Reverse vaccinology (RevVac) is an emerging field that uses bioinformatics to identify vaccine candidates directly from the genome sequence of pathogenic bacteria. The long-term goal of the proposed research is to identify new vaccine candidates that may be formulated into subunit vaccines that protect against bacterial pathogens. The objective of this particular application is to dramatically improve upon methods of RevVac developed in my laboratory. The central hypothesis is that my RevVac procedure will be enhanced by building larger training data sets, incorporating new protein annotation, assessing multiple machine learning methods, investigating additional validation techniques, and by facilitating open access to predicted vaccine candidates to the vaccine community through the Bacterial Protective Antigen Database (BPAD).Guided by published preliminary data in the journal Vaccine, my hypothesis will be tested by pursuing the following objectives: (1) To improve the prediction of vaccine candidates, (2) To understand what makes a bacterial protein a good vaccine candidate, and (3) To create a vaccine resource for bacteriologists. The rationale for the proposed research is that once there is high confidence in predicted vaccine candidates, then only a small number will need to be tested in animal models in order to identify those with protective effects that can be formulated into subunit vaccines.This proposal is responsive to the objectives of the work programme since it will establish my novel research talents and knowledge with respect to reverse vaccinology and gene expression biomarker analysis in the EU. In addition, I will bring a plethora of longstanding collaborations solidified while working in the USA. Finally, the University of Southampton is fully committed to my long-term EU integration vaccinology (RevVac) is an emerging field that uses bioinformatics to identify vaccine candidates directly from the genome sequence of pathogenic bacteria.