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A Hidden Markov Model Web Application for Analysing Bacterial Genomotyping DNA Microarray Experiments

A Hidden Markov Model Web Application for Analysing Bacterial Genomotyping DNA Microarray... Whole genome DNA microarray genomotyping experiments compare the gene content of different species or strains of bacteria. A statistical approach to analysing the results of these experiments was developed, based on a Hidden Markov model (HMM), which takes adjacency of genes along the genome into account when calling genes present or absent. The model was implemented in the statistical language R and applied to three datasets. The method is numerically stable with good convergence properties. Error rates are reduced compared with approaches that ignore spatial information. Moreover, the HMM circumvents a problem encountered in a conventional analysis: determining the cut-off value to use to classify a gene as absent. An Apache Struts web interface for the R script was created for the benefit of users unfamiliar with R. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Bioinformatics Springer Journals

A Hidden Markov Model Web Application for Analysing Bacterial Genomotyping DNA Microarray Experiments

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References (30)

Publisher
Springer Journals
Copyright
Copyright © 2006 by Adis Data Information BV
Subject
Pharmacy; Pharmacy
ISSN
1175-5636
DOI
10.2165/00822942-200605040-00003
Publisher site
See Article on Publisher Site

Abstract

Whole genome DNA microarray genomotyping experiments compare the gene content of different species or strains of bacteria. A statistical approach to analysing the results of these experiments was developed, based on a Hidden Markov model (HMM), which takes adjacency of genes along the genome into account when calling genes present or absent. The model was implemented in the statistical language R and applied to three datasets. The method is numerically stable with good convergence properties. Error rates are reduced compared with approaches that ignore spatial information. Moreover, the HMM circumvents a problem encountered in a conventional analysis: determining the cut-off value to use to classify a gene as absent. An Apache Struts web interface for the R script was created for the benefit of users unfamiliar with R.

Journal

Applied BioinformaticsSpringer Journals

Published: Aug 22, 2012

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