iCRISEE

iCRISEE performs integrative analysis of CRISPR/Cas9 screening data to reduce false positive hits and improve identification of true genetic dependencies.

Key Features:

• Data Preprocessing: Precise data filtering and normalization with model selection and valid sgRNA number correction to ensure robustness across different single guide RNA (sgRNA) libraries.
• Quality Control and sgRNA Extraction: Quality control and sgRNA extraction and alignment from sequencing data.
• sgRNA Ranking: Advanced algorithms for sgRNA ranking that integrate multiple hypotheses from various algorithms.
• Gene Ranking: Algorithms for gene-level ranking based on aggregated sgRNA signals.
• Pathway Enrichment Analysis: Pathway enrichment analysis downstream of gene ranking to identify affected biological pathways.
• Integrated Analysis Workflow: End-to-end integration of preprocessing, quality control, sgRNA/gene ranking, and pathway enrichment analysis.

Scientific Applications:

• Large-scale CRISPR/Cas9 screening: Analysis of large-scale CRISPR/Cas9 screens for systematic interrogation of genome function.
• Gene essentiality screens: Improving accuracy of gene essentiality screens by reducing false positives.
• Functional genomics studies: Supporting functional genomics studies to identify genes associated with specific phenotypes.
• Pathway analysis: Facilitating pathway-level interpretation through pathway enrichment analysis.
• Phenotype identification: Aiding identification of specific phenotypes from pooled CRISPR screens.

Methodology:

Computational steps include data filtering and normalization, model selection, valid sgRNA number correction, quality control, sgRNA extraction and alignment, sgRNA ranking, integration of multiple algorithmic hypotheses, gene ranking, and pathway enrichment analysis.