Evidence map›Paper›PMID 41140851›Full record

ArticleData in brief2025

NEMESISdb: A full length 16S rRNA gene dataset for the detection of human, fish, and crustacean potentially pathogenic bacteria.

Son-Hoang Tran, Claudia Ximena Restrepo-Ortiz, Dinh Quang Vu, Marc Troussellier, Yvan Bettarel, Thierry Bouvier, Van Ngoc Bui, Nguyen Hieu Minh, Trung Du Hoang, Quang Huy Nguyen and 1 more

Abstract read
In one paragraph

Article in Data in brief, 2025. 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

11 authors.

Son-Hoang TranUMR MARBEC, Univ Montpellier, CNRS, Ifremer, IRD, Montpellier, France.
Claudia Ximena Restrepo-OrtizUMR MARBEC, Univ Montpellier, CNRS, Ifremer, IRD, Sète, France.
Dinh Quang VuUniversity of Science and Technology of Hanoi, Vietnam Academy of Science and Technology (VAST), 18 Hoang Quoc Viet, Hanoi, Vietnam.
Marc TroussellierUMR MARBEC, Univ Montpellier, CNRS, Ifremer, IRD, Montpellier, France.
Yvan BettarelUMR MARBEC, Univ Montpellier, CNRS, Ifremer, IRD, Montpellier, France.
Thierry BouvierUMR MARBEC, Univ Montpellier, CNRS, Ifremer, IRD, Montpellier, France.
Van Ngoc BuiInstitute of Biology (IB), Vietnam Academy of Science and Technology (VAST), 18 Hoang Quoc Viet, Hanoi, Vietnam.
Nguyen Hieu MinhInstitute of Oceanography, Vietnam Academy of Science and Technology (VAST), 01, Cau Da, Nha Trang, Khanh Hoa, Vietnam.
Trung Du HoangInstitute of Oceanography, Vietnam Academy of Science and Technology (VAST), 01, Cau Da, Nha Trang, Khanh Hoa, Vietnam.
Quang Huy NguyenUniversity of Science and Technology of Hanoi, Vietnam Academy of Science and Technology (VAST), 18 Hoang Quoc Viet, Hanoi, Vietnam.
Jean-Christophe AuguetUMR MARBEC, Univ Montpellier, CNRS, Ifremer, IRD, Montpellier, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

NEMESISdb is a 16S rRNA full length sequence curated dataset designed to enable the identification and tracking of potentially pathogenic bacteria (PPB) for human, fish, and crustacean hosts. It addresses the limited focus on marine and coastal environments as key reservoirs for PPB, where bacteria from diverse sources-terrestrial, marine, and animal-can coexist. Leveraging recent advances in high-throughput sequencing, NEMESISdb provides a robust resource for the detection of PPB in 16S rRNA gene metabarcoding or metagenomic data. The database comprises three datasets corresponding to human, fish, and crustacean hosts, containing 1703, 222, and 64 PPB species, respectively, with a total of over 150,000 16S rRNA full length sequences curated for accuracy. This resource was constructed by extracting sequences from the SILVA 138.2 SSU Ref NR99 database, refining them through a rigorous curation pipeline to ensure taxonomic consistency and eliminate misclassifications. The resulting datasets are optimized for use with popular tools such as BLAST and classifier software, enabling rapid and accurate detection of PPB in metabarcoding and metagenomic data. NEMESISdb supports diverse applications, including pathogen surveillance in aquatic ecosystems, studies on environmental factors influencing PPB dynamics, and the development of targeted strategies for mitigating pathogen impacts in aquaculture. Additionally, it facilitates research within the One Health framework by linking the circulation of PPB across environmental, animal, and human compartments.

Indexed as

DatasetMarine ecosystems, Human, AnimalOne HealthPathogenic bacteria

Identifiers

PMID41140851
PMCPMC12553003

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LicenceCC BY
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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.