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Related Experiment Video

Updated: Feb 13, 2026

RNA Secondary Structure Prediction Using High-throughput SHAPE

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

Structure-aware Graph Learning Predicts RNA Editability Across Tissues and Species.

Zohar Rosenwasser, Michael Levitt, Erez Y Levanon

Biorxiv : the Preprint Server for Biology

|February 12, 2026

Summary

This summary is machine-generated.

Predicting RNA editing by ADAR enzymes is challenging. A new graph-attention framework, A dar E dit , accurately predicts RNA editing sites by considering RNA structure, outperforming sequence-based models.

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RNA Secondary Structure Prediction Using High-throughput SHAPE

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Published on: May 31, 2013

A Nonsequencing Approach for the Rapid Detection of RNA Editing

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Published on: April 21, 2022

RNA Catalyst as a Reporter for Screening Drugs against RNA Editing in Trypanosomes

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Published on: July 22, 2014

Area of Science:

Biochemistry
Computational Biology
Genomics

Background:

Adenosine-to-Inosine (A-to-I) RNA editing, mediated by ADAR enzymes, is a crucial post-transcriptional modification with therapeutic potential.
Predicting A-to-I editing sites is difficult due to reliance on double-stranded RNA (dsRNA) geometry and stability, not just sequence.
Current models struggle to capture the complex structural determinants of ADAR enzyme recognition.

Purpose of the Study:

To develop a structure-explicit computational framework for predicting A-to-I RNA editing sites.
To improve the accuracy of predicting RNA editing by incorporating dsRNA structural features.
To investigate conserved principles of ADAR substrate recognition across different species.

Main Methods:

Developed A dar E dit , a graph-attention framework representing dsRNA substrates as nucleotide graphs with backbone and base-pair edges.
Augmented the graph representation with typed interactions and a motif-sensitive sequence branch.
Trained and evaluated the model on human inverted Alu duplexes using RNAfold-predicted secondary structures and GTEx RNA-seq data.

Main Results:

A dar E dit consistently outperformed sequence-only CNN, transformer, and RNA language models across multiple tissue contexts.
Achieved high discrimination performance (AUROC/AUPRC = 0.96; F1 ≈ 0.90) on combined human tissue data.
Demonstrated successful transferability of the graph representation to evolutionarily distant non-Alu species, revealing conserved ADAR recognition principles.

Conclusions:

A dar E dit provides a powerful, structure-aware approach for predicting A-to-I RNA editing sites.
The findings highlight the importance of dsRNA structure in ADAR substrate recognition, with conserved principles across species.
The model's attention profiles and mutagenesis analysis offer insights into biochemical constraints and long-range structural influences on RNA editing.