A Hidden Markov model web application for analysing bacterial genomotyping DNA microarray experiments.
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.
Newton
Richard
R
School of Crystallography, Birkbeck College, University of London, London, UK. r.newton@mail.cryst.bbk.ac.uk
Hinds
Jason
J
Wernisch
Lorenz
L
D000465:Algorithms; D001185:Artificial Intelligence; D002874:Chromosome Mapping / Q000379:methods*; D016680:Genome, Bacterial / Q000235:genetics*; D005838:Genotype; D020407:Internet; D008390:Markov Chains; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D011381:Programming Languages