Authors
Simon Bettinger, Auguste Verdier, Morgane Bruno, Stéphanie Manel, Alexis Joly, Maximilien Servajean, Alice Valentini, Rodolphe Devillers, David Mouillot
Published in
Molecular ecology resources. Volume 26. Issue 8.
Abstract
Environmental DNA (eDNA) has emerged as a powerful and cost-effective biodiversity monitoring tool, providing comprehensive and non-invasive taxonomic inventories. However, eDNA-based monitoring is strongly dependent on the completeness of genetic reference databases, which varies greatly among regions and taxa. Since completing gaps at the species level is unrealistic in the short term, an alternative is to infer coarser taxonomic assignments for sequences belonging to species not yet in reference databases. To address this challenge, we introduce BouillaBERT, a novel deep learning method tailored for the operational classification of eDNA sequences from unknown genera. BouillaBERT leverages data augmentation, transfer and transductive learning to overcome the small size and imbalance of eDNA reference databases for fishes. We implement and train the model across four commonly used fish eDNA markers and compare its performance with lowest common ancestor (LCA) alignment methods and existing deep learning approaches. BouillaBERT outperforms all tested LCA alignment and deep learning methods in recall for the assignment of unknown sequences across a range of commonly used fish eDNA primers, while achieving comparable or higher precision. BouillaBERT can be rapidly re-trained to fit a custom sample set or used as-is, enabling more consistent biodiversity assessments across undersampled regions or taxa.
PMID:
43015074
Bibliographic data and abstract were imported from PubMed on 12 Nov 2026.
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