gcapc

gcapc corrects GC-content bias in ChIP-seq peak calling to improve identification of protein-binding sites and reduce false-positive peaks.

Key Features:

• GC Bias Estimation: gcapc employs generalized linear mixture models and an effective GC strategy to quantify how GC content influences sequencing read coverage.
• Peak Significance Adjustment: The estimated GC bias is integrated into peak significance estimation to refine detection of protein-binding sites.
• Improved Consistency Across Experiments: Accounting for GC-content bias enhances reproducibility of ChIP-seq peak calls across different experimental setups.
• Reduction of False Positives: The method distinguishes genuine binding events from GC-content-related noise, reducing false-positive peaks.

Scientific Applications:

• Functional Genomics: Improves ChIP-seq peak calling used for constructing public catalogs of genomic regions bound by specific proteins and for discovery and validation of regulatory elements affecting gene expression and cellular function.

Methodology:

Uses generalized linear mixture models to estimate GC bias and a statistical approach that simultaneously models GC effects on both nonspecific noise and signal induced by protein-binding sites, and integrates the estimated GC bias into peak significance estimation.