manually and computationally created databases and clustering information at 95% similarity. Results We found more than 2 million potentially taxonomically misclassified proteins in the NR database. Using simulated data, we show a high precision of 97% and a recall of 87% for detecting taxonomically misclassified proteins. The proposed approach and findings could also be applied to other databases. Availability Source code, dataset, documentation, Jupyter notebooks, and Docker container are available at https://github.com/boalang/nr. Supplementary information Supplementary data are available at Bioinformatics online.