MambaHM: High-Resolution Histone Modification Prediction from ATAC-seq and DNA Sequence

Histone modifications play a crucial role in transcriptional regulation and are essential targets for genome annotation and gene expression modeling. However, experimentally profiling histone modifications across species, tissues, and cellular states is costly and often impractical. While numerous deep learning models have been proposed to predict histone modifications, most operate at relatively low resolution (128 bp), limiting their utility in fine-scale genomic analysis. In this study, we present MambaHM (Mamba for predicting Histone Modifications), a novel deep learning framework built upon Mamba architecture. MambaHM integrates DNA sequence features with chromatin accessibility data (ATAC-seq) to predict ten histone modifications. Leveraging the linear computational complexity of Mamba's state-space modeling, our model avoids excessive compression of feature length during embedding. This enables the model to retain sufficient contextual information while simultaneously achieving 16-bp resolution, thereby enhancing the granularity and accuracy of prediction. Experiments demonstrate that MambaHM achieves a mean Pearson correlation of 0.836 (± 0.063) on the K562 cell line test set. Furthermore, the model generalizes well across cell types, tissues, and species, with a mean cross-context correlation of $0.557 (\pm 0.105)$, approaching the reliability of experimental assays. Compared to state-of-the-art models, MambaHM achieves a $\mathbf{7. 0 3 3 \%}(\mathbf{\pm 0. 0 4 1})$ improvement in cross-cell-type performance and over a 58.978 % (877 bp) improvement in prediction deviation evaluation for peak calling. Overall, MambaHM provides a powerful, cost-effective, and fineresolution tool for histone modification prediction, offering precise epigenomic references for downstream analysis and potential applications in drug discovery. The source code is available at https://github.com/zhichunlizzx/MambaHM.