A python-based pipeline for optimized sgRNA target-site identification in CRISPR-Cas9 gene knockout

Abstract

This study presents the development of a novel Python-based pipeline for the identification of optimal single-guide RNA (sgRNA) target sites in CRISPR-Cas9 gene knockout applications. The pipeline offers a comprehensive analysis framework for targeting the Myostatin gene in Gallus gallus, assessing both the efficiency and specificity of potential sgRNA sites. By integrating state-of-the-art bioinformatics tools and databases during the design phase, the pipeline has been rigorously tested across various genetic models, demonstrating superior performance relative to existing software. Notably, our pipeline predicted a maximum efficiency of 100% in targeting the Myostatin gene, outperforming the CHOPCHOP and E-CRISP tools by identifying novel, high-potential target sites. Additionally, the pipeline’s user-friendly interface and interactive visualization capabilities enhance its accessibility, making it an invaluable resource for researchers and biotechnological applications in CRISPR-Cas9 genome editing. This work aims to advance gene editing precision, streamline workflows, and establish a new benchmark for genetic engineering technologies.

Keywords

• CRISPR-Cas9
• sgRNA
• Target site identification
• Genome editing
• Python bioinformatics tools

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