MACS refers to “Model-based Analysis of ChiP-Seq data.” MACS allows the analysis of sequencing data from short-read sequences such as generated from the Genome Analyzer from Solexa.
Deep DNA sequencing methods such as chromatin immunoprecipitation sequencing (ChIP-seq) and RNA sequencing (RNA-seq) allow genome-wide studies of protein-DNA interactions and transcriptomes.
Zhang et al., in 2008, reported the development of a new algorithm for the analysis of ChiP-Seq data. MACS models the shift of ChIP-Seq tags to improve the spatial resolution of predicted binding sites. A dynamic Poisson distribution captures local biases. MACS captures regional preferences in the genome and enhances the robustness and specificity of the prediction. Also, MACS can analyze copy number variations and digital gene expression without the need for controls.
According to Zhang et al., MACS allows empirically modeling the shift size of ChIP-Seq tags. MACS improves the spatial resolution of predicted binding sites. MACS provides detailed information for each peak identified, including genome coordinates, p-value, false discovery rate (FDR), fold_enrichment, and summit or peak center.
Zhang et al. showed that MACS compares favorably to existing ChIP-Seq peak-finding algorithms. Also, MACS is freely available encoded in Python [MACS].
Feng J, Liu T, Zhang Y. Using MACS to identify peaks from ChIP-Seq data. Curr Protoc Bioinformatics. 2011 Jun;Chapter 2:Unit 2.14.[PMC]
Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, Bernstein BE, Nusbaum C, Myers RM, Brown M, Li W, Liu XS. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 2008;9(9):R137. [Pubmed]