Yu, H.; Li, W.; Li, W.; Liu, Y.; Chen, Y.; Zhang, X.; He, S.; Chen, Z.; Wang, H.; Ni, J.; Gao, T.; Li, F.; Lu, L.

Enhancer-derived RNAs (eRNAs) are critical regulators of gene transcription, yet their genome-wide annotation remains challenging. Here, we present eRNAformer, a multi-modal deep learning framework that integrates convolutional neural networks with transformers, specifically designed to capture long-range genetic features associated with bidirectional transcription. This approach enables de novo mapping of eRNA loci using DNA sequence and aggregated conventional RNA-seq data. When evaluated on ENCODE datasets, eRNAformer demonstrated high sensitivity and specificity in discriminating known eRNA loci from non-eRNA loci. Notably, the newly identified eRNA loci were enriched with evolutionarily constrained variants and genetic risk factors for complex diseases, and exhibit potential relevance for cancer therapy. Applied to GEO datasets, eRNAformer identified a range from 14,219 to 56,451 eRNA loci across multiple hematologic malignancies, facilitating the construction of a comprehensive eRNA database for blood cancers. We further identified and experimentally validated FOXO1e, a cluster of eRNAs located approximately 120 kb upstream of FOXO1, a known oncogene that drives t(8;21) acute myeloid leukemia (AML) preleukemic program. Together, these findings establish eRNAformer as a powerful tool for genome-wide eRNA annotation, provide a valuable resource for eRNA studies in hematologic cancers, and underscore the functional importance of eRNAs in AML pathogenesis.