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 can...

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Bibliographic Details
Published in:Bioinformatics (Oxford, England) England), 2014-08, Vol.30 (16), p.2381-2383
Main Authors: Goodswen, Stephen J, Kennedy, Paul J, Ellis, John T
Format: Article
Language:English
Subjects:
Algorithms
Applications Notes
Artificial Intelligence
Computer Simulation
Sequence Analysis, Protein
Software
Vaccines - chemistry
Vaccines - genetics
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Description
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. 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.
ISSN:1367-4803
1367-4811
DOI:10.1093/bioinformatics/btu300