Bayesian estimation of the number of inversions in the history of two chromosomes

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

Bayesian estimation of the number of inversions in the history of two chromosomes. / York, Thomas L.; Durrett, Richard; Nielsen, Rasmus.

In: Journal of Computational Biology, Vol. 9, No. 6, 01.12.2002, p. 805-818.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

York, TL, Durrett, R & Nielsen, R 2002, 'Bayesian estimation of the number of inversions in the history of two chromosomes', Journal of Computational Biology, vol. 9, no. 6, pp. 805-818. https://doi.org/10.1089/10665270260518281

APA

York, T. L., Durrett, R., & Nielsen, R. (2002). Bayesian estimation of the number of inversions in the history of two chromosomes. Journal of Computational Biology, 9(6), 805-818. https://doi.org/10.1089/10665270260518281

Vancouver

York TL, Durrett R, Nielsen R. Bayesian estimation of the number of inversions in the history of two chromosomes. Journal of Computational Biology. 2002 Dec 1;9(6):805-818. https://doi.org/10.1089/10665270260518281

Author

York, Thomas L. ; Durrett, Richard ; Nielsen, Rasmus. / Bayesian estimation of the number of inversions in the history of two chromosomes. In: Journal of Computational Biology. 2002 ; Vol. 9, No. 6. pp. 805-818.

Bibtex

@article{f9408cc20db74b84995de7230936c85d,
title = "Bayesian estimation of the number of inversions in the history of two chromosomes",
abstract = "We present a Bayesian approach to the problem of inferring the history of inversions separating homologous chromosomes from two different species. The method is based on Markov Chain Monte Carlo (MCMC) and takes full advantage of all the information from marker order. We apply the method both to simulated data and to two real data sets. For the simulated data, we show that the MCMC method provides accurate estimates of the true posterior distributions and in the analysis of the real data we show that the most likely number of inversions in some cases is considerably larger than estimates obtained based on the parsimony inferred number of inversions. Indeed, in the case of the Drosophila repleta-D. melanogaster comparison, the lower boundary of a 95% highest posterior density credible interval for the number of inversions is considerably larger than the most parsimonious number of inversions.",
keywords = "Bayesian estimation, Breakpoint graph, Inversions, MCMC",
author = "York, {Thomas L.} and Richard Durrett and Rasmus Nielsen",
year = "2002",
month = dec,
day = "1",
doi = "10.1089/10665270260518281",
language = "English",
volume = "9",
pages = "805--818",
journal = "Journal of Computational Biology",
issn = "1066-5277",
publisher = "Mary Ann Liebert, Inc. Publishers",
number = "6",

}

RIS

TY - JOUR

T1 - Bayesian estimation of the number of inversions in the history of two chromosomes

AU - York, Thomas L.

AU - Durrett, Richard

AU - Nielsen, Rasmus

PY - 2002/12/1

Y1 - 2002/12/1

N2 - We present a Bayesian approach to the problem of inferring the history of inversions separating homologous chromosomes from two different species. The method is based on Markov Chain Monte Carlo (MCMC) and takes full advantage of all the information from marker order. We apply the method both to simulated data and to two real data sets. For the simulated data, we show that the MCMC method provides accurate estimates of the true posterior distributions and in the analysis of the real data we show that the most likely number of inversions in some cases is considerably larger than estimates obtained based on the parsimony inferred number of inversions. Indeed, in the case of the Drosophila repleta-D. melanogaster comparison, the lower boundary of a 95% highest posterior density credible interval for the number of inversions is considerably larger than the most parsimonious number of inversions.

AB - We present a Bayesian approach to the problem of inferring the history of inversions separating homologous chromosomes from two different species. The method is based on Markov Chain Monte Carlo (MCMC) and takes full advantage of all the information from marker order. We apply the method both to simulated data and to two real data sets. For the simulated data, we show that the MCMC method provides accurate estimates of the true posterior distributions and in the analysis of the real data we show that the most likely number of inversions in some cases is considerably larger than estimates obtained based on the parsimony inferred number of inversions. Indeed, in the case of the Drosophila repleta-D. melanogaster comparison, the lower boundary of a 95% highest posterior density credible interval for the number of inversions is considerably larger than the most parsimonious number of inversions.

KW - Bayesian estimation

KW - Breakpoint graph

KW - Inversions

KW - MCMC

U2 - 10.1089/10665270260518281

DO - 10.1089/10665270260518281

M3 - Journal article

C2 - 12614548

AN - SCOPUS:0037000637

VL - 9

SP - 805

EP - 818

JO - Journal of Computational Biology

JF - Journal of Computational Biology

SN - 1066-5277

IS - 6

ER -

ID: 222644979