SeqCor

SeqCor corrects sequence-dependent biases in CRISPR/Cas9-based screening experiments by extracting gRNA sequence features and applying machine learning to adjust observed single-guide RNA knockout effects.

Key Features:

• Machine learning-based bias correction: Uses machine learning algorithms to model and correct variations in gRNA sequence composition that influence screening outcomes.
• Sequence feature extraction: Automates extraction of sequence features from gRNA libraries that may impact single-guide RNA knockout efficiency.
• gRNA library composition analysis: Organizes and analyzes the composition of gRNA sequences in a specific library to identify sequence-dependent confounders.
• Library-specific adaptability: Adapts correction models to different gRNA libraries rather than applying a universal correction strategy.

Scientific Applications:

• Gene function discovery: Improves accuracy of gene-level phenotype assignments in CRISPR/Cas9 screens by reducing sequence-induced bias.
• Pathway analysis: Reduces sequence-dependent noise to enable more reliable pathway-level interpretations from screening data.
• Phenotypic characterization across systems: Mitigates gRNA sequence biases across different cell types and organisms to support comparative studies.
• Reproducibility of functional genomics: Enhances reproducibility and fidelity of CRISPR/Cas9 screening results by correcting sequence-related confounders.

Methodology:

SeqCor extracts sequence features from gRNA libraries and applies machine learning algorithms to model and correct sequence-dependent effects on single-guide RNA knockout efficiency.