MACS performs model-based analysis of ChIP-Seq data generated by short read sequencers.

Model-based Analysis of ChIP-seq (MACS) is a computational algorithm that identifies genome-wide locations of transcription/chromatin factor binding or histone modification from ChIP-seq data. MACS consists of four steps: removing redundant reads, adjusting read position, calculating peak enrichment, and estimating the empirical false discovery rate. In this protocol, we provide a detailed demonstration of how to install MACS and how to use it to analyze three common types of ChIP-seq datasets with different characteristics: the sequence-specific transcription factor FoxA1, the histone modification mark H3K4me3 with sharp enrichment, and the H3K36me3 mark with broad enrichment. We also explain how to interpret and visualize the results of MACS analyses. The algorithm requires approximately 3 GB of RAM and 1.5 hours of computing time to analyze a ChIP-seq dataset containing 30 million reads, an estimate that increases with sequence coverage. MACS is open-source and is available from http://liulab.dfci.harvard.edu/MACS.

Complex organisms require tissue-specific transcriptional programs, yet little is known about how these are established. The transcription factor FoxA1 is thought to contribute to gene regulation through its ability to act as a pioneer factor binding to nucleosomal DNA. Through genome-wide positional analyses, we demonstrate that FoxA1 cell type-specific functions rely primarily on differential recruitment to chromatin predominantly at distant enhancers rather than proximal promoters. This differential recruitment leads to cell type-specific changes in chromatin structure and functional collaboration with lineage-specific transcription factors. Despite the ability of FoxA1 to bind nucleosomes, its differential binding to chromatin sites is dependent on the distribution of histone H3 lysine 4 dimethylation. Together, our results suggest that methylation of histone H3 lysine 4 is part of the epigenetic signature that defines lineage-specific FoxA1 recruitment sites in chromatin. FoxA1 translates this epigenetic signature into changes in chromatin structure thereby establishing lineage-specific transcriptional enhancers and programs.

SUMMARY The evolution of prostate cancer from an androgen-dependent state (ADPCa) to one that is androgen-independent (AIPCa) marks its lethal progression. The androgen receptor (AR) is essential in both, though its function in AIPCa is poorly understood. We have defined the direct AR-dependent target genes in both AIPCa and ADPCa by generating AR-dependent gene expression profiles and AR cistromes. In contrast to ADPCa, AR selectively up-regulates M-phase cell cycle genes in AIPCa including UBE2C, a gene that inactivates the M-phase checkpoint. Selective epigenetic marks and collaborating transcription factor occupancy at UBE2C enhancers leads to increased AR recruitment and UBE2C over-expression in AIPCa cell lines and clinical cases. Silencing of UBE2C blocks AIPCa but not ADPCa growth. Thus the role of AR in AIPCa is not to direct the androgen-dependent gene expression program without androgen, but rather to execute a distinct program resulting in androgen-independent growth.