Evidence map›Paper›PMID 42163722›Full record

ArticleNucleic acids research2026

Microbe Decoder uncovers functional traits of microbes in microbiome datasets.

Timothy J Hackmann, John P Parris, Rekha Seshadri, Christopher Lingga

Abstract read
In one paragraph

Article in Nucleic acids research, 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

4 authors.

Timothy J HackmannDepartment of Animal Science, University of California, Davis, CA 95618, United States.ORCID 0000-0001-6455-1862
John P ParrisDepartment of Animal Science, University of California, Davis, CA 95618, United States.
Rekha SeshadriDOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, United States.ORCID 0000-0003-3219-2900
Christopher LinggaDepartment of Animal Science, University of California, Davis, CA 95618, United States.

Funding

National Institute of Food and Agriculture 1019985National Institute of Food and Agriculture 2018-67015-27495University of California, Davis Small Grant in Aid of Research
6 · The paper itself

Abstract

Microbe Decoder is a web server that predicts functional traits of microbes in microbiome sequencing datasets. Sequencing has revealed thousands of organisms in most ecosystems, but the functional traits of many organisms remain unclear. Existing tools can predict names of organisms or their genes, but they rarely predict concrete biological functions (e.g. fermentation or anaerobic growth). Microbe Decoder fills this gap using three complementary tools relying on taxonomy, metabolic networks, or machine learning. These tools accept either names or gene functions as inputs and are integrated into an easy-to-use web app. When tested against data for microbial isolates, Microbe Decoder showed good predictive performance (e.g. balanced accuracy of 0.85). When applied to datasets from the gut, sediment, and sea, it predicted shifts in functional traits over space and time. Microbe Decoder is designed for use with prokaryotes, with the goal of including eukaryotes in the future. By revealing functional traits of microbes in biological systems, Microbe Decoder will advance biology, medicine, and environmental science. Microbe Decoder is available at https://www.microbe-decoder.org/.

Indexed as

MicrobiotaSoftwareBacteriaInternetMachine LearningMetabolic Networks and Pathways

Identifiers

PMID42163722
PMCPMC13355083

What OpenQuestion holds

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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.