Transposable elements (TEs) are fundamental drivers of genome evolution, yet their annotation and classification remain inconsistent, fragmented, and difficult to reproduce across species. This challenge arises from sequence divergence, lineage-specific innovations, and heterogeneous taxonomies across databases and computational tools, ultimately limiting large-scale comparative analyses. Here, we present PanTEon, a cross-kingdom deep learning framework designed to enable reproducible, scalable, and standardized TE classification. PanTEon integrates two key components: (i) the PanTEon Database, an automatically curated repository comprising ~240,000 structurally validated TE sequences from 2,790 species across animals, plants, and fungi, and (ii) a modular benchmarking and training platform that supports parallel evaluation and deployment of multiple ML and DL models. The PanTEon framework enables unified training, inference, and comparison across nine ML/DL architectures, while remaining fully extensible to user-defined models. Using this standardized environment, we benchmark seven state-of-the-art TE classifiers and demonstrate that classification performance is strongly influenced by taxonomic origin and TE superfamily, revealing significant biases and limitations in current approaches. Furthermore, we leverage the PanTEon training module to systematically retrain and compare nine architectures on a harmonized task involving 30 TE superfamilies. Our results show that ensemble strategies and taxon-specific models substantially improve predictive performance, while cross-kingdom generalization remains a key unresolved challenge. Additionally, we demonstrate the flexibility of the framework by addressing auxiliary tasks such as false positive detection in TE libraries. Overall, PanTEon establishes the first cross-kingdom, deep learning-driven ecosystem for TE classification, providing a robust foundation for benchmarking, model development, and large-scale genomic analyses.

Co-authors: Iamil Ferrer-Pomer, Fabiana Rodrigues de Goes, Simon Gaviria-Orrego, Juan Gómiz-Fernández, Jordi Llatser-Torres, Alexandre R. Paschoal, Romain Guyot, Toni Gabaldón