title: Fast MCMC sampling for hidden Markov Models to determine copy number variations.
abstract: BACKGROUND
  Hidden Markov Models (HMM) are often used for analyzing Comparative Genomic Hybridization (CGH) data to identify chromosomal aberrations or copy number variations by segmenting observation sequences. For efficiency reasons the parameters of a HMM are often estimated with maximum likelihood and a segmentation is obtained with the Viterbi algorithm. This introduces considerable uncertainty in the segmentation, which can be avoided with Bayesian approaches integrating out parameters using Markov Chain Monte Carlo (MCMC) sampling. While the advantages of Bayesian approaches have been clearly demonstrated, the likelihood based approaches are still preferred in practice for their lower running times; datasets coming from high-density arrays and next generation sequencing amplify these problems.
  
  
  RESULTS
  We propose an approximate sampling technique, inspired by compression of discrete sequences in HMM computations and by kd-trees to leverage spatial relations between data points in typical data sets, to speed up the MCMC sampling.
  
  
  CONCLUSIONS
  We test our approximate sampling method on simulated and biological ArrayCGH datasets and high-density SNP arrays, and demonstrate a speed-up of 10 to 60 respectively 90 while achieving competitive results with the state-of-the art Bayesian approaches.
  
  
  BACKGROUND
  An implementation of our method will be made available as part of the open source GHMM library from http://ghmm.org.
authors: Department of Computer Science, Rutgers University, 110 Frelinghuysen Road, Piscataway, NJ 08854, USA. pavelm@cs.rutgers.edu
authors: Md Pavel
authors: MP
authors: Mahmud
authors: Alexander
authors: A
authors: Schliep
mesh_terms: D000465:Algorithms; D001483:Base Sequence; D001499:Bayes Theorem; D055028:Comparative Genomic Hybridization; D056915:DNA Copy Number Variations; D006801:Humans; D020522:Lymphoma, Mantle-Cell / Q000235:genetics; D008390:Markov Chains; D008957:Models, Genetic; D009010:Monte Carlo Method; D011336:Probability