KAS-former: a transformer-based model for predicting histone modifications using KAS-seq

Histone modifications (HMs) play a critical role in various biological processes, but annotating histone modifications across different cell types using experimental methods alone is extremely challenging. Although many deep learning methods have been developed to predict histone modifications, most rely solely on DNA sequences and do not incorporate novel cell-specific features. In this study, we propose KAS-former, a transformer-based model that integrates DNA sequences with cell-specific features derived from KAS-seq data, enabling effective prediction of histone modifications. Leveraging this transformer architecture coupled with dilated convolution, KAS-former achieves a broad receptive field, effectively capturing cell type-specific specificity from KAS-seq data. Our results demonstrate that KAS-former achieves high accuracy in predicting histone modifications across multiple cell types and shows strong potential for transcription factor prediction. By capturing cell-specific features, this approach not only improves the accuracy of histone modification predictions but also offers valuable insights into the interplay between histone modifications and transcription regulation. The code for KAS-former is available on GitHub at https://github.com/wzhy2000/KAS-former.