PanTEon: a cross-kingdom framework to guide the design of transposable element classifiers

Abstract

Transposable elements (TEs) are major drivers of genome evolution, yet their annotation and classification remain inconsistent and hard to reproduce across species. Fragmented repeats, lineage-specific mutations, and the use of heterogeneous taxonomies across databases and tools make comparative analyses difficult and hamper progress in TE biology. To address this, we developed PanTEon, a cross-kingdom deep learning framework for reproducible TE classification that combines a harmonized database with an open, modular benchmarking platform. The PanTEon Database is an automatically curated, taxonomically broad TE repository spanning animals, plants, and fungi. The PanTEon platform standardizes training, evaluation, and inference across nine machine learning methods, while being extensible to user-defined architectures. We use this framework to benchmark state-of-the-art machine learning-based TE classifiers and find that performance varies markedly across TE superfamilies and major eukaryotic lineages. Ensemble approaches and phylum-specific models improve predictive F1 scores, but cross-species generalization remains challenging. Together, the PanTEon Database and platform provide a reproducible, scalable, and extensible foundation for TE classification, enabling standardized evaluation of future AI methods and supporting community-driven annotation efforts.