Antimicrobial resistance is becoming a major threat to public health throughout the world. Researchers are attempting to contrast it by developing both new antibiotics and patient-specific treatments. In the second case, whole-genome sequencing has had a huge impact in two ways: first, it is becoming cheaper and faster to perform whole-genome sequencing, and this makes it competitive with respect to standard phenotypic tests; second, it is possible to statistically associate the phenotypic patterns of resistance to specific mutations in the genome. Therefore, it is now possible to develop catalogues of genomic variants associated with resistance to specific antibiotics, in order to improve prediction of resistance and suggest treatments. It is essential to have robust methods for identifying mutations associated to resistance and continuously updating the available catalogues. This work proposes a general method to study minimal inhibitory concentration distributions and to identify clusters of strains showing different levels of resistance to antimicrobials. Once the clusters are identified and strains allocated to each of them, it is possible to perform regression method to identify with high statistical power the mutations associated with resistance. The method is applied to a new 96-well microtiter plate used for testing Mycobacterium tuberculosis.

中文翻译：

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通过贝叶斯混合模型对最小抑制浓度数据进行聚类：检测结核分枝杆菌耐药突变的应用。

抗生素耐药性正在成为全世界公共卫生的主要威胁。研究人员正试图通过开发新的抗生素和针对患者的治疗方法来对此进行对比。在第二种情况下，全基因组测序在两个方面产生了巨大影响：首先，进行全基因组测序变得更便宜、更快，这使得它相对于标准表型测试具有竞争力；其次，可以将耐药表型模式与基因组中的特定突变进行统计关联。因此，现在可以开发与特定抗生素耐药性相关的基因组变异目录，以改善耐药性预测并提出治疗建议。必须有强大的方法来识别与耐药性相关的突变并不断更新可用的目录。这项工作提出了一种研究最小抑菌浓度分布并识别表现出不同水平抗菌药物耐药性的菌株簇的通用方法。一旦识别出簇并将菌株分配给每个簇，就可以执行回归方法以高统计能力识别与耐药性相关的突变。该方法应用于用于检测结核分枝杆菌的新型 96 孔微量滴定板。