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Context dependent DNA evolutionary models

Publikation: Working paper/Preprint Working paperForskning

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Context dependent DNA evolutionary models. / Jensen, Jens Ledet.

Institut for Matematiske Fag, Aarhus Universitet, 2005.

Publikation: Working paper/Preprint Working paperForskning

Harvard

Jensen, JL 2005 'Context dependent DNA evolutionary models' Institut for Matematiske Fag, Aarhus Universitet.

APA

Jensen, J. L. (2005). Context dependent DNA evolutionary models. Institut for Matematiske Fag, Aarhus Universitet. Research Report Bind 458

CBE

Jensen JL. 2005. Context dependent DNA evolutionary models. Institut for Matematiske Fag, Aarhus Universitet.

MLA

Jensen, Jens Ledet Context dependent DNA evolutionary models. Institut for Matematiske Fag, Aarhus Universitet. (Research Report, Bind 458). 2005., 45 s.

Vancouver

Jensen JL. Context dependent DNA evolutionary models. Institut for Matematiske Fag, Aarhus Universitet. 2005.

Author

Jensen, Jens Ledet. / Context dependent DNA evolutionary models. Institut for Matematiske Fag, Aarhus Universitet, 2005. (Research Report, Bind 458).

Bibtex

@techreport{9f5cd060989511dabee902004c4f4f50,
title = "Context dependent DNA evolutionary models",
abstract = "This paper is about stochastic models for the evolution of DNA. For a set of aligned DNA sequences, connected in a phylogenetic tree, the models should be able to explain - in probabilistic terms - the differences seen in the sequences. From the estimates of the parameters in the model one can start to make biologically interpretations and conclusions concerning the evolutionary forces at work. In parallel with the increase in computing power, models have become more complex. Starting with Markov processes on a space with 4 states, and extended to Markov processes with 64 states, we are today studying models on spaces with 4n (or 64n) number of states with n well above one hundred, say. For such models it is no longer possible to calculate the transition probability analytically, and often Markov chain Monte Carlo is used in connection with likelihood analysis. This is also the approach taken in this paper, and a time discretization of the process is presented in order to make the calculations more feasible. Apart from the time discretization we introduce a set of simple estimating equations, together with an EM type algorithm, for finding the parameter estimates. A detailed derivation of the asymptotic properties of the estimates is also given.",
author = "Jensen, {Jens Ledet}",
year = "2005",
language = "English",
series = "Research Report",
publisher = "Institut for Matematiske Fag, Aarhus Universitet",
type = "WorkingPaper",
institution = "Institut for Matematiske Fag, Aarhus Universitet",

}

RIS

TY - UNPB

T1 - Context dependent DNA evolutionary models

AU - Jensen, Jens Ledet

PY - 2005

Y1 - 2005

N2 - This paper is about stochastic models for the evolution of DNA. For a set of aligned DNA sequences, connected in a phylogenetic tree, the models should be able to explain - in probabilistic terms - the differences seen in the sequences. From the estimates of the parameters in the model one can start to make biologically interpretations and conclusions concerning the evolutionary forces at work. In parallel with the increase in computing power, models have become more complex. Starting with Markov processes on a space with 4 states, and extended to Markov processes with 64 states, we are today studying models on spaces with 4n (or 64n) number of states with n well above one hundred, say. For such models it is no longer possible to calculate the transition probability analytically, and often Markov chain Monte Carlo is used in connection with likelihood analysis. This is also the approach taken in this paper, and a time discretization of the process is presented in order to make the calculations more feasible. Apart from the time discretization we introduce a set of simple estimating equations, together with an EM type algorithm, for finding the parameter estimates. A detailed derivation of the asymptotic properties of the estimates is also given.

AB - This paper is about stochastic models for the evolution of DNA. For a set of aligned DNA sequences, connected in a phylogenetic tree, the models should be able to explain - in probabilistic terms - the differences seen in the sequences. From the estimates of the parameters in the model one can start to make biologically interpretations and conclusions concerning the evolutionary forces at work. In parallel with the increase in computing power, models have become more complex. Starting with Markov processes on a space with 4 states, and extended to Markov processes with 64 states, we are today studying models on spaces with 4n (or 64n) number of states with n well above one hundred, say. For such models it is no longer possible to calculate the transition probability analytically, and often Markov chain Monte Carlo is used in connection with likelihood analysis. This is also the approach taken in this paper, and a time discretization of the process is presented in order to make the calculations more feasible. Apart from the time discretization we introduce a set of simple estimating equations, together with an EM type algorithm, for finding the parameter estimates. A detailed derivation of the asymptotic properties of the estimates is also given.

M3 - Working paper

T3 - Research Report

BT - Context dependent DNA evolutionary models

PB - Institut for Matematiske Fag, Aarhus Universitet

ER -