NeighborFinder: an R package inferring local microbial network around a species of interest

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ID: 329985
2026
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Abstract
Abstract Motivation Understanding interactions from microbiome data is a central aspect in microbial ecology, as it provides insights into ecosystem stability, disease mechanisms, and can be used to design synthetic communities. Current network inference tools reconstruct global networks from co-abundance data, which means they capture the overall correlation structure for the entire set of taxa considered. These approaches are computationally intensive and suboptimal when the focus is on the local neighborhood of one or a few taxa of interest. Results We introduce NeighborFinder, a local network inference method that enables the targeted discovery of direct neighbors around a species of interest. Using cross-validated multiple linear regression with ℓ1 penalty and microbiome-specific filters, our approach infers interpretable species-centered interactions, with F1 score ≥ 0.95 on simulated cohorts ranging from 250 to 1000 samples. This method is well-suited for large metagenomic datasets and is particularly valuable for exploratory studies where the targeted hypotheses outweigh the need for global community structure. The approach complements existing methods by being biologically intuitive and computationally efficient. Availability and Implementation The R package is available on CRAN https://CRAN.R-project.org/package=NeighborFinder. The data and source code used to calculate performances and produce the use case example can be found respectively at: https://doi.org/10.57745/UPITJ0 and https://doi.org/10.57745/HJLWW4. Supplementary information Supplementary data are available at Bioinformatics Advances online.
Reference Key
openalex_W7214607282 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Mathilde Sola, Adrien Paravel, Sandrine Auger, Jean-Marc Chatel, Florian Plaza Oñate, Emmanuelle Le Chatelier, Marion Leclerc, Patrick Veiga, Clémence Frioux, Mahendra Mariadassou, Magali Berland
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Journal Bioinformatics advances
Year 2026
DOI
10.1093/bioadv/vbag201
URL
https://doi.org/10.1093/bioadv/vbag201
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