ArticleBriefings in bioinformatics2026
circMAC: microRNA-conditioned binding-site localization on circular RNA isoforms.
Article in Briefings in bioinformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Circular RNAs (circRNAs) regulate gene expression in part through interactions with microRNAs (miRNAs), but identifying miRNA binding sites on full-length circRNA isoforms remains challenging. Binding context can differ across circRNA isoforms, and sequence continuity across the back-splice junction may be overlooked when circRNAs are represented as linear transcripts. Existing circRNA-miRNA resources and computational approaches mainly support association-level prediction or rule-based candidate-site screening. Although rule-based tools can be applied to full-length circRNA sequences, they do not directly learn miRNA-conditioned nucleotide-level binding-site localization while preserving circular sequence continuity. Here, we formulate circRNA-miRNA binding-site prediction as a miRNA-conditioned sequence-labeling task and present circMAC, a purpose-built framework for nucleotide-level localization on full-length circRNA isoforms. Given a full-length circRNA isoform and a mature miRNA sequence, circMAC predicts a binding probability for each circRNA nucleotide. circMAC combines established sequence-modeling components in a task-specific architecture, including attention-based global context modeling, Mamba-based sequential modeling, and convolutional local motif extraction. Paired miRNA information is incorporated through cross-attention, allowing each circRNA nucleotide to be evaluated in a miRNA-specific context. We evaluated circMAC against pretrained RNA language models, conventional sequence encoders, alternative pretraining strategies, architectural ablations, and stricter isoform-disjoint and back-splice-junction-disjoint splits. circMAC showed improved nucleotide-level localization performance under the evaluated benchmark settings, while qualitative and aggregate analyses indicated concentration of prediction signals around annotated binding-site regions. These results support task-specific full-length circular isoform modeling for prioritizing candidate circRNA-miRNA binding-site regions.
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