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

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.

DOI: 10.1093/bioinformatics/bts570

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Conflict of Interest: none declared. REFERENCES Akaike,H. (1974) A new look at the statistical model identification

Funding: This work was supported by US National Institutes of Health grants R01HG005119 (to Z

and the Ludwig Institute for

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