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Related Concept Videos

RNA Structure01:23

RNA Structure

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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.
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Intact DNA strands can be found in fossils, while scientists sometimes struggle to keep RNA intact under laboratory conditions. The structural variations between RNA and DNA underlie the differences in their stability and longevity. Because DNA is double-stranded, it is inherently more stable. The single-stranded structure of RNA is less stable but also more flexible and can form weak internal bonds. Additionally, most RNAs in the cell are relatively short, while DNA can be up to 250 million...
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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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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
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Updated: Sep 2, 2025

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

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Markov Chain-Based Sampling for Exploring RNA Secondary Structure under the Nearest Neighbor Thermodynamic Model and

Anna Kirkpatrick1, Kalen Patton1,2, Prasad Tetali1,2

  • 1School of Mathematics, Georgia Institute of Technology, Atlanta, GA 30332, USA.

Mathematical & Computational Applications
|August 4, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a Markov chain to analyze RNA secondary structure dispersion using the Nearest Neighbor Thermodynamic Model. The method helps understand branching properties independent of specific sequences, aiding bioinformatics research.

Keywords:
Markov chain Monte CarloMarkov chain convergenceRNA secondary structurenearest neighbor thermodynamic Model

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Biophysics

Background:

  • Ribonucleic acid (RNA) secondary structures and branching are crucial for biological function.
  • The Nearest Neighbor Thermodynamic Model (NNTM) is widely used for RNA structure analysis, but assessing the dispersion of its results is challenging.

Purpose of the Study:

  • To develop a Markov chain for analyzing the dispersion of RNA secondary structures and branching properties.
  • To provide a method for evaluating NNTM energy function minimization results independent of specific nucleotide sequences.

Main Methods:

  • Modeling RNA secondary structures as plane trees with energies assigned via NNTM.
  • Defining a Gibbs distribution on these trees and establishing a bijection with 2-Motzkin paths.
  • Constructing a Markov chain converging to the Gibbs distribution and analyzing its spectral gap for fast mixing time.

Main Results:

  • A novel Markov chain was constructed to examine RNA secondary structure dispersion.
  • The chain demonstrates fast mixing time, verified through spectral gap estimation.
  • The algorithm is applicable to long RNA sequences and energy model parameter analysis.

Conclusions:

  • The developed Markov chain offers a new tool for exploring RNA branching structure.
  • This approach aids in understanding the dependence of branching structure on energy model parameters.
  • The mathematical techniques employed have potential applications in bioinformatics and computational biology.