CRSeek: a Python module for facilitating complicated CRISPR design strategies
Author and article information
Abstract
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.
Cite this as
2018. CRSeek: a Python module for facilitating complicated CRISPR design strategies. PeerJ Preprints 6:e27094v1 https://doi.org/10.7287/peerj.preprints.27094v1Author comment
This is a submission to PeerJ for review.
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Competing Interests
The authors declare that they have no competing interests.
Author Contributions
Will Dampier conceived and designed the experiments, analyzed the data, contributed reagents/materials/analysis tools, prepared figures and/or tables, authored or reviewed drafts of the paper, approved the final draft.
Cheng-Han Chung analyzed the data, authored or reviewed drafts of the paper, approved the final draft.
Neil T Sullivan performed the experiments, contributed reagents/materials/analysis tools, authored or reviewed drafts of the paper, approved the final draft.
Andrew J Atkins performed the experiments, contributed reagents/materials/analysis tools, authored or reviewed drafts of the paper, approved the final draft.
Michael R Nonnemacher conceived and designed the experiments, authored or reviewed drafts of the paper, approved the final draft.
Brian Wigdahl conceived and designed the experiments, authored or reviewed drafts of the paper, approved the final draft.
Human Ethics
The following information was supplied relating to ethical approvals (i.e., approving body and any reference numbers):
Patients in the Drexel Medicine CARES cohort were recruited under protocol 1201000748
278 (Brian Wigdahl, PI) approved by the Drexel University IRB board.
Data Deposition
The following information was supplied regarding data availability:
All code is available for review at https://github.com/DamLabResources/crseek
Funding
This work was supported by National Institute of Health grants (No. MH110360, MH092177, MH079785). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
