Evidence map›Paper›PMID 33691770›Full record

ArticleGenome biology2021

gapseq: informed prediction of bacterial metabolic pathways and reconstruction of accurate metabolic models.

Johannes Zimmermann, Christoph Kaleta, Silvio Waschina

Abstract read
In one paragraph

Article in Genome biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 183 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
183citing papers in PubMed, 1 pooled it
–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

183 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Article
  4. Bridging the gap:Gut microbes · 2026
    Review
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  12. Nutrient control enables metabolic reconstruction ofbioRxiv : the preprint server for biology · 2026
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123 more citing papers are in PubMed but not listed here.

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

3 authors.

Johannes ZimmermannChristian-Albrechts-University Kiel, Institute of Experimental Medicine, Research Group Medical Systems Biology, Michaelis-Str. 5, Kiel, 24105, Germany.
Christoph KaletaChristian-Albrechts-University Kiel, Institute of Experimental Medicine, Research Group Medical Systems Biology, Michaelis-Str. 5, Kiel, 24105, Germany.
Silvio WaschinaChristian-Albrechts-University Kiel, Institute of Experimental Medicine, Research Group Medical Systems Biology, Michaelis-Str. 5, Kiel, 24105, Germany. s.waschina@nutrinf.uni-kiel.de.ORCID 0000-0002-6290-3593

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Genome-scale metabolic models of microorganisms are powerful frameworks to predict phenotypes from an organism's genotype. While manual reconstructions are laborious, automated reconstructions often fail to recapitulate known metabolic processes. Here we present gapseq ( https://github.com/jotech/gapseq ), a new tool to predict metabolic pathways and automatically reconstruct microbial metabolic models using a curated reaction database and a novel gap-filling algorithm. On the basis of scientific literature and experimental data for 14,931 bacterial phenotypes, we demonstrate that gapseq outperforms state-of-the-art tools in predicting enzyme activity, carbon source utilisation, fermentation products, and metabolic interactions within microbial communities.

Indexed as

Energy MetabolismMetabolic Networks and PathwaysSoftwareBacteriaComputational BiologyDatabases, FactualFermentationGastrointestinal MicrobiomeGenome, BacterialGenomicsHumansMetagenomeMetagenomicsSoil MicrobiologyBenchmarkCommunity simulationGenome-scale metabolic modelsMetabolic networksMetabolic pathway analysisMetagenomeMicrobiome

Identifiers

PMID33691770
PMCPMC7949252

What OpenQuestion holds

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LicenceCC BY
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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.