CRISPRO: identification of functional protein coding sequences based on genome editing dense mutagenesis

Area of Science:

• Genomics and Proteomics
• Bioinformatics and Computational Biology
• CRISPR/Cas9 Gene Editing Technologies

Background:

• CRISPR/Cas9 pooled screening enables large-scale genetic perturbations for functional genomics studies.
• Analysis of complex screening datasets requires sophisticated computational tools for mapping and interpretation.
• Existing tools lack comprehensive functionality for correlating guide RNA activity with genomic and protein structural data.

Purpose of the Study:

• To develop CRISPRO, a computational pipeline for analyzing CRISPR/Cas9 screening data.
• To enable systematic mapping of guide RNA functional scores to genomic, transcript, and protein levels.
• To facilitate the generation of genotype-phenotype maps for exploring protein structure-function relationships.

Main Methods:

• Development of the CRISPRO computational pipeline.
• Integration of guide RNA functional scores with genomic, transcriptomic, and protein coordinate data.
• Application of machine learning models utilizing CRISPRO features to predict guide RNA efficacy.

Main Results:

• CRISPRO provides a unique platform for visualizing and analyzing CRISPR/Cas9 screening data.
• Generated genotype-phenotype maps reveal potential structure-function relationships within proteins.
• Machine learning predictions based on CRISPRO features demonstrate improved accuracy in guide RNA efficacy assessment.

Conclusions:

• CRISPRO offers novel functionality for the comprehensive analysis of CRISPR/Cas9 pooled screening data.
• The tool aids in hypothesis generation regarding protein structure-function dynamics.
• CRISPRO enhances the predictive power of guide RNA efficacy, advancing CRISPR technology applications.