title: A hidden Markov random field-based Bayesian method for the detection of long-range chromosomal interactions in Hi-C data.
abstract: Advances in chromosome conformation capture and next-generation sequencing technologies are enabling genome-wide investigation of dynamic chromatin interactions. For example, Hi-C experiments generate genome-wide contact frequencies between pairs of loci by sequencing DNA segments ligated from loci in close spatial proximity. One essential task in such studies is peak calling, that is, detecting non-random interactions between loci from the two-dimensional contact frequency matrix. Successful fulfillment of this task has many important implications including identifying long-range interactions that assist interpreting a sizable fraction of the results from genome-wide association studies. The task - distinguishing biologically meaningful chromatin interactions from massive numbers of random interactions - poses great challenges both statistically and computationally. Model-based methods to address this challenge are still lacking. In particular, no statistical model exists that takes the underlying dependency structure into consideration.
  
  
  
  In this paper, we propose a hidden Markov random field (HMRF) based Bayesian method to rigorously model interaction probabilities in the two-dimensional space based on the contact frequency matrix. By borrowing information from neighboring loci pairs, our method demonstrates superior reproducibility and statistical power in both simulation studies and real data analysis.
  
  
  
  The Source codes can be downloaded at: http://www.unc.edu/∼yunmli/HMRFBayesHiC CONTACT: ming.hu@nyumc.org or yunli@med.unc.edu
  
  
  
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Biostatistics, Department of Genetics, Department of Computer Science.
authors.forename: Zheng
authors.initials: Z
authors.lastname: Xu
authors.affiliation: Department of Computer Science, Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, NC 27599, USA.
authors.forename: Guosheng
authors.initials: G
authors.lastname: Zhang
authors.affiliation: Department of Genetics, School of Medicine, Case Western Reserve University, Cleveland, Ohio 44016.
authors.forename: Fulai
authors.initials: F
authors.lastname: Jin
authors.affiliation: Department of Biostatistics, Department of Genetics.
authors.forename: Mengjie
authors.initials: M
authors.lastname: Chen
authors.affiliation: Department of Genetics.
authors.forename: Terrence S
authors.initials: TS
authors.lastname: Furey
authors.affiliation: Department of Genetics, Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
authors.forename: Patrick F
authors.initials: PF
authors.lastname: Sullivan
authors.affiliation: Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA and.
authors.forename: Zhaohui
authors.initials: Z
authors.lastname: Qin
authors.affiliation: Division of Biostatistics, Department of Population Health, New York University School of Medicine, New York, NY 10016, USA.
authors.forename: Ming
authors.initials: M
authors.lastname: Hu
authors.affiliation: Department of Biostatistics, Department of Genetics, Department of Computer Science.
authors.forename: Yun
authors.initials: Y
authors.lastname: Li
mesh_terms: D001499:Bayes Theorem; D002875:Chromosomes; D055106:Genome-Wide Association Study; D008390:Markov Chains; D015203:Reproducibility of Results