Administration of appropriate therapeutic regimes for infections arising from pathogenic species of Burkholderia is critically dependent upon rapid and accurate diagnoses. The purpose of this work is to establish a bioinformatic pipeline to assess protein sequences for their potential as diagnostic targets for the detection of Burkholderia species. Data are presented showing both a bioinformatic methodology for prediction of surface-associated and secreted proteins and its application to a test dataset of proteins from the pathogen B. pseudomallei. A subset of proteins, known to be produced by the organism, is identified which represents potential targets for development of new diagnostic reagents. In addition, a 'reverse diagnostics' bioinformatics approach has been established which can now be extended to whole genome analyses.
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