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

SUMMARY: 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. AVAILABILITY: Freely available on the web at: http://www.people.fas.harvard.edu/∼junliu/HiCNorm/. CONTACT: jliu@stat.harvard.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.