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Bioinformatics. 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.

Author information

1
Department of Statistics, Harvard University, Cambridge, MA 02138, USA.

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.

PMID:
23023982
PMCID:
PMC3509491
DOI:
10.1093/bioinformatics/bts570
[Indexed for MEDLINE]
Free PMC Article

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