title: FastHiC: a fast and accurate algorithm to detect long-range chromosomal interactions from Hi-C data.
abstract: How chromatin folds in three-dimensional (3D) space is closely related to transcription regulation. As powerful tools to study such 3D chromatin conformation, the recently developed Hi-C technologies enable a genome-wide measurement of pair-wise chromatin interaction. However, methods for the detection of biologically meaningful chromatin interactions, i.e. peak calling, from Hi-C data, are still under development. In our previous work, we have developed a novel hidden Markov random field (HMRF) based Bayesian method, which through explicitly modeling the non-negligible spatial dependency among adjacent pairs of loci manifesting in high resolution Hi-C data, achieves substantially improved robustness and enhanced statistical power in peak calling. Superior to peak callers that ignore spatial dependency both methodologically and in performance, our previous Bayesian framework suffers from heavy computational costs due to intensive computation incurred by modeling the correlated peak status of neighboring loci pairs and the inference of hidden dependency structure.
  
  
  
  In this work, we have developed FastHiC, a novel approach based on simulated field approximation, which approximates the joint distribution of the hidden peak status by a set of independent random variables, leading to more tractable computation. Performance comparisons in real data analysis showed that FastHiC not only speeds up our original Bayesian method by more than five times, bus also achieves higher peak calling accuracy.
  
  
  
  FastHiC is freely accessible at:http://www.unc.edu/∼yunmli/FastHiC/ CONTACTS: : yunli@med.unc.edu or ming.hu@nyumc.org
  
  
  
  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: College of Veterinary Medicine, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China.
authors.forename: Cong
authors.initials: C
authors.lastname: Wu
authors.affiliation: Department of Biostatistics Department of Genetics Department of Computer Science.
authors.forename: Yun
authors.initials: Y
authors.lastname: Li
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
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D002843:Chromatin; D002874:Chromosome Mapping; D002875:Chromosomes; D003198:Computer Simulation; D016678:Genome; D008962:Models, Theoretical; D012984:Software
chemical_list: D002843:Chromatin