Autometa: Automated extraction of microbial genomes from individual shotgun metagenomes

Motivation: 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 cultu...

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Bibliographic Details
Published inbioRxiv
Main Authors Miller, Ian J, Rees, Evan R, Ross, Jennifer, Miller, Izaak, Baxa, Jared, Lopera, Juan, Kerby, Robert L, Rey, Federico E, Kwan, Jason C
Format Paper
LanguageEnglish
Published Cold Spring Harbor Cold Spring Harbor Laboratory Press 30.04.2018
Subjects
Automation
Embedding
Genomes
Homology
Pattern recognition
Stochasticity
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Summary:Motivation: 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. Existing methods, however, suffer from: (1) reliance on human pattern recognition, which is inherently unscalable; (2) requirement for multiple co-assembled metagenomes, which degrades assembly quality due to strain variance; and (3) assumption of prior host genome removal not feasible for non-model hosts. We therefore devised a fully-automated pipeline, termed "Autometa," to address these issues. Results: Autometa implements a method for taxonomic partitioning of contigs based on predicted protein homology, and this was shown to vastly improve binning in host-associated and complex metagenomes. Autometa's method of automated clustering, based on Barnes-Hut Stochastic Neighbor Embedding (BH-tSNE) and DBSCAN, was shown to be highly scalable, outperforming other binning pipelines in complex simulated datasets. Availability and implementation: 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). Footnotes * Analysis, Figures 3 and 4 updated to include BusyBee Web; Added discussion of perplexity parameter in BH-tSNE along with Figures S1-S12; Acknowledgements updated to include individuals who provided feedback on version 1.
DOI:10.1101/251462