CRSeek: a Python module for facilitating complicated CRISPR design strategies

With the popularization of the CRISPR-Cas gene editing system there has been an explosion of new techniques made possible by this versatile technology. However, the computational field has lagged behind with a current lack of computational tools for developing complicated CRISPR-Cas gene editing strategies. We present crseek, a Python package that provides a consistent application programming interface (API) for multiple cleavage prediction algorithms. Four popular cleavage prediction algorithms were implemented and further adapted to work on draft-quality genomes. Furthermore, since crseek mirrors the popular scikit-learn API, the package can be easily integrated as an upstream processing module for facilitating further CRISPR-Cas machine learning research. The package is fully integrated with the biopython package facilitating simple import, export, and manipulation of sequences before and after gene editing. This manuscript presents four common gene editing tasks that would be difficult with current tools but are easily performed with the crseek package. We believe this package will help bioinformaticians rapidly design complex CRISPR-Cas gene editing strategies and will be a useful addition to the field.