Villon Sébastien auteur aut IRD Mangeas Morgan auteur aut IRD Berteaux-Lecellier V. auteur aut IRD Vigliola Laurent auteur aut IRD Lecellier Gael auteur aut IRD text journalArticle eng text/pdf reformatted digital access Environmental DNA (eDNA) metabarcoding is an innovative tool that is transforming ecological research. It offers a simple and effective method for simultaneously detecting numerous species across a wide range of environments. The method relies on assigning DNA sequences sampled from the environment to taxa, which is straightforward for species that have already been sequenced and are represented in reference databases. However, existing bioinformatics tools often fail to deliver accurate, fine-grained assignments when target species are absent from these databases. This limitation arises from handcrafted classification thresholds that do not account for nucleotide positional information. Here, we propose a deep neural architecture specifically designed to exploit both nucleotide identity and positional patterns in short TELEO sequences. Using an in-silico validation framework based on NCBI genbank sequences, we compare our approach with several state-of-the-art bioinformatics tools (Obitools, Kraken2, Lolo), as well as alternative sequence embedding methods, under controlled conditions. Our approach yields significantly higher classification accuracy at the genus and family levels, achieving average accuracies of 94.7% at the genus level and 86.5% at the family level, substantially outperforming the tested reference-based pipelines. The method remains robust with limited training data and shows improved performance when nucleotide positional information is preserved through sequence alignment. These results demonstrate the potential of AI-powered eDNA metabarcoding to complement existing taxonomic assignment tools, particularly in contexts where reference databases are incomplete or species-level resolution is not achievable, thereby supporting biodiversity monitoring and ecosystem management. specialized biodiversity monitoring environmental DNA bioinformatics deep learning algorithms convolutional neural networks CNN fish diversity ecological survey 020 036 15 3 285 [16 p.] 2026 https://www.documentation.ird.fr/hor/fdi:010096350 10.3390/biology15030285 [F B010096350] https://www.documentation.ird.fr/hor/fdi:010096350 https://horizon.documentation.ird.fr/exl-doc/pleins_textes/2026-03/010096350.pdf IRD - Base Horizon / Pleins textes 2026-03-11 2026-03-30 fdi:010096350 fre