Transformer-Based Classification of Transposable Element Consensus Sequences with TEclass2

Transposable elements (TEs) constitute a significant portion of eukaryotic genomes and play crucial roles in genome evolution, yet their diverse and complex sequences pose challenges for accurate classification. Existing tools often lack reliability in TE classification, limiting genomic analyses. Here, we present TEclass2, a software employing a deep learning approach based on a linear transformer architecture with k-mer tokenization and sequence-specific adaptations to classify TE consensus sequences into sixteen superfamilies. TEclass2 demonstrates improved classification performance and offers flexible model training on custom datasets. Accessible via a web interface with pre-trained models, TEclass2 facilitates rapid and reliable TE classification. These advancements provide a foundation for enhanced genomic annotation and support further bioinformatics research involving transposable elements.