PDF(Pubmed)
Abstract：
OBJECTIVE: Much has been written about the utility of genomic databases to public health. Within food safety these databases contain data from two types of isolates-those from patients (i.e., clinical) and those from non-clinical sources (e.g., a food manufacturing environment). A genetic match between isolates from these sources represents a signal of interest. We investigate the match rate within three large genomic databases (Listeria monocytogenes, Escherichia coli, and Salmonella) and the smaller Cronobacter database; the databases are part of the Pathogen Detection project at NCBI (National Center for Biotechnology Information).
RESULTS: Currently, the match rate of clinical isolates to non-clinical isolates is 33% for L. monocytogenes, 46% for Salmonella, and 7% for E. coli. These match rates are associated with several database features including the diversity of the organism, the database size, and the proportion of non-clinical BioSamples. Modeling match rate via logistic regression showed relatively good performance. Our prediction model illustrates the importance of populating databases with non-clinical isolates to better identify a match for clinical samples. Such information should help public health officials prioritize surveillance strategies and show the critical need to populate fledgling databases (e.g., Cronobacter sakazakii).
RESULTS: Currently, the match rate of clinical isolates to non-clinical isolates is 33% for L. monocytogenes, 46% for Salmonella, and 7% for E. coli. These match rates are associated with several database features including the diversity of the organism, the database size, and the proportion of non-clinical BioSamples. Modeling match rate via logistic regression showed relatively good performance. Our prediction model illustrates the importance of populating databases with non-clinical isolates to better identify a match for clinical samples. Such information should help public health officials prioritize surveillance strategies and show the critical need to populate fledgling databases (e.g., Cronobacter sakazakii).
摘要：
目的：关于基因组数据库在公共卫生中的应用已经写了很多。在食品安全中，这些数据库包含来自两种类型的分离株的数据-来自患者的数据(即，临床)和非临床来源(例如，食品制造环境)。来自这些来源的分离株之间的遗传匹配代表了感兴趣的信号。我们调查了三个大型基因组数据库(单核细胞增生李斯特菌，大肠杆菌,和沙门氏菌）和较小的Cronobacter数据库；这些数据库是NCBI（国家生物技术信息中心）病原体检测项目的一部分。
结果：目前，单核细胞增生李斯特菌的临床分离株与非临床分离株的匹配率为33%，46%为沙门氏菌，和7%的大肠杆菌。这些匹配率与几个数据库特征相关，包括生物体的多样性，数据库大小，和非临床生物样品的比例。通过逻辑回归建模匹配率显示出相对较好的性能。我们的预测模型说明了用非临床分离株填充数据库以更好地识别临床样品的匹配的重要性。此类信息应有助于公共卫生官员优先考虑监测策略，并显示填充新兴数据库的关键需求(例如，SakazakiiCronobacter).
结果：目前，单核细胞增生李斯特菌的临床分离株与非临床分离株的匹配率为33%，46%为沙门氏菌，和7%的大肠杆菌。这些匹配率与几个数据库特征相关，包括生物体的多样性，数据库大小，和非临床生物样品的比例。通过逻辑回归建模匹配率显示出相对较好的性能。我们的预测模型说明了用非临床分离株填充数据库以更好地识别临床样品的匹配的重要性。此类信息应有助于公共卫生官员优先考虑监测策略，并显示填充新兴数据库的关键需求(例如，SakazakiiCronobacter).
