ArticleScientific reports2020
Microbe-set enrichment analysis facilitates functional interpretation of microbiome profiling data.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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.
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.
Who cites it
13 citing papers in PubMed.
- A longitudinal profiling of microbiome of diabetic foot ulcers shows functional role of microbial communities in wound worsening and chronicity.Current research in microbial sciences · 2026Article
- GeneHarmony: A Knowledge-Based Tool for Biomarker Discovery in Disease: Sjögren's Disease vs. Rheumatoid Arthritis and Systemic Lupus Erythematosus.International journal of molecular sciences · 2025Article
- TaxSEA: rapid interpretation of microbiome alterations using taxon set enrichment analysis and public databases.Briefings in bioinformatics · 2025Article
- ATOMIC: a graph attention network for atopic dermatitis prediction using human gut microbiome.Frontiers in immunology · 2025Article
- BugSigDB captures patterns of differential abundance across a broad range of host-associated microbial signatures.Nature biotechnology · 2024Article
- Article
- Addressing erroneous scale assumptions in microbe and gene set enrichment analysis.PLoS computational biology · 2023Article
- Total RNA sequencing reveals gene expression and microbial alterations shared by oral pre-malignant lesions and cancer.Human genomics · 2023Article
- Characteristics of gut microbiota in patients with metabolic associated fatty liver disease.Scientific reports · 2023Article
- Candida expansion in the gut of lung cancer patients associates with an ecological signature that supports growth under dysbiotic conditions.Nature communications · 2023Article
- Total RNA sequencing reveals gene expression and microbial alterations shared by oral pre-malignant lesions and cancer.bioRxiv : the preprint server for biology · 2023Article
- CDEMI: Characterizing differences in microbial composition and function in microbiome data.Computational and structural biotechnology journal · 2023Article
- Intratumor Microbiome Analysis Identifies Positive Association BetweenFrontiers in immunology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The commensal microbiome is known to influence a variety of host phenotypes. Microbiome profiling followed by differential abundance analysis has been established as an effective approach to study the mechanisms of host-microbiome interactions. However, it is challenging to interpret the collective functions of the resultant microbe-sets due to the lack of well-organized functional characterization of commensal microbiome. We developed microbe-set enrichment analysis (MSEA) to enable the functional interpretation of microbe-sets by examining the statistical significance of their overlaps with annotated groups of microbes that share common attributes such as biological function or phylogenetic similarity. We then constructed microbe-set libraries by query PubMed to find microbe-mammalian gene associations and disease associations by parsing the Disbiome database. To demonstrate the utility of our novel MSEA methodology, we carried out three case studies using publicly available curated knowledge resource and microbiome profiling datasets focusing on human diseases. We found MSEA not only yields consistent findings with the original studies, but also recovers insights about disease mechanisms that are supported by the literature. Overall, MSEA is a useful knowledge-based computational approach to interpret the functions of microbes, which can be integrated with microbiome profiling pipelines to help reveal the underlying mechanism of host-microbiome interactions.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.