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Related Concept Videos

RNA Structure01:19

RNA Structure

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The basic structure of RNA consists of a string of ribonucleotides attached by phosphodiester bonds. Although most RNA is single-stranded, it can form complex secondary and tertiary structures. Such structures play essential roles in the regulation of transcription and translation.
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There are three main types of ribonucleic acid (RNA) involved in protein synthesis: messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). All three...
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The pentose sugar in DNA is deoxyribose, while in RNA the pentose sugar is ribose. The difference between the sugars is the presence of the hydroxyl group on the ribose's second carbon and a hydrogen on the deoxyribose's second carbon. The phosphate residue attaches to the hydroxyl group of the 5′ carbon of one sugar and the hydroxyl group of the 3′ carbon of the sugar of the next nucleotide, which forms  a 5′ to 3′ phosphodiester linkage.
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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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RNA interference (RNAi) is a cellular mechanism that inhibits gene expression by suppressing its transcription or activating the RNA degradation process. The mechanism was discovered by Andrew Fire and Craig Mello in 1998 in plants. Today, it is observed in almost all eukaryotes, including protozoa, flies, nematodes, insects, parasites, and mammals. This precise cellular mechanism of gene silencing has been developed into a technique that provides an efficient way to identify and determine the...
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Updated: Jul 1, 2025

RNA Secondary Structure Prediction Using High-throughput SHAPE
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Published on: May 31, 2013

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Graph Convolutional Network for predicting secondary structure of RNA.

Palawat Busaranuvong1,2, Aukkawut Ammartayakun1, Dmitry Korkin3

  • 1Department of Data Science, Worcester Polytechnic Institute, Worcester, 01609, Massachusetts, USA.

Research Square
|March 11, 2024
PubMed
Summary

This study introduces GCNfold, a novel Graph Convolutional Network for predicting RNA secondary structures. GCNfold improves accuracy by integrating minimum free energy, outperforming existing algorithms for RNA folding.

Keywords:
Energy-based ModelGraph Convolutional Neural NetworkRNA Secondary StructureRNAfoldSARS-CoV-2

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Structural Biology

Background:

  • Predicting RNA secondary structures is crucial for molecular diagnostics and RNA therapeutics.
  • The complexity of RNA folding presents significant computational challenges.

Approach:

  • A Graph Convolutional Network (GCNfold) was developed to predict RNA secondary structures.
  • GCNfold models RNA sequences as graphs, utilizing prior base-pairing probabilities from McCaskill's partition function.
  • Minimum free energy information was integrated into the network for enhanced robustness.

Key Points:

  • GCNfold demonstrates superior performance compared to state-of-the-art RNA folding algorithms.
  • The Symmetric Argmax Post-processing algorithm ensures the generation of valid RNA structures.
  • The algorithm was validated on the SARS-CoV-2 E gene, analyzing structures across Betacoronavirus subgenera.

Conclusions:

  • GCNfold offers a robust and accurate method for RNA secondary structure prediction.
  • This approach advances the potential for RNA-based diagnostics and therapeutics.