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

TitleHiCNorm: removing biases in Hi-C data via Poisson regression.
Publication TypeJournal Article
Year of Publication2012
AuthorsHu M, Deng K, Selvaraj S, Qin Z, Ren B, Liu JS
JournalBioinformatics
Volume28
Issue23
Pagination3131-3
Date Published2012 Dec 1
ISSN1367-4811
KeywordsBase Composition, Chromatin, Chromosome Mapping, Genomic Library, Internet, Linear Models, Reproducibility of Results, Software, Statistics, Nonparametric
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 [at] stat.harvard.edu Supplementary information: Supplementary data are available at Bioinformatics online.

DOI10.1093/bioinformatics/bts570
PubMed URLhttp://www.ncbi.nlm.nih.gov/pubmed/23023982?dopt=Abstract
PMCPMC3509491
Alternate JournalBioinformatics
PubMed ID23023982