It is estimated that millions of species exist in the deep-sea environment, such as hydrothermal vents, cold seeps, abyssal plains and seamounts, which have yet to be described. Non-invasive biodiversity assessment methods can be applied using deep-sea environmental DNA (eDNA) metabarcoding, but it can sometimes be limited by the fact that deep-sea taxa are not well represented in curated reference databases. This study proposes a proof-of-concept artificial intelligence (AI)-driven framework for deep-sea eDNA analysis consisting of three different and complementary parts. To learn taxonomically informative sequence representations that cannot be obtained from conventional reference matching, a hybrid CNN Transformer model is presented and trained. The suggested methodology confirmed the practicality of confidence-guided novel taxon discovery with a macro F1 score of 0.847 using simulated data sets drawn from CMLRE expedition metadata. These findings provide the framework for future validation with actual deep-sea sequencing datasets. Second, a module for discovering potential, confidence-based novel taxa has been implemented, which sends low-confidence predictions to an unsupervised workflow that includes UMAP and HDBSCAN. In the simulated evaluation scenario, this module retrieved 91.5% of novel taxa as new taxa, and with 88.2% precision. Third, taxonomic abundance estimations that are suitable for downstream alpha and beta diversity analysis are provided by normalization and ecological profiling modules. The proposed framework consumed one million sequencing reads in 2.1 h across four NVIDIA A100 GPUs, significantly shorter than the processing times used in the different workflows evaluated in this study. Collectively, these findings indicate that deep learning-assisted eDNA analysis could help in biodiversity assessments where there is a lack of extensive reference databases. Due to all quantitative assessments conducted on simulated datasets, the results obtained should be regarded as proof-of-concept results and further testing with real data from deep-sea expeditions needs to be carried out to exercise the operational performance.
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