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Decoding RNA-RNA Interactions: The Role of Low-Complexity Repeats and a Deep Learning Framework for Sequence-Based Prediction

Guardado en:
Publicado en:bioRxiv (Feb 16, 2025)
Autor principal: Setti, Adriano
Otros Autores: Bini, Giorgio, Maiorca, Valentino, Pellegrini, Flamina, Proietti, Gabriele, Miltiadis-Vrachnos, Dimitrios, Armaos, Alexandros, Martone, Julie, Monti, Michele, Ruocco, Giancarlo, Rodola, Emanuele, Bozzoni, Irene, Colantoni, Alessio, Tartaglia, Gian Gaetano
Publicado:
Cold Spring Harbor Laboratory Press
Materias:
RNA processing
Non-coding RNA
Nucleotide sequence
Gene regulation
Deep learning
Acceso en línea:Citation/Abstract
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Resumen:RNA-RNA interactions (RRIs) are fundamental to gene regulation and RNA processing, yet their molecular determinants remain unclear. In this work, we analyzed several large-scale RRI datasets and identified low-complexity repeats (LCRs), including simple tandem repeats, as key drivers of RRIs. Our findings reveal that LCRs enable thermodynamically stable interactions with multiple partners, positioning them as key hubs in RNA-RNA interaction networks. RNA-sequencing of the interactors of the Lhx1os lncRNA allowed to validate the importance of LCRs in shaping interactions potentially involved in neuronal development.Recognizing the pivotal role of sequence determinants, we developed RIME, a deep learning model that predicts RRIs by leveraging embeddings from a nucleic acid language model. RIME outperforms traditional thermodynamics-based tools, successfully captures the role of LCRs and prioritizes high-confidence interactions, including those established by lncRNAs. RIME is freely available at https://tools.tartaglialab.com/rna_rna.Competing Interest StatementThe authors have declared no competing interest.
ISSN:2692-8205
DOI:10.1101/2025.02.16.638500
Fuente:Biological Science Database