HiCNorm: removing biases in Hi-C data via Poisson regression.
Ontology highlight
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.eduSupplementary information
Supplementary data are available at Bioinformatics online.
SUBMITTER: Hu M
PROVIDER: S-EPMC3509491 | biostudies-literature | 2012 Dec
REPOSITORIES: biostudies-literature
Publications
HiCNorm: removing biases in Hi-C data via Poisson regression.
Hu Ming M Deng Ke K Selvaraj Siddarth S Qin Zhaohui Z Ren Bing B Liu Jun S JS
Bioinformatics (Oxford, England) 20120927 23
<h4>Summary</h4>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.<h4>Availability</h4>Freely available on the web at: http://www.people.fas.harvard.edu/∼junliu/HiCNorm/.<h4>Contact</h4>jliu@stat.harvard.edu<h4>S ...[more]
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