title: HiCNorm: removing biases in Hi-C data via Poisson regression.
abstract: CONCLUSIONS
  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.
  
  
  BACKGROUND
  Freely available on the web at: http://www.people.fas.harvard.edu/∼junliu/HiCNorm/.
  
  
  BACKGROUND
  jliu@stat.harvard.edu
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Statistics, Harvard University, Cambridge, MA 02138, USA.
authors.forename: Ming
authors.initials: M
authors.lastname: Hu
authors.forename: Ke
authors.initials: K
authors.lastname: Deng
authors.forename: Siddarth
authors.initials: S
authors.lastname: Selvaraj
authors.forename: Zhaohui
authors.initials: Z
authors.lastname: Qin
authors.forename: Bing
authors.initials: B
authors.lastname: Ren
authors.forename: Jun S
authors.initials: JS
authors.lastname: Liu
mesh_terms: D001482:Base Composition; D002843:Chromatin / Q000235:genetics; D002874:Chromosome Mapping / Q000379:methods*; D015698:Genomic Library; D020407:Internet; D016014:Linear Models; D015203:Reproducibility of Results; D012984:Software; D018709:Statistics, Nonparametric
chemical_list: D002843:Chromatin