Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

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 Structure01:19

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-seq03:21

RNA-seq

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. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...
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...
Types of RNA01:20

Types of RNA

Three main types of RNA are involved in protein synthesis: messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). These RNAs perform diverse functions and can be broadly classified as protein-coding or non-coding RNA. Non-coding RNAs play important roles in regulating gene expression in response to developmental and environmental changes. Non-coding RNAs in prokaryotes can be manipulated to develop more effective antibacterial drugs for human or animal use.
RNA Performs Diverse...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Identifying transcription factors with cell-type specific DNA binding signatures.

BMC genomics·2024
Same author

Assessing Global-Local Secondary Structure Fingerprints to Classify RNA Sequences With Deep Learning.

IEEE/ACM transactions on computational biology and bioinformatics·2021
Same author

WACS: improving ChIP-seq peak calling by optimally weighting controls.

BMC bioinformatics·2021
Same author

RiboFSM: frequent subgraph mining for the discovery of RNA structures and interactions.

BMC bioinformatics·2014
Same author

IncMD: incremental trie-based structural motif discovery algorithm.

Journal of bioinformatics and computational biology·2014
Same author

RNA-level unscrambling of fragmented genes in Diplonema mitochondria.

RNA biology·2013

Related Experiment Video

Updated: Jul 9, 2026

Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells
10:34

Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells

Published on: December 9, 2022

Simultaneous alignment and structure prediction of three RNA sequences.

Beeta Masoumi, Marcel Turcotte

    International Journal of Bioinformatics Research and Applications
    |December 1, 2007
    PubMed
    Summary

    Using three RNA sequences for comparative analysis improves secondary structure prediction accuracy compared to two sequences. This method enhances prediction reliability and accuracy for RNA structure modeling.

    More Related Videos

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

    RNA Secondary Structure Prediction Using High-throughput SHAPE

    Published on: May 31, 2013

    Analyzing and Building Nucleic Acid Structures with 3DNA
    16:24

    Analyzing and Building Nucleic Acid Structures with 3DNA

    Published on: April 26, 2013

    Related Experiment Videos

    Last Updated: Jul 9, 2026

    Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells
    10:34

    Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells

    Published on: December 9, 2022

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

    RNA Secondary Structure Prediction Using High-throughput SHAPE

    Published on: May 31, 2013

    Analyzing and Building Nucleic Acid Structures with 3DNA
    16:24

    Analyzing and Building Nucleic Acid Structures with 3DNA

    Published on: April 26, 2013

    Area of Science:

    • Computational Biology
    • Bioinformatics
    • Molecular Biology

    Background:

    • Comparative RNA sequence analysis is a powerful method for predicting RNA secondary structures.
    • Recent advancements include the determination of ribosomal subunits (30S and 50S), providing further evidence for these methods.
    • Current inference tools integrate free energy minimization with comparative analysis to enhance prediction accuracy.

    Purpose of the Study:

    • To investigate whether using three input RNA sequences improves average secondary structure prediction accuracy over using two.
    • To determine if prediction accuracy, particularly the minimum accuracy, is enhanced with three sequences compared to two.
    • To assess if the consensus structure derived from three sequences is less representative of individual sequences and if average coverage decreases.

    Main Methods:

    • Comparative RNA sequence analysis.
    • Secondary structure prediction using inference tools that combine free energy minimization and comparative analysis.
    • Evaluation of prediction accuracy and consensus structure representation using two versus three input sequences.

    Main Results:

    • Using three input sequences significantly improves the average accuracy of RNA secondary structure predictions compared to using two sequences.
    • The minimum prediction accuracy (worst-case scenario) is also demonstrably higher when employing three sequences.
    • The consensus structure derived from three sequences shows reduced representativeness of individual sequences, with a corresponding decrease in average coverage.

    Conclusions:

    • Incorporating a third RNA sequence into comparative analysis enhances the overall accuracy and reliability of RNA secondary structure predictions.
    • While consensus structures become less representative of individual sequences, the improved accuracy justifies the use of three sequences.
    • These findings support the advancement of computational tools for more precise RNA structure modeling.