Goodswen Stephen J, Kennedy Paul J, Ellis John T
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.
Bioinformatics. 2014 Aug 15;30(16):2381-3. doi: 10.1093/bioinformatics/btu300. Epub 2014 Apr 29.
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.
我们展示了Vacceed，这是一个高度可配置且可扩展的框架，旨在自动化真核病原体高通量计算机模拟疫苗候选物发现的过程。以来自目标病原体的数千个蛋白质序列作为输入，主要输出是由一组机器学习算法确定的蛋白质候选物排名列表。Vacceed有可能通过减少分配用于实验室验证的假候选物数量来节省时间和金钱。如果需要，Vacceed还可以从病原体基因组预测蛋白质序列。
Vacceed在Linux上进行了测试，可从https://github.com/sgoodswe/vacceed/releases免费下载（包括带有示例数据的工作示例）。Vacceed用户指南可从https://github.com/sgoodswe/vacceed获取。
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