ArticleISME communications2025
SpeSpeNet: an interactive and user-friendly tool to create and explore microbial correlation networks.
Article in ISME communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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.
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Who cites it
3 citing papers in PubMed.
- Longitudinal Change in Rumen-Associated Bacterial Communities Following Aso Limonite Supplementation in Japanese Brown Cattle.Animals : an open access journal from MDPI · 2026Article
- Microbiota and horizontal microbial transmission dynamics associated with bramble (Rubus spp.) flowers.Environmental microbiome · 2026Article
- Pesticide Residues Reduce Bacterial Diversity but Enhance Stability via Network Motif Restructuring.Toxics · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Correlation networks are commonly used to explore microbiome data. In these networks, nodes are microbial taxa and edges represent correlations between their abundances. As clusters of correlating taxa (co-abundance clusters) often indicate a shared response to environmental drivers, network visualization contributes to the system understanding. Currently, most tools for creating and visualizing co-abundance networks from microbiome data either require the researcher to have coding skills or are not user-friendly, with high time expenditure and limited customizability. Furthermore, existing tools lack a focus on the association between environmental drivers and the structure of the microbiome, even though many edges in correlation networks can be understood through a shared association of two taxa with the environment. For these reasons, we developed SpeSpeNet (Species-Species Network, https://tbb.bio.uu.nl/SpeSpeNet), a practical and user-friendly R-shiny tool to construct and visualize correlation networks from taxonomic abundance tables. The details of data preprocessing, network construction, and visualization are automated, require no programming ability for the web version, and are highly customizable, including associations with user-provided environmental data. Here, we present the details of SpeSpeNet and demonstrate its utility using three case studies.
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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.