title: A computational strategy to adjust for copy number in tumor Hi-C data.
abstract: The Hi-C technology was designed to decode the three-dimensional conformation of the genome. Despite progress towards more and more accurate contact maps, several systematic biases have been demonstrated to affect the resulting data matrix. Here we report a new source of bias that can arise in tumor Hi-C data, which is related to the copy number of genomic DNA. To address this bias, we designed a chromosome-adjusted iterative correction method called caICB. Our caICB correction method leads to significant improvements when compared with the original iterative correction in terms of eliminating copy number bias.
  
  
  
  The method is available at https://bitbucket.org/mthjwu/hicapp CONTACT: michor@jimmy.harvard.eduSupplementary information: Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Computational Biology and Biostatistics, Dana-Farber Cancer Institute, and Department of Biostatistics, Harvard School of Public Health, Boston, MA 02215, USA.
authors.forename: Hua-Jun
authors.initials: HJ
authors.lastname: Wu
authors.affiliation: Department of Computational Biology and Biostatistics, Dana-Farber Cancer Institute, and Department of Biostatistics, Harvard School of Public Health, Boston, MA 02215, USA.
authors.forename: Franziska
authors.initials: F
authors.lastname: Michor
mesh_terms: D019295:Computational Biology / Q000379:methods*; D056915:DNA Copy Number Variations; D006801:Humans; D009369:Neoplasms / Q000235:genetics*; D012984:Software