TEclass2: Classification of transposable elements using Transformers

MotivationTransposable elements (TEs) are interspersed repetitive sequences that are major constituents of most eukaryotic genomes and are crucial for genome evolution. Despite the existence of multiple tools for their classification and annotation, none of them can achieve completely reliable results making it a challenge for genomic studies. In this work, we introduce TEclass2, a new software that uses a deep learning approach based upon a linear Transformer architecture with a k-mer to-kenizer and further adaptations to handle DNA sequences. This software has an easy configuration that allows training models on new datasets and the classification of TE models providing multiple metrics for a reliable evaluation of the results. ResultsThis work shows a successful adaptation of deep learning with Transformers for the classification of TE models from consensus sequences, and these results lay a foundation for novel methodologies in bioinformatics. We provide a tool for the training of models and the classification of consensus sequences from TE models on custom data and a web page interface with a pre-trained dataset based on curated and non-curated TE libraries allowing a fast and simple classification of TEs. Availabilityhttps://bioinformatics.uni-muenster.de/tools/teclass2/index.pl Contactwojmak@uni-muenster.de Supplementary informationSupplementary data are available at Bioinformatics online.