Xiang, Y.; Xiao, X.; Zhou, B.; Xie, L.

Motivation: Enhancer-derived RNAs (eRNAs) and their fusion with protein coding genes represent a crucial yet understudied layer of transcriptional regulation. eRNAs are typically expressed at low levels, which makes fusion events difficult to detect with conventional fusion detection tools. In addition, these tools are not designed to capture fusion transcripts arising from spatial proximity between distal regulatory elements and gene loci. Reads spanning such regions are also frequently filtered as mapping artifacts. As a result, computational approaches for systematically identifying spatially mediated enhancer-exon fusion transcripts remain lacking. Methods: We developed ChiMER, a graph-based framework for detecting ChiMeric Enhancer RNAs from short-read RNA-seq data. ChiMER constructs splice graphs with chromatin contact information to introduce enhancer-exon edges and uses graph alignment to search for potential transcriptional paths. A ranking-based scoring module then prioritizes high-confidence events. Evaluations on simulated and real RNA-seq datasets show that ChiMER achieves higher sensitivity than conventional linear fusion detection methods while maintaining low false-positive rates. Results: Applied to cancer cell line RNA-seq datasets, ChiMER identified multiple enhancer-exon chimeric transcripts, several associated with super-enhancer regions. Multi-omics analysis further show that fusion transcripts occur in transcriptionally active regulatory environments and frequently coincide with strong R-loop signals, suggesting a potential role of RNA-DNA hybrid structures in facilitating long-range transcriptional joining events.