Vacceed: a high-throughput in silico vaccine candidate discovery pipeline for eukaryotic pathogens based on reverse vaccinology.
We present Vacceed, a highly configurable and scalable framework designed to automate the process of high-throughput in silico vaccine candidate discovery for eukaryotic pathogens. Given thousands of protein sequences from the target pathogen as input, the main output is a ranked list of protein candidates determined by a set of machine learning algorithms. Vacceed has the potential to save time and money by reducing the number of false candidates allocated for laboratory validation. Vacceed, if required, can also predict protein sequences from the pathogen's genome.


METHODS
Vacceed is tested on Linux and can be freely downloaded from https://github.com/sgoodswe/vacceed/releases (includes a worked example with sample data). Vacceed User Guide can be obtained from https://github.com/sgoodswe/vacceed.
School of Medical and Molecular Biosciences, The ithree Institute and Faculty of Engineering and Information Technology, School of Software, The Centre for Quantum Computation and Intelligent Systems, University of Technology Sydney (UTS), Ultimo, NSW 2007, Australia.
Stephen J
SJ
Goodswen
School of Medical and Molecular Biosciences, The ithree Institute and Faculty of Engineering and Information Technology, School of Software, The Centre for Quantum Computation and Intelligent Systems, University of Technology Sydney (UTS), Ultimo, NSW 2007, Australia.
Paul J
PJ
Kennedy
School of Medical and Molecular Biosciences, The ithree Institute and Faculty of Engineering and Information Technology, School of Software, The Centre for Quantum Computation and Intelligent Systems, University of Technology Sydney (UTS), Ultimo, NSW 2007, Australia.
John T
JT
Ellis
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D020539:Sequence Analysis, Protein; D012984:Software; D014612:Vaccines / Q000737:chemistry* / Q000235:genetics
D014612:Vaccines