ArticleNucleic acids research2019
Discovering sequence and structure landscapes in RNA interaction motifs.
Article in Nucleic acids research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- The role of low-complexity repeats in RNA-RNA interactions and a deep learning framework for duplex prediction.Nature communications · 2026Article
- Tumor tissue-of-origin classification using miRNA-mRNA-lncRNA interaction networks and machine learning methods.Frontiers in bioinformatics · 2025Article
- Human lncRNAs harbor conserved modules embedded in different sequence contexts.Non-coding RNA research · 2024Article
- ALS-associated FUS mutation reshapes the RNA and protein composition of stress granules.Nucleic acids research · 2024Article
- Exploring the landscape of tools and resources for the analysis of long non-coding RNAs.Computational and structural biotechnology journal · 2023Review
- RNALigands: a database and web server for RNA-ligand interactions.RNA (New York, N.Y.) · 2022Article
- Inferring RNA-binding protein target preferences using adversarial domain adaptation.PLoS computational biology · 2022Article
- Artificial intelligence methods enhance the discovery of RNA interactions.Frontiers in molecular biosciences · 2022Review
- Prediction of RNA-protein interactions using a nucleotide language model.Bioinformatics advances · 2022Article
- BRIO: a web server for RNA sequence and structure motif scan.Nucleic acids research · 2021Article
- ELAVL1 primarily couples mRNA stability with the 3' UTRs of interferon-stimulated genes.Cell reports · 2021Article
- Motif Discovery from CLIP Experiments.Methods in molecular biology (Clifton, N.J.) · 2021Article
- Zooming in on protein-RNA interactions: a multi-level workflow to identify interaction partners.Biochemical Society transactions · 2020Review
- DeepCLIP: predicting the effect of mutations on protein-RNA binding with deep learning.Nucleic acids research · 2020Article
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6 authors.
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Abstract
RNA molecules are able to bind proteins, DNA and other small or long RNAs using information at primary, secondary or tertiary structure level. Recent techniques that use cross-linking and immunoprecipitation of RNAs can detect these interactions and, if followed by high-throughput sequencing, molecules can be analysed to find recurrent elements shared by interactors, such as sequence and/or structure motifs. Many tools are able to find sequence motifs from lists of target RNAs, while others focus on structure using different approaches to find specific interaction elements. In this work, we make a systematic analysis of RBP-RNA and RNA-RNA datasets to better characterize the interaction landscape with information about multi-motifs on the same RNAs. To achieve this goal, we updated our BEAM algorithm to combine both sequence and structure information to create pairs of patterns that model motifs of interaction. This algorithm was applied to several RNA binding proteins and ncRNAs interactors, confirming already known motifs and discovering new ones. This landscape analysis on interaction variability reflects the diversity of target recognition and underlines that often both primary and secondary structure are involved in molecular recognition.
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