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Related Concept Videos

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Related Experiment Video

Updated: Feb 14, 2026

Enhanced Crosslinking Immunoprecipitation eCLIP Method for Efficient Identification of Protein-bound RNA in Mouse Testis
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Sequence-Based Models for RNA-Protein Interactions Imputation Might Be Insufficient for Novel Signal Prediction in

Arsenii K Rybakov1, Daniil A Khlebnikov1,2, Daria Y Ovchinnikova1

  • 1Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, 1-73 Leninskie Gory, Moscow 119991, Russia.

International Journal of Molecular Sciences
|February 13, 2026
PubMed
Summary
This summary is machine-generated.

Predicting RNA-protein interactions is difficult due to biases in experimental data. The PLERIO framework uses eCLIP data to map interactions across the transcriptome, improving predictions for both highly and lowly expressed RNAs.

Keywords:
RNAcompeteRNA–protein interactionseCLIPmachine learningsequence-based models

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Molecular Biology

Background:

  • Predicting RNA-protein interactions is complex, with existing methods lacking a unified approach.
  • In vivo immunoprecipitation (IP) experiments exhibit biases, overrepresenting highly expressed RNAs.
  • A comprehensive understanding of RNA-protein interactions across the entire transcriptome is needed.

Purpose of the Study:

  • To develop a machine learning framework (PLERIO) for predicting RNA-protein interactions.
  • To reconstruct the full spectrum of protein interactions with both highly and lowly expressed RNAs using eCLIP data.
  • To evaluate the framework's applicability to multi-protein prediction and different experimental contexts.

Main Methods:

  • Utilized enhanced CLIP (eCLIP) data for single-protein interaction prediction.
  • Developed the PLERIO machine learning framework.
  • Extended the methodology to 220 cellular proteins for de novo interaction prediction.
  • Assessed the framework's performance with in vivo and in vitro experimental data.

Main Results:

  • PLERIO successfully reconstructs interactions with both highly and lowly expressed RNAs for a single protein.
  • Extrapolation to 220 proteins revealed limitations for de novo prediction using current in vivo IP data.
  • The approach showed potential utility for in vitro experiments like RNAcompete.

Conclusions:

  • The PLERIO framework offers a novel approach to predicting RNA-protein interactions, particularly for underrepresented RNAs.
  • Current in vivo IP data limitations may restrict the de novo prediction accuracy for multiple proteins.
  • The framework's applicability may be better suited for in vitro assays in its current form.