PDF(Pubmed)
Abstract：
CONCLUSIONS: The reliable and timely recognition of outbreaks is a key component of public health surveillance for foodborne diseases. Whole genome sequencing (WGS) offers high resolution typing of foodborne bacterial pathogens and facilitates the accurate detection of outbreaks. This detection relies on grouping WGS data into clusters at an appropriate genetic threshold. However, methods and tools for selecting and adjusting such thresholds according to the required resolution of surveillance and epidemiological context are lacking. Here we present DODGE (Dynamic Outbreak Detection for Genomic Epidemiology), an algorithm to dynamically select and compare these genetic thresholds. DODGE can analyse expanding datasets over time and clusters that are predicted to correspond to outbreaks (or \"investigation clusters\") can be named with established genomic nomenclature systems to facilitate integrated analysis across jurisdictions. DODGE was tested in two real-world Salmonella genomic surveillance datasets of different duration, 2 months from Australia and 9 years from the United Kingdom. In both cases only a minority of isolates were identified as investigation clusters. Two known outbreaks in the United Kingdom dataset were detected by DODGE and were recognized at an earlier timepoint than the outbreaks were reported. These findings demonstrated the potential of the DODGE approach to improve the effectiveness and timeliness of genomic surveillance for foodborne diseases and the effectiveness of the algorithm developed.
METHODS: DODGE is freely available at https://github.com/LanLab/dodge and can easily be installed using Conda.
METHODS: DODGE is freely available at https://github.com/LanLab/dodge and can easily be installed using Conda.
摘要：
结论：可靠和及时地识别疾病暴发是食源性疾病公共卫生监测的关键组成部分。全基因组测序（WGS）可提供食源性细菌病原体的高分辨率分型，并有助于准确检测疫情。这种检测依赖于以适当的遗传阈值将WGS数据分组为簇，然而,缺乏根据监测和流行病学背景所需分辨率选择和调整此类阈值的方法和工具。在这里，我们介绍DODGE（基因组流行病学的动态爆发检测），一种动态选择和比较这些遗传阈值的算法。DODGE可以分析随着时间的推移而扩展的数据集，并且可以使用已建立的基因组命名系统来命名预测与爆发相对应的集群（或“调查集群”），以促进跨司法管辖区的综合分析。DODGE在两个不同持续时间的真实世界基因组监测数据集中进行了测试，从澳大利亚出发两个月，从英国出发9年。在这两种情况下，只有少数分离株被确定为调查簇。DODGE在英国数据集中发现了两次已知的暴发，并在比报告的暴发更早的时间点被发现。这些发现证明了DODGE方法在提高食源性疾病基因组监测的有效性和及时性以及所开发算法的有效性方面的潜力。
背景：DODGE可在https://github.com/LanLab/dodge免费获得，可以使用Conda轻松安装。
背景：补充数据可在Bioinformatics在线获得。
背景：DODGE可在https://github.com/LanLab/dodge免费获得，可以使用Conda轻松安装。
背景：补充数据可在Bioinformatics在线获得。
