Division of Pharmaceutical Sciences, School of Pharmacy, University of Wisconsin-Madison, 777 Highland Avenue, Madison, WI 53705, USA.
Department of Bacteriology, University of Wisconsin-Madison, 1550 Linden Drive, Madison, WI 53706, USA.
Nucleic Acids Res. 2019 Jun 4;47(10):e57. doi: 10.1093/nar/gkz148.
Shotgun metagenomics is a powerful, high-resolution technique enabling the study of microbial communities in situ. However, species-level resolution is only achieved after a process of 'binning' where contigs predicted to originate from the same genome are clustered. Such culture-independent sequencing frequently unearths novel microbes, and so various methods have been devised for reference-free binning. As novel microbiomes of increasing complexity are explored, sometimes associated with non-model hosts, robust automated binning methods are required. Existing methods struggle with eukaryotic contamination and cannot handle highly complex single metagenomes. We therefore developed an automated binning pipeline, termed 'Autometa', to address these issues. This command-line application integrates sequence homology, nucleotide composition, coverage and the presence of single-copy marker genes to separate microbial genomes from non-model host genomes and other eukaryotic contaminants, before deconvoluting individual genomes from single metagenomes. The method is able to effectively separate over 1000 genomes from a metagenome, allowing the study of previously intractably complex environments at the level of single species. Autometa is freely available at https://bitbucket.org/jason_c_kwan/autometa and as a docker image at https://hub.docker.com/r/jasonkwan/autometa under the GNU Affero General Public License 3 (AGPL 3).
shotgun 宏基因组学是一种强大的、高分辨率的技术,能够原位研究微生物群落。然而,只有在“分类”过程之后才能实现物种水平的分辨率,在此过程中,预测源自同一基因组的 contigs 被聚类。这种与培养无关的测序经常会发现新的微生物,因此已经设计了各种用于无参考分类的方法。随着越来越复杂的新型微生物组的探索,有时与非模型宿主相关联,需要强大的自动化分类方法。现有的方法难以处理真核生物污染,并且无法处理高度复杂的单个宏基因组。因此,我们开发了一种自动化分类管道,称为“Autometa”,以解决这些问题。这个命令行应用程序集成了序列同源性、核苷酸组成、覆盖度和单拷贝标记基因的存在,以将微生物基因组与非模型宿主基因组和其他真核生物污染物分离,然后从单个宏基因组中推断出单个基因组。该方法能够有效地从宏基因组中分离出超过 1000 个基因组,从而能够研究以前难以处理的复杂环境中单种水平的情况。Autometa 可在 https://bitbucket.org/jason_c_kwan/autometa 上免费获得,并可在 https://hub.docker.com/r/jasonkwan/autometa 上作为 Docker 映像获得,许可证是 GNU Affero General Public License 3(AGPL 3)。