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

Bioinformatics. 2012 Dec 1;28(23):3131-3. doi: 10.1093/bioinformatics/bts570. Epub 2012 Sep 27.

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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Base Composition
  • Chromatin / genetics
  • Chromosome Mapping / methods*
  • Genomic Library
  • Internet
  • Linear Models*
  • Reproducibility of Results
  • Software*
  • Statistics, Nonparametric

Substances

  • Chromatin