Beyond Bioinformatics

Title

Combining algorithms to predict Bacterial protein sub-cellular location: Parallel versus concurrent implementations

Authors

Paul D. Taylor¹, Teresa K. Attwood² and Darren R. Flower^1,*

Affiliation

1. The Jenner Institute, University of Oxford, Compton, Newbury, Berkshire, RG20 7NN, UK; 2. Faculty of Life Sciences & School of Computer Science, The University of Manchester, Oxford Road, Manchester M13 9PT, UK

Email

darren.flower@jenner.ac.uk; * Corresponding author

Phone

+44 1635 577954

Fax

+44 1635 577908

Article Type

Prediction Model

Date

received November 24, 2006; accepted December 05, 2006; published online December 06, 2006

Abstract

We describe a novel and potentially important tool for candidate subunit vaccine selection through in silico reverse-vaccinology. A set of Bayesian networks able to make individual predictions for specific subcellular locations is implemented in three pipelines with different architectures: a parallel implementation with a confidence level-based decision engine and two serial implementations with a hierarchical decision structure, one initially rooted by prediction between membrane types and another rooted by soluble versus membrane prediction. The parallel pipeline outperformed the serial pipeline, but took twice as long to execute. The soluble-rooted serial pipeline outperformed the membrane-rooted predictor. Assessment using genomic test sets was more equivocal, as many more predictions are made by the parallel pipeline, yet the serial pipeline identifies 22 more of the 74 proteins of known location.

Keywords

beta barrel transmembrane protein; prokaryotic membrane proteins; Bayesian Networks; prediction method; subcellular location

Citation

Edited by

P. Kangueane

ISSN

0973-2063

Publisher

Biomedical Informatics Publishing Group

Copyright

Publisher

Copyright Transfer Agreement

The authors of published articles in Bioinformation automatically transfer the copyright to the publisher upon formal acceptance. However, the authors reserve right to use the information contained in the article for non commercial purposes.

License

This is an open-access article, which permits unrestricted use, distribution, and reproduction in any medium, for non-commercial purposes, provided the original author and source are credited.