BACKGROUND: Since a milestone work on Neisseria meningitidis B, Reverse 
Vaccinology has strongly enhanced the identification of vaccine candidates by 
replacing several experimental tasks using in silico prediction steps. These 
steps have allowed scientists to face the selection of antigens from the 
predicted proteome of pathogens, for which cell culture is difficult or 
impossible, saving time and money. However, this good example of 
bioinformatics-driven immunology can be further developed by improving in silico 
steps and implementing biologist-friendly tools.
RESULTS: We introduce NERVE (New Enhanced Reverse Vaccinology Environment), an 
user-friendly software environment for the in silico identification of the best 
vaccine candidates from whole proteomes of bacterial pathogens. The software 
integrates multiple robust and well-known algorithms for protein analysis and 
comparison. Vaccine candidates are ranked and presented in a html table showing 
relevant information and links to corresponding primary data. Information 
concerning all proteins of the analyzed proteome is not deleted along selection 
steps but rather flows into an SQL database for further mining and analyses.
CONCLUSION: After learning from recent years' works in this field and analysing 
a large dataset, NERVE has been implemented and tuned as the first available 
tool able to rank a restricted pool (approximately 8-9% of the whole proteome) 
of vaccine candidates and to show high recall (approximately 75-80%) of known 
protective antigens. These vaccine candidates are required to be "safe" (taking 
into account autoimmunity risk) and "easy" for further experimental, 
high-throughput screening (avoiding possibly not soluble antigens). NERVE is 
expected to help save time and money in vaccine design and is available as an 
additional file with this manuscript; updated versions will be available at 
http://www.bio.unipd.it/molbinfo.