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

Extracted Key Phrases

02040201220132014201520162017
Citations per Year

116 Citations

Semantic Scholar estimates that this publication has 116 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@article{Hu2012HiCNormRB, title={HiCNorm: removing biases in Hi-C data via Poisson regression}, author={Ming Hu and Ke Deng and Siddarth Selvaraj and Zhaohui S. Qin and Bing Ren and Jun S. Liu}, journal={Bioinformatics}, year={2012}, volume={28 23}, pages={3131-3} }