Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Autometa: Automated extraction of microbial genomes from individual shotgun metagenomes

Ian J. Miller, Evan R. Rees, Jennifer Ross, Izaak Miller, Jared Baxa, Juan Lopera, Robert L. Kerby, Federico E. Rey, View ORCID ProfileJason C. Kwan
doi: https://doi.org/10.1101/251462
Ian J. Miller
1Division of Pharmaceutical Sciences, University of Wisconsin‐‐Madison, Madison, WI 53705, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Evan R. Rees
1Division of Pharmaceutical Sciences, University of Wisconsin‐‐Madison, Madison, WI 53705, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jennifer Ross
1Division of Pharmaceutical Sciences, University of Wisconsin‐‐Madison, Madison, WI 53705, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Izaak Miller
1Division of Pharmaceutical Sciences, University of Wisconsin‐‐Madison, Madison, WI 53705, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jared Baxa
1Division of Pharmaceutical Sciences, University of Wisconsin‐‐Madison, Madison, WI 53705, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Juan Lopera
1Division of Pharmaceutical Sciences, University of Wisconsin‐‐Madison, Madison, WI 53705, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Robert L. Kerby
2Department of Bacteriology, University of Wisconsin‐‐Madison, Madison, WI 53706, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Federico E. Rey
2Department of Bacteriology, University of Wisconsin‐‐Madison, Madison, WI 53706, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jason C. Kwan
1Division of Pharmaceutical Sciences, University of Wisconsin‐‐Madison, Madison, WI 53705, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Jason C. Kwan
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

Abstract

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).

Contact jason.kwan{at}wisc.edu

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Back to top
PreviousNext
Posted January 22, 2018.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Autometa: Automated extraction of microbial genomes from individual shotgun metagenomes
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Autometa: Automated extraction of microbial genomes from individual shotgun metagenomes
Ian J. Miller, Evan R. Rees, Jennifer Ross, Izaak Miller, Jared Baxa, Juan Lopera, Robert L. Kerby, Federico E. Rey, Jason C. Kwan
bioRxiv 251462; doi: https://doi.org/10.1101/251462
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Autometa: Automated extraction of microbial genomes from individual shotgun metagenomes
Ian J. Miller, Evan R. Rees, Jennifer Ross, Izaak Miller, Jared Baxa, Juan Lopera, Robert L. Kerby, Federico E. Rey, Jason C. Kwan
bioRxiv 251462; doi: https://doi.org/10.1101/251462

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (3609)
  • Biochemistry (7590)
  • Bioengineering (5533)
  • Bioinformatics (20833)
  • Biophysics (10347)
  • Cancer Biology (7998)
  • Cell Biology (11663)
  • Clinical Trials (138)
  • Developmental Biology (6619)
  • Ecology (10227)
  • Epidemiology (2065)
  • Evolutionary Biology (13648)
  • Genetics (9557)
  • Genomics (12860)
  • Immunology (7932)
  • Microbiology (19575)
  • Molecular Biology (7678)
  • Neuroscience (42193)
  • Paleontology (309)
  • Pathology (1259)
  • Pharmacology and Toxicology (2208)
  • Physiology (3272)
  • Plant Biology (7064)
  • Scientific Communication and Education (1295)
  • Synthetic Biology (1953)
  • Systems Biology (5435)
  • Zoology (1119)