Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Berlin, Germany.
Efficient Algorithms for Omics Data, Max Planck Institute for Molecular Genetics, and Algorithmic Bioinformatics, Institute for Bioinformatics, Freie Universität Berlin, Berlin, Germany.
PLoS Comput Biol. 2019 Jul 23;15(7):e1007208. doi: 10.1371/journal.pcbi.1007208. eCollection 2019 Jul.
Horizontal gene transfer (HGT) has changed the way we regard evolution. Instead of waiting for the next generation to establish new traits, especially bacteria are able to take a shortcut via HGT that enables them to pass on genes from one individual to another, even across species boundaries. The tool Daisy offers the first HGT detection approach based on read mapping that provides complementary evidence compared to existing methods. However, Daisy relies on the acceptor and donor organism involved in the HGT being known. We introduce DaisyGPS, a mapping-based pipeline that is able to identify acceptor and donor reference candidates of an HGT event based on sequencing reads. Acceptor and donor identification is akin to species identification in metagenomic samples based on sequencing reads, a problem addressed by metagenomic profiling tools. However, acceptor and donor references have certain properties such that these methods cannot be directly applied. DaisyGPS uses MicrobeGPS, a metagenomic profiling tool tailored towards estimating the genomic distance between organisms in the sample and the reference database. We enhance the underlying scoring system of MicrobeGPS to account for the sequence patterns in terms of mapping coverage of an acceptor and donor involved in an HGT event, and report a ranked list of reference candidates. These candidates can then be further evaluated by tools like Daisy to establish HGT regions. We successfully validated our approach on both simulated and real data, and show its benefits in an investigation of an outbreak involving Methicillin-resistant Staphylococcus aureus data.
水平基因转移 (HGT) 改变了我们对进化的看法。与等待下一代建立新特征不同，细菌特别能够通过 HGT 走捷径，使它们能够将基因从一个个体传递到另一个个体，甚至跨越物种界限。Daisy 提供了基于读映射的第一个 HGT 检测方法，与现有方法相比提供了补充证据。然而，Daisy 依赖于涉及 HGT 的受体和供体生物是已知的。我们引入了 DaisyGPS，这是一种基于映射的管道，能够根据测序读取识别 HGT 事件的受体和供体参考候选者。受体和供体的识别类似于基于测序读取的宏基因组样本中的物种识别，这是宏基因组分析工具解决的问题。然而，受体和供体参考具有某些特性，因此这些方法不能直接应用。DaisyGPS 使用 MicrobeGPS，这是一种专门用于估计样本中生物与参考数据库之间基因组距离的宏基因组分析工具。我们增强了 MicrobeGPS 的底层评分系统，以根据 HGT 事件中涉及的受体和供体的映射覆盖率来考虑序列模式，并报告参考候选者的排名列表。然后，可以使用像 Daisy 这样的工具来进一步评估这些候选者，以确定 HGT 区域。我们在模拟和真实数据上成功验证了我们的方法，并在涉及耐甲氧西林金黄色葡萄球菌数据的暴发调查中展示了它的优势。
