Identifying antimicrobial resistant (AMR) bacteria in metagenomics samples is essential for public health and food safety. Next-generation sequencing (NGS) technology has provided a powerful tool in identifying the genetic variation and constructing the correlations between genotype and phenotype in humans and other species. However, for complex bacterial samples, there lacks a powerful bioinformatic tool to identify genetic polymorphisms or copy number variations (CNVs) for given genes. Here we provide a Bayesian framework for genotype estimation for mixtures of multiple bacteria, named as Genetic Polymorphisms Assignments (GPA). Simulation results showed that GPA has reduced the false discovery rate (FDR) and mean absolute error (MAE) in CNV and single nucleotide variant (SNV) identification. This framework was validated by whole-genome sequencing and Pool-seq data from Klebsiella pneumoniae with multiple bacteria mixture models, and showed the high accuracy in the allele fraction detections of CNVs and SNVs in AMR genes between two populations. The quantitative study on the changes of AMR genes fraction between two samples showed a good consistency with the AMR pattern observed in the individual strains. Also, the framework together with the genome annotation and population comparison tools has been integrated into an application, which could provide a complete solution for AMR gene identification and quantification in unculturable clinical samples. The GPA package is available at https://github.com/IID-DTH/GPA-package.
随着细菌耐药的增加，对于细菌抗生素耐药的监测，在公共卫生和食品安全领域越来越重要。近期，高通量测序技术的重大突破，为人类和其他物种中基因型检测提供了有效的工具，也为建立基因型-表型关联提供了重要手段。但是，由于临床混合样本中，菌群结构复杂，相关的生物信息挖掘工具及软件，仍然缺乏。本研究中，我们建立了一套基于贝叶斯模型的工具，用于复杂细菌菌群中特定基因型检测，称为GPA。在模拟肺炎克雷伯菌基因组数据中，该工具不仅可检测CNV和SNV，而且有效的降低了混合样本中假阳性发现率FDR和平均绝对偏差MAE。进而，我们利用肺炎克雷伯菌的混合测定数据，分析在2009年和2013年两年病人中耐药基因的CNV和SNV突变频率的差异，该结果可成功构建肺炎克雷伯菌基因-耐药表型关联。最后，我们使用了公开的宏基因组数据进行软件评估，表明该方法可用于临床样本中非培养的细菌的鉴定及其耐药基因的定量分析。该工具已经提交至网站 https://github.com/IID-DTH/GPA-package。





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