Antimicrobial Resistance Prediction in PATRIC and RAST.
Antimicrobial Resistance Prediction in PATRIC and RAST.
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DOI：
10.1038/srep27930
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发表时间：
2016-06-14
影响因子：
4.6
通讯作者：
Stevens R
中科院分区：
文献类型：
--
作者：
Davis JJ;Boisvert S;Brettin T;Kenyon RW;Mao C;Olson R;Overbeek R;Santerre J;Shukla M;Wattam AR;Will R;Xia F;Stevens R
细菌病原体中抗菌素耐药性（AMR）机制的出现和传播，加上有效抗生素数量的减少，已经造成了全球健康危机。能够在培养前识别AMR的遗传机制并预测细菌病原体的耐药表型，可以为临床决策提供信息并缩短反应时间。在PATRIC（http：//patricbrc.org/），我们多年来一直在收集带有AMR元数据的细菌基因组。为了推进表型预测和与AMR相关的基因组区域的识别，我们更新了PATRIC FTP服务器，以访问按AMR表型分组的基因组，以及包括最低抑制浓度在内的元数据。使用此基础设施，我们定制了AdaBoost（自适应增强）机器学习分类器，用于识别鲍曼不动杆菌的碳青霉烯耐药性、金黄色葡萄球菌的甲氧西林耐药性以及肺炎链球菌的β-内酰胺和复方新诺明耐药性，准确率范围为88- 99%。我们还对结核分枝杆菌中的异烟肼、卡那霉素、氧氟沙星、利福平和链霉素耐药性进行了研究，准确率为71- 88%。这组分类器已被用来提供一个初始的框架，物种特异性AMR表型和基因组特征预测的RAST和PATRIC注释服务。
The emergence and spread of antimicrobial resistance (AMR) mechanisms in bacterial pathogens, coupled with the dwindling number of effective antibiotics, has created a global health crisis. Being able to identify the genetic mechanisms of AMR and predict the resistance phenotypes of bacterial pathogens prior to culturing could inform clinical decision-making and improve reaction time. At PATRIC (http://patricbrc.org/), we have been collecting bacterial genomes with AMR metadata for several years. In order to advance phenotype prediction and the identification of genomic regions relating to AMR, we have updated the PATRIC FTP server to enable access to genomes that are binned by their AMR phenotypes, as well as metadata including minimum inhibitory concentrations. Using this infrastructure, we custom built AdaBoost (adaptive boosting) machine learning classifiers for identifying carbapenem resistance in Acinetobacter baumannii, methicillin resistance in Staphylococcus aureus, and beta-lactam and co-trimoxazole resistance in Streptococcus pneumoniae with accuracies ranging from 88–99%. We also did this for isoniazid, kanamycin, ofloxacin, rifampicin, and streptomycin resistance in Mycobacterium tuberculosis, achieving accuracies ranging from 71–88%. This set of classifiers has been used to provide an initial framework for species-specific AMR phenotype and genomic feature prediction in the RAST and PATRIC annotation services.