Evidence map›Paper›PMID 26460590›Full record

ArticleNature communications2015

Harnessing the landscape of microbial culture media to predict new organism-media pairings.

Matthew A Oberhardt, Raphy Zarecki, Sabine Gronow, Elke Lang, Hans-Peter Klenk, Uri Gophna, Eytan Ruppin

Open access · goldAbstract read
In one paragraph

Article in Nature communications, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
75citing papers in PubMed, 1 pooled it
3.8field-weighted citation impact, top 6% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

75 citing papers in PubMed, 1 synthesis or guideline pooled it, 161 citations in OpenAlex.

  1. Pooled it
  2. Bridging the gap:Gut microbes · 2026
    Review
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  4. Article
  5. Article
  6. Article
  7. Article
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  9. The Gut Bacteria ofInsects · 2025
    Article
  10. Article
  11. Explainable rule-based prediction of cultivation media for microbes.Computational and structural biotechnology journal · 2025
    Article
  12. Article
  13. Review
  14. Article
  15. Review
  16. Review
  17. Review
  18. Review
  19. Review
  20. Article

15 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 4 institutions in 4 countries.

Matthew A OberhardtBlavatnik School of Computer Sciences and Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.
Raphy ZareckiBlavatnik School of Computer Sciences and Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.
Sabine GronowLeibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures, Braunschweig 38124, Germany.
Elke LangLeibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures, Braunschweig 38124, Germany.
Hans-Peter KlenkSchool of Biology, Newcastle University, Newcastle upon Tyne NE1 7RU, UK.
Uri GophnaDepartment of Molecular Microbiology and Biotechnology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel.
Eytan RuppinBlavatnik School of Computer Sciences and Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.
Tel Aviv University · ILLeibniz Institute DSMZ – German Collection of Microorganisms and Cell Cultures · DENewcastle University · GBUniversity of Maryland, College Park · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Culturing microorganisms is a critical step in understanding and utilizing microbial life. Here we map the landscape of existing culture media by extracting natural-language media recipes into a Known Media Database (KOMODO), which includes >18,000 strain-media combinations, >3300 media variants and compound concentrations (the entire collection of the Leibniz Institute DSMZ repository). Using KOMODO, we show that although media are usually tuned for individual strains using biologically common salts, trace metals and vitamins/cofactors are the most differentiating components between defined media of strains within a genus. We leverage KOMODO to predict new organism-media pairings using a transitivity property (74% growth in new in vitro experiments) and a phylogeny-based collaborative filtering tool (83% growth in new in vitro experiments and stronger growth on predicted well-scored versus poorly scored media). These resources are integrated into a web-based platform that predicts media given an organism's 16S rDNA sequence, facilitating future cultivation efforts.

Indexed as

Culture MediaCulture TechniquesDatabases as TopicNatural Language ProcessingPhylogenyCulture Media

Identifiers

PMID26460590
PMCPMC4633754
OpenAlexW2261641299

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.