CRSeek: a Python module for facilitating complicated CRISPR design strategies

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. 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.

Safe CRISPR-Cas9 Inhibition of HIV-1 with High Specificity and Broad-Spectrum Activity by Targeting LTR NF-κB Binding Sites

“…The CRSeek python module was used to calculate PVC and off-target cleavage sites adapting cas-offinder. 50 The analyses and figures can be reproduced using the python notebook.…”

“…We have developed CRSeek to account for on-target HIV-1 sequence variants from HIV-1-infected patients and off-target risk from the published human genome in the development pipeline. 50 The collected sequences of HIV-1 subtype B in the Los Alamos National Laboratory (LANL) database were used to predict the percent coverage of sequence variants for designed gRNAs. The screening of gRNAs that effectively damage HIV-1 structurally and/or functionally after proviral sequence edits is required for CRISPR-based anti-HIV-1 therapy to ascertain the therapeutic efficacy in the absence of provirus excision.…”

“…Percent variant coverage (PVC) across the total HIV-1 5′ LTR variants was calculated for gRNAs using CRSeek. 50 Counts of potential off-target sites were estimated by cas-offinder using human reference genome build GRCh37. Lead gRNAs with high PVC and low off-target counts were subjected to a test of efficacy at stopping HIV-1 transcription in HIV-1 indicator cell lines as well as off-target evaluation by GUIDE-seq.…”

“…An Experimental Overview of In Silico gRNA Screening and In Vitro Functional Testing for Identification of Optimized anti-HIV-1 gRNAsAll distinct 23-bp HIV-1 LTR sequences ending with two consecutive guanines were collected as a potential gRNA subset from HIV-1 subtype B sequences in the LANL database. Percent variant coverage (PVC) across the total HIV-1 5 0 LTR variants was calculated for gRNAs using CRSeek 50. Counts of potential off-target sites were estimated by cas-offinder using human reference genome build GRCh37.…”

Computational Design of gRNAs Targeting Genetic Variants Across HIV-1 Subtypes for CRISPR-Mediated Antiviral Therapy

“…Alpha levels were set at 5% for the Wald Tests for the significance of b coefficients (slope) from simple linear regression models. The CRSeek package was used to calculate percent variant coverage and potential off-target cleavage sites adapting cas-offinder (Dampier et al, 2018b).…”