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bioRxiv · 10.64898/2026.04.09.717429

DIANA: Deep Learning Identification and Assessment of Ancient DNA

Duitama Gonzalez, C.·Lopopolo, M.·Nishimura, L.·Faure, R.·Duchene, S.
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Abstract

The field of ancient metagenomics provides insights into past microbiomes, but with a growing dataset size, methods that rely on reference databases have limited scope. Here, we introduce DIANA, a multi-task neural network that predicts key metadata categories from unitig abundances. Trained on 2,597 run accessions (1.72 Tbp of assembled unitig sequences), DIANA accurately identifies sample host (94.6%), community type (90.0%), and material (88.9%) on held-out test data and demonstrates robust generalisation on an independent validation set. A key innovation is DIANAs ability to perform semantic generalisation, correctly classifying samples with labels unseen during training -- such as novel subspecies -- to their appropriate parent categories. By leveraging both known and uncharacterized genomic sequences, DIANA provides a rapid, data-driven system for metadata validation and quality control, accelerating discovery in ancient metagenomics research.

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Duitama Gonzalez, C., Lopopolo, M., Nishimura, L., Faure, R., Duchene, S.. 2026-04-10. DIANA: Deep Learning Identification and Assessment of Ancient DNA. https://doi.org/10.64898/2026.04.09.717429

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