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. 2012 Dec 1;28(23):3131-3.
doi: 10.1093/bioinformatics/bts570. Epub 2012 Sep 27.

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

Ming Hu  1 , Ke Deng, Siddarth Selvaraj, Zhaohui Qin, Bing Ren, Jun S Liu
Affiliations

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

Ming Hu et al. Bioinformatics. .

Abstract

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.

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