Centre for Marine Bio-Innovation, University of New South Wales, Sydney, NSW, 2052, Australia.
School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, 2052, Australia.
Microbiome. 2019 Mar 4;7(1):36. doi: 10.1186/s40168-019-0649-y.
Metagenomic datasets provide an opportunity to study horizontal gene transfer (HGT) on the level of a microbial community. However, current HGT detection methods cannot be applied to community-level datasets or require reference genomes. Here, we present MetaCHIP, a pipeline for reference-independent HGT identification at the community level.
Assessment of MetaCHIP's performance on simulated datasets revealed that it can predict HGTs with various degrees of genetic divergence from metagenomic datasets. The results also indicated that the detection of very recent gene transfers (i.e. those with low levels of genetic divergence) from metagenomics datasets is largely affected by the read assembly step. Comparison of MetaCHIP with a previous analysis on soil bacteria showed a high level of consistency for the prediction of recent HGTs and revealed a large number of additional non-recent gene transfers, which can provide new biological and ecological insight. Assessment of MetaCHIP's performance on real metagenomic datasets confirmed the role of HGT in the spread of genes related to antibiotic resistance in the human gut microbiome. Further testing also showed that functions related to energy production and conversion as well as carbohydrate transport and metabolism are frequently transferred among free-living microorganisms.
MetaCHIP provides an opportunity to study HGTs among members of a microbial community and therefore has several applications in the field of microbial ecology and evolution. MetaCHIP is implemented in Python and freely available at https://github.com/songweizhi/MetaCHIP .
宏基因组数据集提供了在微生物群落水平上研究水平基因转移（HGT）的机会。然而，目前的 HGT 检测方法不能应用于群落级数据集或需要参考基因组。在这里，我们提出了 MetaCHIP，这是一种用于在群落水平上进行无参考 HGT 识别的管道。
在模拟数据集上评估 MetaCHIP 的性能表明，它可以预测来自宏基因组数据集的具有不同遗传分化程度的 HGT。结果还表明，从宏基因组数据集中检测非常近期的基因转移（即遗传分化水平较低的基因转移）在很大程度上受到读组装步骤的影响。MetaCHIP 与之前对土壤细菌的分析进行比较，对近期 HGT 的预测具有很高的一致性，并揭示了大量额外的非近期基因转移，这可以提供新的生物学和生态学见解。对真实宏基因组数据集的 MetaCHIP 性能评估证实了 HGT 在人类肠道微生物组中抗生素抗性相关基因传播中的作用。进一步的测试还表明，与能量产生和转化以及碳水化合物运输和代谢相关的功能在自由生活的微生物中经常转移。
MetaCHIP 为研究微生物群落成员之间的 HGT 提供了机会，因此在微生物生态学和进化领域有多种应用。MetaCHIP 是用 Python 实现的，并可在 https://github.com/songweizhi/MetaCHIP 上免费获得。
