SIMMER employs similarity algorithms to accurately identify human gut microbiome species and enzymes capable of known chemical transformations

  1. Annamarie E Bustion
  2. Renuka R Nayak
  3. Ayushi Agrawal
  4. Peter J Turnbaugh
  5. Katherine S Pollard  Is a corresponding author
  1. University of California, San Francisco, United States
  2. Gladstone Institutes, United States

Peer review process

This article was accepted for publication via eLife's original publishing model. eLife publishes the authors' accepted manuscript as a PDF only version before the full Version of Record is ready for publication. Peer reviews are published along with the Version of Record.

History

  1. Version of Record published
  2. Accepted Manuscript updated
  3. Accepted Manuscript published
  4. Accepted
  5. Preprint posted
  6. Received

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Annamarie E Bustion
  2. Renuka R Nayak
  3. Ayushi Agrawal
  4. Peter J Turnbaugh
  5. Katherine S Pollard
(2023)
SIMMER employs similarity algorithms to accurately identify human gut microbiome species and enzymes capable of known chemical transformations
eLife 12:e82401.
https://doi.org/10.7554/eLife.82401

Share this article

https://doi.org/10.7554/eLife.82401