MACS (Model-based Analysis of ChIP-Seq) is a method for analyzing ChIP-Seq data to identify protein-DNA binding sites. It improves the spatial resolution of predicted binding sites by empirically modeling the shift size of ChIP-Seq tags and using a dynamic Poisson distribution to capture local genome biases. MACS compares favorably with existing ChIP-Seq peak-finding algorithms and is freely available. The method addresses challenges in ChIP-Seq data analysis, such as the representation of only the ends of ChIP fragments and regional biases in the genome. MACS models the shift size of tags to better locate binding sites and uses a dynamic parameter, λ_local, to account for local biases. It also calculates the false discovery rate (FDR) for detected peaks and can be applied to differential binding between two conditions. MACS was tested on three datasets: FoxA1 in MCF7 cells, NRSF in Jurkat T cells, and CTCF in CD4+ T cells. It produced results superior to other ChIP-Seq peak-finding algorithms. MACS improves the detection of binding sites and provides detailed information for each peak, including genome coordinates, p-value, FDR, fold-enrichment, and summit. MACS is implemented in Python and is freely available. It is user-friendly and can be applied to ChIP-Seq experiments with or without controls. The method is efficient and can process large datasets quickly. MACS is recommended for its accuracy, robustness, and ability to capture regional biases in the genome. It is particularly useful for identifying binding sites in ChIP-Seq experiments where matching control samples are not available.MACS (Model-based Analysis of ChIP-Seq) is a method for analyzing ChIP-Seq data to identify protein-DNA binding sites. It improves the spatial resolution of predicted binding sites by empirically modeling the shift size of ChIP-Seq tags and using a dynamic Poisson distribution to capture local genome biases. MACS compares favorably with existing ChIP-Seq peak-finding algorithms and is freely available. The method addresses challenges in ChIP-Seq data analysis, such as the representation of only the ends of ChIP fragments and regional biases in the genome. MACS models the shift size of tags to better locate binding sites and uses a dynamic parameter, λ_local, to account for local biases. It also calculates the false discovery rate (FDR) for detected peaks and can be applied to differential binding between two conditions. MACS was tested on three datasets: FoxA1 in MCF7 cells, NRSF in Jurkat T cells, and CTCF in CD4+ T cells. It produced results superior to other ChIP-Seq peak-finding algorithms. MACS improves the detection of binding sites and provides detailed information for each peak, including genome coordinates, p-value, FDR, fold-enrichment, and summit. MACS is implemented in Python and is freely available. It is user-friendly and can be applied to ChIP-Seq experiments with or without controls. The method is efficient and can process large datasets quickly. MACS is recommended for its accuracy, robustness, and ability to capture regional biases in the genome. It is particularly useful for identifying binding sites in ChIP-Seq experiments where matching control samples are not available.