RNA–RNA Interactomics and Higher-Order RNA Architecture

RNA molecules do not act in isolation. Across the transcriptome, both coding and non-coding RNAs form dynamic intra- and intermolecular contacts that shape RNA processing, localization, stability, translation, and regulatory function. Messenger RNAs interact not only with each other but also with a broad repertoire of non-coding RNAs, including long non-coding RNAs, small RNAs, circular RNAs, and other regulatory RNA species, creating complex interaction networks that influence gene expression and cellular state. Recent advances in proximity ligation, crosslinking-based assays, and structure-aware sequencing have made it possible to profile RNA–RNA interactions at unprecedented scale, opening the door to a systems-level view of how coding and non-coding RNAs jointly organize the transcriptome. At the same time, major computational challenges remain in distinguishing true biological interactions from experimental noise, reconstructing duplexes and contact maps from sparse or biased data, and integrating interaction evidence across assays, conditions, and cell types.This Research Topic will highlight methodological and applied studies focused on transcriptome-scale RNA–RNA interaction mapping and the computational analysis of higher-order RNA architecture, with particular emphasis on the interplay between coding and non-coding RNAs. We welcome contributions on experimental–computational pipelines for identifying RNA duplexes involving mRNAs and non-coding RNAs, graph- and thermodynamics-based approaches for reconstructing RNA contact networks, probabilistic and machine learning methods for denoising and scoring interactions, and frameworks that connect local duplex formation with global RNA structural organization. Studies that combine RNA–RNA interactomics with complementary modalities, such as RNA structure probing, RNA-binding protein occupancy, long-read sequencing, spatial transcriptomics, or perturbation datasets—are especially encouraged, particularly when they help illuminate the regulatory roles of non-coding RNAs in transcriptome organization.A central goal of this topic is to advance rigorous benchmarking of prediction methods. We seek work that compares algorithms for interaction discovery, duplex reconstruction, and contact map inference using well-defined reference datasets, simulated data, orthogonal validation, or community standards. Submissions addressing reproducibility, calibration, uncertainty estimation, bias correction, and the interpretability of predictive models are particularly relevant. By bringing together new datasets, computational methods, and benchmarking practices, this collection aims to clarify how RNA–RNA interactions among coding and non-coding transcripts contribute to higher-order RNA architecture, gene regulation, and disease, and to establish robust foundations for the next generation of RNA bioinformatics tools.Relevant themes include:- Transcriptome-wide experimental mapping of RNA–RNA interactions involving coding and non-coding RNAs- Computational reconstruction of RNA duplexes and interaction networks across diverse RNA biotypes- Contact map inference for individual transcripts and transcriptome-scale datasets- Benchmark datasets, validation strategies, and performance metrics for prediction methods- Bias correction and statistical modeling for ligation- and crosslinking-based assays- Integration of RNA interactomics with structure probing and protein-binding data- Machine learning and generative models for RNA interaction prediction- Comparative analysis of RNA architecture across conditions, cell types, or species- Functional interpretation of coding–non-coding RNA interactions in regulation and disease- Roles of lncRNAs, small RNAs, circular RNAs, and other non-coding RNA classes in higher-order RNA organization. Topic editors: Sharmishtha Shyamal, Bo-Ya Ji Source: https://www.frontiersin.org/research-topics/79771/rna-rna-interactomics-and-higher-order-rna-architecture