Model-based analysis of ChIP-Seq (MACS).
Zhang Y,
Liu T,
Meyer CA,
Eeckhoute J,
Johnson DS,
Bernstein BE,
Nussbaum C,
Myers RM,
Brown M,
Li W,
Liu XS.
Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, MA 02115, USA.
We present Model-based Analysis of ChIP-Seq data, MACS, which analyzes data generated by short read sequencers such as Solexa's Genome Analyzer. MACS empirically models the shift size of ChIP-Seq tags, and uses it to improve the spatial resolution of predicted binding sites. MACS also uses a dynamic Poisson distribution to effectively capture local biases in the genome, allowing for more robust predictions. MACS compares favorably to existing ChIP-Seq peak-finding algorithms, and is freely available.
PMID: 18798982 [PubMed - indexed for MEDLINE]
PMCID: PMC2592715