Zhang Yong, Liu Tao, Meyer Clifford A, Eeckhoute Jérôme, Johnson David S, Bernstein Bradley E, Nusbaum Chad, Myers Richard M, Brown Myles, Li Wei, Liu X Shirley
Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, MA 02115, USA.
Genome Biol. 2008;9(9):R137. doi: 10.1186/gb-2008-9-9-r137. Epub 2008 Sep 17.
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.
我们展示了基于模型的ChIP-Seq数据分析方法MACS，该方法可分析由短读测序仪（如Solexa的基因组分析仪）生成的数据。MACS通过经验对ChIP-Seq标签的移位大小进行建模，并利用它来提高预测结合位点的空间分辨率。MACS还使用动态泊松分布来有效捕获基因组中的局部偏差，从而实现更可靠的预测。与现有的ChIP-Seq峰检测算法相比，MACS具有优势，并且可免费获取。
