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- Title
Vacceed: a high-throughput in silico vaccine candidate discovery pipeline for eukaryotic pathogens based on reverse vaccinology.
- Authors
Goodswen, Stephen J.; Kennedy, Paul J.; Ellis, John T.
- Abstract
Summary: 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.Availability: 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.Contact: John.Ellis@uts.edu.au
- Subjects
VACCINE research; PATHOGENIC microorganisms; PROTEIN research; MACHINE learning; GENOMICS
- Publication
Bioinformatics, 2014, Vol 30, Issue 16, p1
- ISSN
1367-4803
- Publication type
Article
- DOI
10.1093/bioinformatics/btu300