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Detecting homogeneous segments in DNA sequences by using hidden Markov models

Lookup NU author(s): Professor Richard Boys, Dr Daniel Henderson, Professor Darren Wilkinson

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Abstract

In recent years there has been a rapid growth in the amount of DNA being sequenced and in its availability through genetic databases. Statistical techniques which identify structure within these sequences can be of considerable assistance to molecular biologists particularly when they incorporate the discrete nature of changes caused by evolutionary processes. This paper focuses on the detection of homogeneous segments within heterogeneous DNA sequences In particular, we study an intron from the chimpanzee α-fetoprotein gene; this protein plays an important role in the embryonic development of mammals. We present a Bayesian solution to this segmentation problem using a hidden Markov model implemented by Markov chain Monte Carlo methods We consider the important practical problem of specifying informative prior knowledge about sequences of this type. Two Gibbs sampling algorithms are contrasted and the sensitivity of the analysis to the prior specification is investigated. Model selection and possible ways to overcome the label switching problem are also addressed. Our analysis of intron 7 identifies three distinct homogeneous segment types, two of which occur in more than one region, and one of which is reversible.


Publication metadata

Author(s): Henderson DA; Wilkinson DJ; Boys RJ

Publication type: Article

Publication status: Published

Journal: Journal of the Royal Statistical Society. Series C: Applied Statistics

Year: 2000

Volume: 49

Issue: 2

Pages: 269-285

Print publication date: 01/01/2000

ISSN (print): 0035-9254

ISSN (electronic): 1467-9876

Publisher: Wiley-Blackwell Publishing Ltd.

URL: http://dx.doi.org/10.1111/1467-9876.00191

DOI: 10.1111/1467-9876.00191


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