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

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
RNA-seq03:21

RNA-seq

10.2K
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...
10.2K
RNA Stability01:53

RNA Stability

33.7K
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...
33.7K
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

5.9K
Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
5.9K
Conserved Binding Sites01:49

Conserved Binding Sites

4.3K
Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
4.3K
Homologous Recombination02:31

Homologous Recombination

4.7K
4.7K

You might also read

Related Articles

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

Sort by
Same author

CAP: Commutative algebra prediction of protein-nucleic acid binding affinities.

Machine learning: science and technology·2026
Same author

Molecular Topological Deep Learning for Polymer Property Prediction.

ACS nano·2026
Same author

Commutative algebra neural network reveals genetic origins of diseases.

ArXiv·2025
Same author

A Review of Topological Data Analysis and Topological Deep Learning in Molecular Sciences.

Journal of chemical information and modeling·2025
Same author

The Hodge Laplacian advances inference of single-cell trajectories.

Nature methods·2025
Same author

CAKL: Commutative algebra k-mer learning of genomics.

ArXiv·2025
Same journal

Nanotechnology-Stem Cell Strategies in 3D Glioblastoma Organoid: Targeting Glioma Stem Cells Within a Complex Tumor Microenvironment.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Mapping the 3D Chromosome Organization of a Biosynthetic Gene Cluster by Capture Hi-C (CHi-C).

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Mapping the 3D Chromosome Organization of Streptomyces by Hi-C.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

CUT&Tag Epigenomic Profiling of Biosynthetic Gene Clusters in Arabidopsis thaliana.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Rhizobium rhizogenes-Mediated Hairy Root Transformation Protocol for Lotus japonicus and Other Legumes.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Characterization of Bioactive Saponins from Sea Cucumbers.

Methods in molecular biology (Clifton, N.J.)·2026
See all related articles

Related Experiment Video

Updated: Aug 5, 2025

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

Persistent Homology for RNA Data Analysis.

Kelin Xia1, Xiang Liu2,3, JunJie Wee2

  • 1Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore, Singapore. xiakelin@ntu.edu.sg.

Methods in Molecular Biology (Clifton, N.J.)
|March 24, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces persistent homology and persistent spectral models for RNA molecular representations. These methods enhance RNA data analysis for structure, flexibility, dynamics, and function prediction using machine learning.

Keywords:
Machine learningMolecular representationPersistent homologyPersistent spectralRNA data analysisSimplicial complexSpectral simplicial complex

More Related Videos

Identification of RNAs Engaged in Direct RNA-RNA Interaction with a Long Non-Coding RNA
07:24

Identification of RNAs Engaged in Direct RNA-RNA Interaction with a Long Non-Coding RNA

Published on: July 9, 2021

2.5K
Practical Aspects of Sample Preparation and Setup of 1H R1ρ Relaxation Dispersion Experiments of RNA
08:17

Practical Aspects of Sample Preparation and Setup of 1H R1ρ Relaxation Dispersion Experiments of RNA

Published on: July 9, 2021

4.8K

Related Experiment Videos

Last Updated: Aug 5, 2025

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
Identification of RNAs Engaged in Direct RNA-RNA Interaction with a Long Non-Coding RNA
07:24

Identification of RNAs Engaged in Direct RNA-RNA Interaction with a Long Non-Coding RNA

Published on: July 9, 2021

2.5K
Practical Aspects of Sample Preparation and Setup of 1H R1ρ Relaxation Dispersion Experiments of RNA
08:17

Practical Aspects of Sample Preparation and Setup of 1H R1ρ Relaxation Dispersion Experiments of RNA

Published on: July 9, 2021

4.8K

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Machine Learning

Background:

  • Effective molecular representations are crucial for RNA data analysis using machine learning.
  • Current methods often rely on geometric or graph-based approaches.

Purpose of the Study:

  • To introduce novel persistent homology and persistent spectral models for RNA structure and interaction representation.
  • To enhance RNA data analysis by providing robust molecular descriptors.

Main Methods:

  • Utilizing persistent homology built on simplicial complexes, a generalization of graph models.
  • Employing persistent spectral models, combining filtration with spectral graph, simplicial complex, and hypergraph approaches.
  • Developing hypergraph-based embedded persistent homology.

Main Results:

  • Obtained persistent attributes from the proposed models for RNA analysis.
  • Demonstrated the potential for improved RNA structure, flexibility, dynamics, and function analysis.
  • Showcased the integration of these persistent attributes with machine learning models.

Conclusions:

  • Persistent homology and persistent spectral models offer powerful new representations for RNA data.
  • These methods advance the field of RNA bioinformatics and machine learning applications.
  • The proposed models can significantly boost the performance of RNA learning models.