Evidence map›Paper›PMID 39361898›Full record

ReviewThe ISME journal2024

Leveraging genomic information to predict environmental preferences of bacteria.

Josep Ramoneda, Michael Hoffert, Elias Stallard-Olivera, Emilio O Casamayor, Noah Fierer

Abstract readReview
In one paragraph

Review in The ISME journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
–field-weighted citation impact
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

16 citing papers in PubMed.

  1. Article
  2. Article
  3. Catabolic Mechanism of 17β-Estradiol inJournal of microbiology and biotechnology · 2026
    Article
  4. Article
  5. Article
  6. Article
  7. A genomic view of Earth's biomes.Nature reviews. Genetics · 2026
    Review
  8. Review
  9. Article
  10. Article
  11. Review
  12. Review
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  14. Review
  15. Polyphasic taxonomy and genome mining ofCurrent research in microbial sciences · 2025
    Article
  16. Review
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

5 authors.

Josep RamonedaDepartment of Ecology and Complexity, Center of Advanced Studies of Blanes (CEAB), Spanish Research Council (CSIC), Blanes, Spain.
Michael HoffertDepartment of Ecology and Evolutionary Biology, University of Colorado, Boulder, CO, United States.
Elias Stallard-OliveraDepartment of Ecology and Evolutionary Biology, University of Colorado, Boulder, CO, United States.
Emilio O CasamayorDepartment of Ecology and Complexity, Center of Advanced Studies of Blanes (CEAB), Spanish Research Council (CSIC), Blanes, Spain.
Noah FiererCooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado, Boulder, Colorado, United States.

Funding

European Regional Development Fund AEROSMIC PID2021-127701NB-I00Generalitat de Catalunya 2022-BP-00050Spanish Agency of ResearchSpanish Agency of Research PID2023-151209NA-I00Swiss National Science Foundation P2EZP3_199849US National Science Foundation OPP 2133684
6 · The paper itself

Abstract

Genomic information is now available for a broad diversity of bacteria, including uncultivated taxa. However, we have corresponding knowledge on environmental preferences (i.e. bacterial growth responses across gradients in oxygen, pH, temperature, salinity, and other environmental conditions) for a relatively narrow swath of bacterial diversity. These limits to our understanding of bacterial ecologies constrain our ability to predict how assemblages will shift in response to global change factors, design effective probiotics, or guide cultivation efforts. We need innovative approaches that take advantage of expanding genome databases to accurately infer the environmental preferences of bacteria and validate the accuracy of these inferences. By doing so, we can broaden our quantitative understanding of the environmental preferences of the majority of bacterial taxa that remain uncharacterized. With this perspective, we highlight why it is important to infer environmental preferences from genomic information and discuss the range of potential strategies for doing so. In particular, we highlight concrete examples of how both cultivation-independent and cultivation-dependent approaches can be integrated with genomic data to develop predictive models. We also emphasize the limitations and pitfalls of these approaches and the specific knowledge gaps that need to be addressed to successfully expand our understanding of the environmental preferences of bacteria.

Indexed as

BacteriaBacterial Physiological PhenomenaEnvironmentGenome, BacterialGenomicsbacterial cultivationenvironmental factorsenvironmental preferencesgenome-based modelsmachine learningmicrobial adaptationmicrobial ecologyuncultured bacteria

Identifiers

PMID39361898
PMCPMC11488383

What OpenQuestion holds

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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.