分析菌群基因水平转移的信息学工具
  • MetaCHIP主要通过BLASTN搜索最佳匹配、进化树构建等两种方法分析水平基因转移;
  • 该流程整合了宏基因组序列组装、分箱、基因预测、序列比对和进化树分析的多个生物信息学工具;
  • 对仿真序列的测试表明该工具可以得出近期和非近期的基因水平转移事件;
  • 应用于人肠道和土壤菌群等真实实验数据时,与已发表研究中的结果吻合,同时还能额外发现新的转移事件;
  • 基因转移后发生大程度突变、序列组装、分箱结果等可能限制该方法的运用。
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高春辉
《Microbiome》近期发表的研究介绍了一种新的生物信息学分析工具——MetaCHIP,可以在不依赖于参考基因组的情况下,用于宏基因组测序数据分析,探索群落水平上的水平基因转移事件。
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Microbiome [IF:11.607]

MetaCHIP: community-level horizontal gene transfer identification through the combination of best-match and phylogenetic approaches

MetaCHIP:结合最佳匹配和系统发生学方法分析群落中的基因水平转移

10.1186/s40168-019-0649-y

2019-03-04, Article

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BACKGROUND: 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.
RESULTS: 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.
CONCLUSION: 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 .

First Authors:
Weizhi Song

Correspondence Authors:
Torsten Thomas

All Authors:
Weizhi Song,Bernd Wemheuer,Shan Zhang,Kerrin Steensen,Torsten Thomas

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