Evidence map›Paper›PMID 40626735›Full record

ArticlemSystems2025

Tetranucleotide frequencies differentiate genomic boundaries and metabolic strategies across environmental microbiomes.

Matthew Kellom, Maureen Berg, I-Min A Chen, Ken Chu, Alicia Clum, Marcel Huntemann, Natalia N Ivanova, Nikos C Kyrpides, Supratim Mukherjee, T B K Reddy and 6 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in mSystems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. 16S rRNA sequence captures microbial functional potential.bioRxiv : the preprint server for biology · 2026
    Article
  2. Article
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

16 authors.

Matthew KellomDOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, California, USA.ORCID 0000-0002-8310-7078
Maureen BergDOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, California, USA.
I-Min A ChenDOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, California, USA.ORCID 0000-0003-2026-9798
Ken ChuDOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, California, USA.
Alicia ClumEnvironmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA.ORCID 0000-0002-5004-3362
Marcel HuntemannDOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, California, USA.ORCID 0000-0002-1284-3748
Natalia N IvanovaDOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, California, USA.ORCID 0000-0002-5802-9485
Nikos C KyrpidesDOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, California, USA.ORCID 0000-0002-6131-0462
Supratim MukherjeeDOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, California, USA.ORCID 0000-0002-6322-2271
T B K ReddyDOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, California, USA.ORCID 0000-0002-0871-5567
Simon RouxDOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, California, USA.
Rekha SeshadriDOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, California, USA.ORCID 0000-0003-3219-2900
Gitta SzaboDOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, California, USA.
Neha J VargheseDOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, California, USA.ORCID 0000-0003-0580-0675
Tanja WoykeDOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, California, USA.ORCID 0000-0002-9485-5637
Emiley A Eloe-FadroshDOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, California, USA.ORCID 0000-0002-8162-1276

Funding

U.S. Department of Energy DE-AC02-05CH11231
6 · The paper itself

Abstract

Microbiomes are constrained by physicochemical conditions, nutrient regimes, and community interactions across diverse environments, yet genomic signatures of this adaptation remain unclear. Metagenome sequencing is a powerful technique to analyze genomic content in the context of natural environments, establishing concepts of microbial ecological trends. Here, we developed a data discovery tool-a tetranucleotide-informed metagenome stability diagram-that is publicly available in the integrated microbial genomes and microbiomes (IMG/M) platform for metagenome ecosystem analyses. We analyzed the tetranucleotide frequencies from quality-filtered and unassembled sequence data of over 12,000 metagenomes to assess ecosystem-specific microbial community composition and function. We found that tetranucleotide frequencies can differentiate communities across various natural environments and that specific functional and metabolic trends can be observed in this structuring. Our tool places metagenomes sampled from diverse environments into clusters and along gradients of tetranucleotide frequency similarity, suggesting microbiome community compositions specific to gradient conditions. Within the resulting metagenome clusters, we identify protein-coding gene identifiers that are most differentiated between ecosystem classifications. We plan for annual updates to the metagenome stability diagram in IMG/M with new data, allowing for refinement of the ecosystem classifications delineated here. This framework has the potential to inform future studies on microbiome engineering, bioremediation, and the prediction of microbial community responses to environmental change. IMPORTANCE: Microbes adapt to diverse environments influenced by factors like temperature, acidity, and nutrient availability. We developed a new tool to analyze and visualize the genetic makeup of over 12,000 microbial communities, revealing patterns linked to specific functions and metabolic processes. This tool groups similar microbial communities and identifies characteristic genes within environments. By continually updating this tool, we aim to advance our understanding of microbial ecology, enabling applications like microbial engineering, bioremediation, and predicting responses to environmental change.

Indexed as

MetagenomeMicrobiotaEcosystemMetagenomicsbioinformaticsenvironmental microbiologymetagenomicsmicrobial ecologymicrobiome

Identifiers

PMID40626735
PMCPMC12363243

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.