← BioTransfer GEO Dataset Finder
GEO series

The role of low-complexity repeats in RNA–RNA interactions and a deep learning framework for duplex prediction

GSE290073 Mus musculus Expression profiling by high throughput sequencing 8 samples Submitted 2025/12/17 Platform GPL24247
Summary
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.
Published in
The role of low-complexity repeats in RNA-RNA interactions and a deep learning framework for duplex prediction
Setti A, Bini G, Pellegrini F et al. · Nature communications 2026 · PMID 41571635 · doi:10.1038/s41467-026-68356-w
This dataset
Download

Direct links to NCBI, no account and no request form: the whole study as GSE290073_RAW.tar, processed values as the series matrix, the supplementary file directory, and per-sample supplementary files for any of the 8 samples. Raw sequencing reads are also available from ENA.

Also filed as BioProject PRJNA1226136 and SRA study SRP565249. Searching any of these in the dataset finder brings you back here.

Samples in this study

The sample list for this study is not cached yet. Press Sort into groups and it will be fetched from NCBI.

+ 8 more — browse all 8 samples with per-sample file links →

Similar datasets

Search all mouse RNA-seq datasets in GEO →

Share this dataset

Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.