Skip Navigation

Bioinformatics 2005 21(Suppl 2):ii166-ii172; doi:10.1093/bioinformatics/bti1127
This Article
Right arrow FREE Full Text (Print PDF) Freely available
Right arrow Comments: Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when Comments are posted
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrow Search for citing articles in:
ISI Web of Science (4)
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Husmeier, D.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Husmeier, D.
Social Bookmarking
 Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oxfordjournals.org

Discriminating between rate heterogeneity and interspecific recombination in DNA sequence alignments with phylogenetic factorial hidden Markov models

Dirk Husmeier

Biomathematics and Statistics Scotland Edinburgh EH9 3JZ, UK

Motivation: A recently proposed method for detecting recombination in DNA sequence alignments is based on the combination of hidden Markov models (HMMs) with phylogenetic trees. Although this method was found to detect breakpoints of recombinant regions more accurately than most existing techniques, it inherently fails to distinguish between recombination and rate variation. In the present paper, we propose to marry the phylogenetic tree to a factorial HMM (FHMM). The states of the first hidden chain represent tree topologies, whereas the states of the second independent hidden chain represent different global scaling factors of the branch lengths. Inference is done in terms of a hierarchical Bayesian model, where parameters and hidden states are sampled from the posterior distribution with Gibbs sampling.

Results: We have tested the proposed model on various synthetic and real-world DNA sequence alignments. The simulation results suggest that as opposed to the standard phylogenetic HMM, the phylogenetic FHMM clearly distinguishes between recombination and rate heterogeneity and thereby avoids the prediction of spurious recombinant regions.

Availability: The proposed method has been implemented in a MATLAB package that extends Kevin Murphy's HMM toolbox. Software and data used in our study are available from http://www.bioss.sari.ac.uk/~dirk/Supplements

Contact: dirk{at}bioss.ac.uk



Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
GeneticsHome page
J. Y. Dutheil, G. Ganapathy, A. Hobolth, T. Mailund, M. K. Uyenoyama, and M. H. Schierup
Ancestral Population Genomics: The Coalescent Hidden Markov Model Approach
Genetics, September 1, 2009; 183(1): 259 - 274.
[Abstract] [Full Text] [PDF]


Home page
GeneticsHome page
V. N. Minin, K. S. Dorman, F. Fang, and M. A. Suchard
Phylogenetic Mapping of Recombination Hotspots in Human Immunodeficiency Virus via Spatially Smoothed Change-Point Processes
Genetics, April 1, 2007; 175(4): 1773 - 1785.
[Abstract] [Full Text] [PDF]



Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.