The article introduces Model-based Analysis of ChIP-Seq (MACS), a novel algorithm designed to analyze data from short-read sequencers like Solexa's Genome Analyzer. MACS addresses the challenges of ChIP-Seq data, such as the uncertainty of tag-to-site distance and regional biases in the genome. It empirically models the shift size of ChIP-Seq tags to improve the spatial resolution of predicted binding sites and uses a dynamic Poisson distribution to capture local biases, enhancing the robustness and specificity of peak predictions. MACS is compared favorably to existing ChIP-Seq peak-finding algorithms and is freely available. The authors demonstrate MACS's effectiveness through comparisons with other methods and by evaluating its performance on ChIP-Seq data for FoxA1, NRSF, and CTCF. The article also discusses the limitations and potential biases in ChIP-Seq data, providing insights into the optimal sequencing depth and the challenges of analyzing repressive factors. Overall, MACS offers a powerful tool for predicting protein-DNA interaction sites from ChIP-Seq data, improving both the accuracy and resolution of peak detection.The article introduces Model-based Analysis of ChIP-Seq (MACS), a novel algorithm designed to analyze data from short-read sequencers like Solexa's Genome Analyzer. MACS addresses the challenges of ChIP-Seq data, such as the uncertainty of tag-to-site distance and regional biases in the genome. It empirically models the shift size of ChIP-Seq tags to improve the spatial resolution of predicted binding sites and uses a dynamic Poisson distribution to capture local biases, enhancing the robustness and specificity of peak predictions. MACS is compared favorably to existing ChIP-Seq peak-finding algorithms and is freely available. The authors demonstrate MACS's effectiveness through comparisons with other methods and by evaluating its performance on ChIP-Seq data for FoxA1, NRSF, and CTCF. The article also discusses the limitations and potential biases in ChIP-Seq data, providing insights into the optimal sequencing depth and the challenges of analyzing repressive factors. Overall, MACS offers a powerful tool for predicting protein-DNA interaction sites from ChIP-Seq data, improving both the accuracy and resolution of peak detection.