Evidence map›Paper›PMID 42092044›Full record

ArticleScientific reports2026

CCMRI: a classification and curated database of climate change-related microbiome studies.

Alexios Loukas, Konstantinos Kalaentzis, Nefeli Kleopatra Venetsianou, Christina Damianou, Savvas Paragkamian, Vincenzo Lagani, Lars Juhl Jensen, Evangelos Pafilis

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Alexios LoukasInstitute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, P.O.Box 2214, 71003, Heraklion, Crete, Greece.
Konstantinos KalaentzisInstitute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, P.O.Box 2214, 71003, Heraklion, Crete, Greece.
Nefeli Kleopatra VenetsianouInstitute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, P.O.Box 2214, 71003, Heraklion, Crete, Greece.
Christina DamianouInstitute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, P.O.Box 2214, 71003, Heraklion, Crete, Greece.
Savvas ParagkamianInstitute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, P.O.Box 2214, 71003, Heraklion, Crete, Greece.
Vincenzo LaganiBiomedical Sciences Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.
Lars Juhl JensenNovo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark.
Evangelos PafilisInstitute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, P.O.Box 2214, 71003, Heraklion, Crete, Greece. pafilis@hcmr.gr.

Funding

Hellenic Foundation for Research and Innovation 2772
6 · The paper itself

Abstract

Climate Change (CC) is reshaping all ecosystem processes and structures. Microbial data provide valuable insights into how microbial processes contribute to CC and how CC, in turn, alters microbial communities. However, the growing volume of environmental genomics data makes identifying CC-related records challenging. The Climate Change Metagenomic Record Index (CCMRI) has been developed to harvest metagenomic/microbiome records pertaining to CC and to provide researchers with a curated database of CC-related microbiome studies ( https://ccmri.hcmr.gr ). To guide interpretation, the database's 169 metagenomic studies have been labelled according to their relation to CC as CC-caused, CC-causing, and CC-mitigating. They have also been annotated with the CC phenomena they explore, like methane production, temperature rise, permafrost thawing, greenhouse gas emission, methanotrophy, and ocean acidification. To ease navigation, they have also been classified according to their biome as aquatic, terrestrial, host-associated, and engineered. The CCMRI database was initially constructed through manual curation of all aquatic and terrestrial studies in the MGnify resource. It was then expanded with the help of the CCMRI curation-assistant system. This leveraged Large Language Models to scan the remaining MGnify studies, filtered them for relevance, and proposed candidates for inclusion. With a recall greater than 90%, the system achieved high accuracy in identifying CC-related studies. The final decisions on CC-relatedness and categorization were performed by a human curator. This approach combines the efficiency of automation with human oversight and greatly reduces the curation effort, ensuring sustainability and scalability.

Indexed as

Climate ChangeDatabases, FactualMicrobiotaBiocurationData CurationMetagenomeMetagenomicsClimate changeLarge language modelsManually curated corpusMetagenomicsMicrobiome study classificationSemi-automated curation

Identifiers

PMID42092044
PMCPMC13338393

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Registered trials

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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.