GPA: A Microbial Genetic Polymorphisms Assignments Tool in Metagenomic Analysis by Bayesian Estimation
GPA: A Microbial Genetic Polymorphisms Assignments Tool in Metagenomic Analysis by Bayesian Estimation
复制标题
GPA：贝叶斯估计宏基因组分析中的微生物遗传多态性分配工具
DOI：
10.1016/j.gpb.2018.12.005
复制
发表时间：
2019-02-01
影响因子：
9.5
通讯作者：
Chen，Chen
中科院分区：
文献类型：
--
作者：
Li，Jiarui;Du，Pengcheng;Chen，Chen
宏基因组样本中耐药菌的鉴定对公共卫生和食品安全至关重要，而新一代测序技术（NGS）为鉴定人类和其他物种的遗传变异和构建基因型与表型之间的相关性提供了强有力的工具。然而，对于复杂的细菌样本，缺乏一个强大的生物信息学工具来识别给定基因的遗传多态性或拷贝数变异（CNVs）。在这里，我们提供了一个贝叶斯框架的基因型估计的混合物的多种细菌，命名为遗传多态性分析（GPA）。仿真结果表明，GPA降低了CNV和SNV识别的错误发现率（FDR）和平均绝对误差（MAE）。该框架通过多细菌混合模型的肺炎克雷伯氏菌全基因组测序和Pool-seqdata进行验证，显示了两个群体之间AMR基因CNV和SNV等位基因分数检测的高准确性。对两个样本间AMR基因组分变化的定量研究表明，与单个菌株中观察到的AMR模式具有良好的一致性。此外，该框架与基因组注释和群体比较工具一起已集成到应用程序中，该应用程序可以为不可培养的临床样本中的AMR基因鉴定和定量提供完整的解决方案。GPA软件包可在https://github.com/IID-DTH/GPA-package上获得。
Identifying antimicrobial resistant (AMR) bacteria inmetagenomicssamples 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 identifygenetic polymorphismsor 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 andPool-seqdata fromKlebsiella pneumoniaewith 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.