csaw

csaw performs differential binding analysis of ChIP-seq data to detect changes in protein-DNA interactions between experimental conditions.

Key Features:

• Peak- and window-based strategies: Employs both peak-based and window-based approaches for detecting differential binding when regions of interest are not predefined.
• Error control considerations: Notes that peak-based methods can lose type I error control if peaks are defined and analyzed in the same dataset and therefore recommends peak calling on pooled libraries prior to statistical analysis.
• False discovery rate (FDR) control: Implements a hybrid approach using Simes' method to control FDR across multiple windows and regions.
• Comparative analysis and validation: Evaluates relative advantages of peak- and window-based strategies using simulations and real datasets.
• Benchmarking: Benchmarks implementations against existing differential binding programs and reports favorable performance.

Scientific Applications:

• Protein-DNA interaction profiling: Identifies genomic regions with differential protein binding across conditions using ChIP-seq data.
• Regulatory mechanism studies: Supports investigation of regulatory mechanisms and modulation of gene expression by changes in binding patterns.
• Genetic variation impact analysis: Assesses effects of genetic variation on binding patterns across experimental conditions.

Methodology:

Uses peak-based and window-based detection; recommends peak calling on pooled libraries prior to statistical analysis; applies a hybrid FDR-control approach using Simes' method; and validates methods via simulations, real datasets, and benchmarking against existing programs.