Yong Zhang, Tao Liu, Clifford A. Meyer, J. Eeckhoute, David S. Johnson, B. Bernstein, C. Nusbaum, R. Myers, Myles A. Brown, Wei Li, X. Liu
2008.9.17GENOME BIOLOGY
tlooto Summary
This work presents Model-based Analysis of ChIP-Seq data, MACS, which analyzes data generated by short read sequencers such as Solexa's Genome Analyzer, and uses a dynamic Poisson distribution to effectively capture local biases in the genome, allowing for more robust predictions.
초록
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.
인용 형식
ZHANG, Yong, et al. Model-based analysis of chip-seq (MACS). GENOME BIOLOGY, 2008, 9: R137-R137.