The two key features of MACS are: empirical modeling of 'd' and tag shifting by d/2 to putative protein-DNA interaction site; and the use of a dynamic λlocal to capture local biases in the genome. To evaluate the effectiveness of tag shifting based on the MACS model d, we compared the performance of MACS to a similar procedure that uses the original tag positions instead of the shifted tag locations. The effectiveness of the dynamic λlocal is assessed by comparing MACS to a procedure that uses a uniform λBG from the genome background. Figure 1e,f show that both the detection specificity, measured by the percentage of predicted peaks with a FKHR motif within 50 bp of the peak summit, and the spatial resolution, defined as the average distance from the peak summit to the nearest FKHR motif, are greatly improved by using tag shifting and the dynamic λlocal. In addition, FoxA1 is known to cooperatively interact with estrogen receptor in breast cancer cells [1,15]. As evidence for this, we also observed enrichment for estrogen receptor elements (3.1-fold enriched relative to genome motif occurrence) and its half-site (2.7-fold) [15] within the center 300 bp regions of MACS-detected FoxA1 ChIP-Seq peaks.
