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Related Concept Videos

RNA Structure01:19

RNA Structure

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.
Different Types of RNA Have the Same Basic Structure
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...
RNA Structure01:23

RNA Structure

Overview
The basic structure of RNA consists of a five-carbon sugar and one of four nitrogenous bases. 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.
Different Types of RNA Have the Same Basic Structure
There are three main types of ribonucleic acid (RNA): messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). All three RNA types consist of a...
RNA Structure01:23

RNA Structure

Overview
The basic structure of RNA consists of a five-carbon sugar and one of four nitrogenous bases. 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.
Different Types of RNA Have the Same Basic Structure
There are three main types of ribonucleic acid (RNA): messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). All three RNA types consist of a...
Nucleic Acid Structure01:25

Nucleic Acid Structure

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.
DNA Structure
DNA has a double-helix structure. The...
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

VSEPR Theory for Determination of Electron Pair Geometries
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...

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Related Experiment Video

Updated: Jun 8, 2026

RNA Secondary Structure Prediction Using High-throughput SHAPE
13:42

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

A predictive model for secondary RNA structure using graph theory and a neural network.

Denise R Koessler1, Debra J Knisley, Jeff Knisley

  • 1Department of Mathematics and Statistics, East Tennessee State University, Johnson City, TN 37614, USA.

BMC Bioinformatics
|October 16, 2010
PubMed
Summary

This study introduces a novel computational method for predicting RNA secondary structures. Using a graph-theoretic approach and neural networks, the model accurately identifies RNA-like structures, offering a new tool for RNA folding predictions.

Related Experiment Videos

Last Updated: Jun 8, 2026

RNA Secondary Structure Prediction Using High-throughput SHAPE
13:42

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Structural Biology

Background:

  • Predicting RNA secondary structure from its primary sequence is a complex computational challenge.
  • Existing algorithms for RNA structure prediction have limitations and room for improvement.
  • Current methods often rely on minimizing free energy, which may not capture all structural nuances.

Purpose of the Study:

  • To develop a novel predictive model for secondary RNA structure.
  • To utilize a graph-theoretic representation and neural networks for RNA structure prediction.
  • To provide a probabilistic measure of RNA-likeness for predicted structures.

Main Methods:

  • Representing secondary RNA structure using a graph-theoretic tree model.
  • Defining a 'merge' graph operation to model the bonding of RNA structures.
  • Generating feature vectors from merge operations for neural network input.
  • Training a neural network to classify trees as RNA-like or not RNA-like.

Main Results:

  • The neural network accurately distinguished between known RNA-like and non-RNA-like structures.
  • The model assigned high probabilities to RNA-like trees and low probabilities to non-RNA-like trees.
  • The approach was successfully applied to predict the RNA-likeness of unclassified trees.

Conclusions:

  • A novel computational approach for secondary RNA structure prediction was developed.
  • The method employs graph theory and neural networks, offering a distinct alternative to energy-based methods.
  • The tool accurately classifies known structures and provides likelihood measures, aiding in the selection of the most probable RNA folds.