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

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

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Title: HiCNorm: removing biases in Hi-C data via Poisson regression
Author: Hu, Ming; Deng, K.; Selvaraj, S.; Qin, Z.; Ren, B.; Liu, Jun

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Citation: Hu, Ming, Ke Deng, Siddarth Selvaraj, Zhaohui Qin, Bing Ren, and Jun S. Liu. 2012. “HiCNorm: Removing Biases in Hi-C Data via Poisson Regression.” Bioinformatics 28 (23) (September 27): 3131–3133. doi:10.1093/bioinformatics/bts570.
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Abstract: 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 to the existing Hi-C normalization method developed by Yaffe and Tanay, HiCNorm has fewer parameters, runs >1,000 times faster, and achieves higher reproducibility.
Published Version: doi:10.1093/bioinformatics/bts570
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:33719923
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