CRISPRbase

CRISPRbase provides a curated database and analytical resource for evaluation and optimization of CRISPR-Cas base editing (BE) systems, aggregating 1,252,935 base editing outcome records across more than 50 cell types from 17 species to support assessment of editing precision, efficiency, and product purity.

Key Features:

• Comprehensive dataset: Aggregates 1,252,935 records of base editing outcomes collected across more than 50 cell types from 17 species.
• Annotation and functional prediction: Incorporates multiple annotations and functional predictions for edited sites to evaluate potential impacts on gene function.
• sgRNA BLAST: Provides a BLAST system tailored for single-guide RNA sequences for sequence-level queries and comparison.
• Editing metrics analysis: Systematically analyzes editing window, editing efficiency, and product purity for different BE systems.
• Off-target characterization: Analyzes off-target effects, reporting that over half of cancer-related off-target mutations are non-synonymous and assessing driver versus passenger frequencies (15.13% driver, 84.87% passenger).
• Comparative evaluation of BE systems: Evaluates putative editing precision of different base editing systems to support comparative analyses.

Scientific Applications:

• Precision assessment: Quantitatively assess and compare editing precision, efficiency, and product purity across BE systems.
• BE design selection: Inform selection and optimization of base editor designs for functional studies and to minimize detrimental byproducts.
• Off-target impact analysis in cancer: Characterize cancer-related off-target mutations including non-synonymous changes and driver versus passenger classification.
• Cross-species and cell-type analyses: Enable comparative analyses of base editing outcomes across multiple cell types and 17 species.

Methodology:

Integrates 1,252,935 base editing outcome records across >50 cell types and 17 species, applies multiple annotations and functional predictions, provides a BLAST system for single-guide RNA sequence queries, and performs computational analysis of off-target mutations including classification of non-synonymous and driver versus passenger frequencies.