A Hidden Markov model web application for analysing bacterial genomotyping DNA microarray experiments.

Richard Newton; Jason Hinds; Lorenz Wernisch

doi:10.2165/00822942-200605040-00003

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A Hidden Markov model web application for analysing bacterial genomotyping DNA microarray experiments.

Richard Newton·Jason Hinds·Lorenz Wernisch

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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. The application may be found at http://hmmgd.cryst.bbk.ac.uk/hmmgd. The source code illustrating how to run R scripts from an Apache Struts-based web application is available from the corresponding author on request. The application is also available for local installation if required.

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Algorithms Artificial Intelligence Chromosome Mapping Genome, Bacterial Genotype Internet Markov Chains Models, Genetic Models, Statistical Oligonucleotide Array Sequence Analysis Pattern Recognition, Automated Programming Languages Journal Article Research Support, Non-U.S. Gov't

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Richard Newton, Jason Hinds, Lorenz Wernisch. 2006. A Hidden Markov model web application for analysing bacterial genomotyping DNA microarray experiments.. https://doi.org/10.2165/00822942-200605040-00003

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