HiCNorm: removing biases in Hi-C data via Poisson regression

We propose a parametric model, HiCNorm, to remove systematic biases in the raw Hi-C contact maps, resulting in a simple, fast, yet accurate normalization procedure. Compared with the existing Hi-C normalization method developed by Yaffe and Tanay, HiCNorm has fewer parameters, runs >1000 times faster and achieves higher reproducibility. Freely available on the web at: http://www.people.fas.harvard.edu/∼junliu/HiCNorm/. jliu@stat.harvard.edu Supplementary data are available at Bioinformatics online.