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

Richard Newton1, Jason Hinds, Lorenz Wernisch

  • 1School of Crystallography, Birkbeck College, University of London, London, UK. r.newton@mail.cryst.bbk.ac.uk

Applied Bioinformatics
|December 5, 2006
PubMed
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A new statistical method using a Hidden Markov Model (HMM) improves bacterial genome analysis by considering gene order. This approach reduces errors and simplifies gene presence/absence calls in DNA microarray experiments.

Area of Science:

  • Genomics
  • Bioinformatics
  • Microbial comparative genomics

Background:

  • Whole genome DNA microarray genomotyping is crucial for comparing bacterial gene content.
  • Existing methods often ignore the spatial information of genes along the genome.

Purpose of the Study:

  • To develop a statistical approach for analyzing bacterial DNA microarray data that accounts for gene adjacency.
  • To improve the accuracy of determining gene presence or absence in bacterial genomes.

Main Methods:

  • A Hidden Markov Model (HMM) was developed to analyze gene content, incorporating the adjacency of genes.
  • The HMM was implemented in the R statistical language and tested on three bacterial datasets.
  • An Apache Struts web interface was created for user accessibility.

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Main Results:

  • The HMM approach demonstrated numerical stability and good convergence properties.
  • Error rates were reduced compared to methods ignoring spatial gene information.
  • The HMM method resolved the issue of determining cut-off values for gene absence classification.

Conclusions:

  • The HMM-based statistical method offers a more accurate and robust analysis of bacterial whole genome DNA microarray data.
  • Incorporating gene adjacency significantly enhances the reliability of gene presence/absence calls.
  • The developed web application and R implementation facilitate wider adoption of this advanced analytical technique.