SeqCor: correct the effect of guide RNA sequences in clustered regularly interspaced short palindromic repeats/Cas9...

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• CRISPR/Cas9 screening is a powerful tool for identifying functional genetic elements across diverse cell types and organisms.
• The efficacy of CRISPR/Cas9 screening can be significantly influenced by the specific sequences of guide RNAs (gRNAs) within screening libraries.
• Existing methods lack a standardized approach to correct for gRNA sequence-driven biases in screening data.

Purpose of the Study:

• To develop a computational tool, SeqCor, to address and correct for biases in CRISPR/Cas9 screening data.
• To enable researchers to account for the influence of gRNA sequence composition on screening outcomes.
• To automate the identification of sequence features impacting single-guide RNA knockout efficiency.

Main Methods:

• Utilized public CRISPR/Cas9 screening experiment data to analyze the impact of gRNA sequences.
• Developed SeqCor, an open-source programming bundle for bias correction in CRISPR/Cas9 screening.
• Integrated a machine learning approach within SeqCor to extract sequence features affecting gRNA knockout efficiency.

Main Results:

• Demonstrated that gRNA sequences within libraries significantly influence CRISPR/Cas9 screening outcomes.
• SeqCor effectively automates the correction of gRNA sequence-related biases in screening data.
• Identified key sequence features that correlate with single-guide RNA knockout efficiency.

Conclusions:

• SeqCor provides a valuable solution for mitigating sequence-dependent biases in CRISPR/Cas9 screening.
• The developed software enhances the reliability and accuracy of functional genomics screens.
• SeqCor is expected to be a beneficial resource for the broader CRISPR/Cas9 screening community.