Bayesian hidden Markov models in DNA sequence segmentation using R: the case of Simian Vacuolating virus (SV40)

References

• Benson DA, Clark K, Karsch-Mizrachi I, et al. Genbank. Nucleic Acids Res. 2014;42:D32–D37. doi: 10.1093/nar/gkt1030
   PubMed Web of Science ®Google Scholar
• GenBank. Growth of GenBank and WGS; 2015 [cited 2015 Apr 16]. Available from: http://www.ncbi.nlm.nih.gov/genbank/statistics
   Google Scholar
• Stückle EE, Emmrich C, Grob U, et al. Statistical analysis of nucleotide sequences. Nucleic Acids Res. 1990;18(22):6641–6647. doi: 10.1093/nar/18.22.6641
   PubMed Web of Science ®Google Scholar
• Oliver JL, Román-Roldán R, Pérez J, et al. SEGMENT: identifying compositional domains in DNA sequences. Bioinformatics. 1999;15(12):974–979. doi: 10.1093/bioinformatics/15.12.974
   PubMed Web of Science ®Google Scholar
• Ramensky VE, Makeev VJ, Roytberg MA, et al. DNA segmentation through the Bayesian approach. J Comput Biol. 2000;7(1/2):215–231. doi: 10.1089/10665270050081487
   PubMed Web of Science ®Google Scholar
• Bernardi G, Oloffson B, Filipski J, et al. The mosaic genome of warm-blooded vertebrates. Science. 1985;228(4702):953–958. doi: 10.1126/science.4001930
   PubMed Web of Science ®Google Scholar
• Bernardi G. The isochore organisation of the human genome. Annu Rev Genet. 1989;23:637–659. doi: 10.1146/annurev.ge.23.120189.003225
   PubMed Web of Science ®Google Scholar
• Guigo R, Fickett JW. Distinctive sequence features in protein coding genic non-coding, and intergenic human DNA. J Mol Biol. 1995;253(1):51–60. doi: 10.1006/jmbi.1995.0535
   PubMed Web of Science ®Google Scholar
• Braun JV, Müller HG. Statistical methods for DNA sequence segmentation. Statist Sci. 1998;13(2):142–162. doi: 10.1214/ss/1028905933
   Web of Science ®Google Scholar
• Baum LE, Petrie T. Statistical inference for probabilistic functions of finite state Markov chains. Ann Math Statist. 1966;37:1554–1563. doi: 10.1214/aoms/1177699147
   Google Scholar
• Baum LE, Eagon JA. An inequality with applications to statistical estimation for probabilistic functions of Markov processes and to a model for ecology. Bull Am Math Soc. 1967;73(3):360–364. doi: 10.1090/S0002-9904-1967-11751-8
   Web of Science ®Google Scholar
• Baum LE, Petrie T, Soules G, et al. A maxmization technique occuring in the statistical analysis of probabilistic functions of Markov chains. Ann Math Statist. 1970;41(1):164–171. doi: 10.1214/aoms/1177697196
   Google Scholar
• Baum LE. An inequality and associated maximization technique in statistical estimation for probabilistic functions of Markov processes. In: Shisha O, editor. Inequalities – III: Proceedings of the Third Symposium on Inequalities Held at the University of California; 1969 Sep 1–9; Los Angeles, New York: Academic Press; 1972. p. 1–8.
   Google Scholar
• Cappé O, Moulines E, Rydén T. Inference in hidden Markov models. New York (NY): Springer; 2005.
   Google Scholar
• Churchill GA. Stochastic models for heterogeneous DNA sequences. Bull Math Biol. 1989;51(1):79–94. doi: 10.1007/BF02458837
   PubMed Web of Science ®Google Scholar
• Churchill GA. Hidden Markov chains and the analysis of genome structure. Comput Chem. 1992;16(2):107–115. doi: 10.1016/0097-8485(92)80037-Z
   Google Scholar
• Boys RJ, Henderson DA, Wilkinson DJ. Detecting homogeneous segments in DNA sequences by using Hidden Markov models. Appl Stat. 2000;49:269–285.
   Web of Science ®Google Scholar
• Boys RJ, Henderson DA. On determining the order of Markov dependence of an observed process governed by a hidden Markov model. Sci Program. 2002;10:241–251.
   Google Scholar
• Boys RJ, Henderson DA. A Bayesian approach to DNA sequence segmentation. Biometrics. 2004;60:573–581. doi: 10.1111/j.0006-341X.2004.00206.x
   PubMed Web of Science ®Google Scholar
• Nur D, Allingham D, Rousseau J, et al. Bayesian hidden Markov model for DNA sequence segmentation: a prior sensitivity analysis. Comput Statist Data Anal. 2009;53(5):1873–1882. doi: 10.1016/j.csda.2008.07.007
   Web of Science ®Google Scholar
• Lunn DJ, Thomas A, Best N, et al. Winbugs – a Bayesian modelling framework: concepts, structure, and extensibility. Stat Comput. 2000 [ cited 2013 June 30];10(4):325–337. Available from: http://dx.doi.org/10.1023/A:1008929526011 doi: 10.1023/A:1008929526011
   Web of Science ®Google Scholar
• R Core Team. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2013 [cited 2014 Jan 15]. Available from: http://www.R-project.org/
   Google Scholar
• Robert CP, Celeux G, Diebolt J. Bayesian estimation of hidden Markov chains: a stochastic implementation. Statist Probab Lett. 1993;16:77–83. doi: 10.1016/0167-7152(93)90127-5
   Web of Science ®Google Scholar
• Stephens M. Dealing with label switching in mixture models. J R Stat Soc Ser B Stat Methodol. 2000;62(4):795–809. doi: 10.1111/1467-9868.00265
   Web of Science ®Google Scholar
• Jasra A, Holmes CC, Stephens DA. Markov chain Monte Carlo methods and the label switching problem in Bayesian mixture modelling. Statist Sci. 2005;20(1):50–67. doi: 10.1214/088342305000000016
   Web of Science ®Google Scholar
• Richardson S, Green PJ. On Bayesian analysis of mixtures with an unknown number of components. J R Stat Soc Ser B Methodol. 1997;59(4):731–792. doi: 10.1111/1467-9868.00095
   Google Scholar
• Plummer M, Best N, Cowles K, et al. Coda: convergence diagnosis and output analysis for MCMC. R News. 2006 [ cited 2013 June 30];6(1):7–11. Available from: http://CRAN.R-project.org/doc/Rnews/
   Google Scholar
• Geweke J. Evaluating the accuracy of sampling-based approaches to calculating posterior moments. In: Bernardo JM, Berger JO, Dawiv AP, Smith AFM, editors. Bayesian analysis. Vol. 4. Oxford: Clarendon Press; 1992. p. 169–193.
   Google Scholar
• Heidelberger P, Welch PD. Simulation run length control in the presence of an initial transition. Oper Res. 1983;31:1109–1144. doi: 10.1287/opre.31.6.1109
   Web of Science ®Google Scholar
• Wilkinson DJ. Parallel bayesian computation. In: Kontoghiorghes EJ, editor. Handbook of parallel computing and statistics. Boca Raton, Florida: Marcel Dekker/CRC Press; 2005. p. 477–508.
   Google Scholar
• Reddy VB, Thimmappaya B, Dhar R, et al. The genome of simian virus 40. Science. 1978;200(4341):494–502. doi: 10.1126/science.205947
   PubMed Web of Science ®Google Scholar
• Lander ES, Linton LM, Birren B, et al. Human Genome. Nature. 2001;409:860–921. doi: 10.1038/35057062
   PubMed Web of Science ®Google Scholar
• Elhaik E, Graur D, Josic K. Comparitive testing of DNA segmentation algorithms using benchmark simulations. Mol Biol Evol. 2010;27(5):1015–1024. doi: 10.1093/molbev/msp307
   PubMed Web of Science ®Google Scholar
• Ferguson JD. Variable duration models for speech. Proc Symp on the Application of Hidden Markov Models to Text and Speech; 1980. p. 143–179.
   Google Scholar
• Yu SZ. Hidden semi-Markov models. Artificial Intelligence. 2010;174:215–243. doi: 10.1016/j.artint.2009.11.011
   Web of Science ®Google Scholar