Zolfo Moreno, Tett Adrian, Jousson Olivier, Donati Claudio, Segata Nicola
Centre for Integrative Biology, University of Trento, Trento, TN 38123, Italy.
Computational Biology Unit, Research and Innovation Centre, Fondazione Edmund Mach, Via Edmund Mach 1, San Michele all'Adige 38010, Italy.
Nucleic Acids Res. 2017 Jan 25;45(2):e7. doi: 10.1093/nar/gkw837. Epub 2016 Sep 19.
Metagenomic characterization of microbial communities has the potential to become a tool to identify pathogens in human samples. However, software tools able to extract strain-level typing information from metagenomic data are needed. Low-throughput molecular typing schema such as Multilocus Sequence Typing (MLST) are still widely used and provide a wealth of strain-level information that is currently not exploited by metagenomic methods. We introduce MetaMLST, a software tool that reconstructs the MLST loci of microorganisms present in microbial communities from metagenomic data. Tested on synthetic and spiked-in real metagenomes, the pipeline was able to reconstruct the MLST sequences with >98.5% accuracy at coverages as low as 1×. On real samples, the pipeline showed higher sensitivity than assembly-based approaches and it proved successful in identifying strains in epidemic outbreaks as well as in intestinal, skin and gastrointestinal microbiome samples.
微生物群落的宏基因组特征分析有潜力成为一种识别人类样本中病原体的工具。然而,需要能够从宏基因组数据中提取菌株水平分型信息的软件工具。诸如多位点序列分型(MLST)等低通量分子分型方案仍被广泛使用,并提供了大量目前宏基因组方法尚未利用的菌株水平信息。我们介绍了MetaMLST,这是一种软件工具,可从宏基因组数据中重建微生物群落中存在的微生物的MLST基因座。在合成和掺入真实宏基因组上进行测试,该流程能够在低至1×的覆盖率下以>98.5%的准确率重建MLST序列。在真实样本上,该流程显示出比基于组装的方法更高的灵敏度,并且在识别疫情爆发以及肠道、皮肤和胃肠道微生物组样本中的菌株方面证明是成功的。