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-seq03:21

RNA-seq

10.3K
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...
10.3K
Ribosome Profiling02:24

Ribosome Profiling

3.6K
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.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...
3.6K
Protein Folding Quality Check in the RER01:29

Protein Folding Quality Check in the RER

3.8K
ER is the primary site for the maturation and folding of soluble and transmembrane secretory proteins. The calnexin cycle is a specific chaperone system that folds and assesses the confirmation of N-glycosylated proteins before they can exit the ER lumen. The primary players of this quality check pipeline are the lectins, ER-resident chaperones, and a glucosyl transferase enzyme. In case the calnexin system in the lumen fails to salvage a misfolded protein, it is transported to the cytoplasm...
3.8K
Nucleic Acid Structure01:25

Nucleic Acid Structure

6.3K
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...
6.3K

You might also read

Related Articles

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

Sort by
Same author

KSRV: a Kernel PCA-Based framework for inferring spatial RNA velocity at single-cell resolution.

Frontiers in genetics·2025
Same author

Machine learning predictions from unpredictable chaos.

Journal of the Royal Society, Interface·2025
Same author

Transcriptional bursting dynamics in gene expression.

Frontiers in genetics·2024
Same author

ABC2A: A Straightforward and Fast Method for the Accurate Backmapping of RNA Coarse-Grained Models to All-Atom Structures.

Molecules (Basel, Switzerland)·2024
Same author

Computational Modeling of DNA 3D Structures: From Dynamics and Mechanics to Folding.

Molecules (Basel, Switzerland)·2023
Same author

Ab initio predictions for 3D structure and stability of single- and double-stranded DNAs in ion solutions.

PLoS computational biology·2022

Related Experiment Video

Updated: Aug 23, 2025

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

Analyzing and Building Nucleic Acid Structures with 3DNA

Published on: April 26, 2013

20.7K

RNAStat: An Integrated Tool for Statistical Analysis of RNA 3D Structures.

Zhi-Hao Guo1,2, Li Yuan1,2, Ya-Lan Tan1

  • 1Research Center of Nonlinear Science, School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan, China.

Frontiers in Bioinformatics
|October 28, 2022
PubMed
Summary

RNAStat is a new tool for analyzing RNA 3D structures, providing statistical insights into their size, shape, and base-pairing geometry. This aids in RNA structure prediction and modeling by offering comprehensive statistical data.

Keywords:
RNA 3D structurenon-canonical base pairsecondary structure motifsstatistical analysisstructure evaluation

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

31.6K
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

4.4K

Related Experiment Videos

Last Updated: Aug 23, 2025

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

Analyzing and Building Nucleic Acid Structures with 3DNA

Published on: April 26, 2013

20.7K
RNA Secondary Structure Prediction Using High-throughput SHAPE
13:42

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

31.6K
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

4.4K

Area of Science:

  • Structural Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Understanding RNA 3D architectures is crucial for elucidating cellular functions.
  • Accurate statistical scoring functions are vital for RNA structure prediction and evaluation.
  • Existing tools for comprehensive statistical analysis of RNA 3D structures are limited.

Purpose of the Study:

  • To develop RNAStat, an integrated tool for comprehensive statistical analysis of RNA 3D structures.
  • To provide insights into RNA structural properties, secondary structure motifs, and base-pairing geometry.
  • To facilitate the development of improved RNA structure modeling and prediction methods.

Main Methods:

  • RNAStat automatically calculates RNA size, shape, and distributions.
  • Utilizes DSSR for annotation of RNA secondary structure motifs (base pairs, stems, loops).
  • Calculates base-pairing/stacking geometry using local coordinate systems and provides atom-distance distributions.

Main Results:

  • RNAStat offers detailed statistical information on RNA 3D structural properties and secondary structure motifs.
  • The tool enables the calculation of base-pairing geometry and atom-distance distributions.
  • A comprehensive statistical analysis of RNA structures was performed using a non-redundant dataset.

Conclusions:

  • RNAStat serves as a valuable tool for statistical analysis of RNA 3D structures.
  • The generated statistical data can guide RNA structure modeling and prediction.
  • The RNAStat tool, dataset, and statistical data are publicly available on GitHub.