Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Epigenetic Regulation

Epigenetic Regulation

Epigenetic changes alter the physical structure of the DNA without changing the genetic sequence and often regulate whether genes are turned on or off. This regulation ensures that each cell produces only proteins necessary for its function. For example, proteins that promote bone growth are not produced in muscle cells. Epigenetic mechanisms play an essential role in healthy development. Conversely, precisely regulated epigenetic mechanisms are disrupted in diseases like cancer.
X-chromosome...

Mutations

Mutations

DNA as a Genetic Template

DNA as a Genetic Template

Two structural features of the DNA molecule provide a basis for the mechanisms of heredity: the four nucleotide bases and its double-stranded nature. The Watson-Crick model of double-helical DNA structure, proposed in 1952, drew heavily upon the X-ray crystallography work of researchers Rosalind Franklin and Maurice Wilkins. Watson, Crick, and Wilkins jointly received the Nobel Prize in Physiology or Medicine for their work in 1962. Franklin was, controversially, excluded from the prize for...

Genomic DNA in Eukaryotes

Genomic DNA in Eukaryotes

Eukaryotes have large genomes compared to prokaryotes. To fit their genomes into a cell, eukaryotic DNA is packaged extraordinarily tightly inside the nucleus. To achieve this, DNA is tightly wound around proteins called histones, which are packaged into nucleosomes that are joined by linker DNA and coil into chromatin fibers. Additional fibrous proteins further compact the chromatin, which is recognizable as chromosomes during certain phases of cell division.

You might also read

Related Articles

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

Sort by

Same author

Bridging local-global transmembrane protein contexts with contrastive pretraining for alignment-free pathogenicity prediction.

Briefings in bioinformatics·2026

Same author

Global Trends and Research Hot Spots in Medication Regimen Simplification: Bibliometric Analysis.

JMIR aging·2026

Same author

Effectiveness of digital health interventions in improving mental health in older adults with mild cognitive impairment: A systematic review and meta-analysis.

Digital health·2026

Same author

MEMO-Stab2: Multi-View Sequence-Based Deep Learning Framework for Predicting Mutation-Induced Stability Changes in Transmembrane Proteins.

Journal of chemical information and modeling·2025

Same author

Predicting the regulatory impacts of noncoding variants on gene expression through epigenomic integration across tissues and single-cell landscapes.

Nature computational science·2025

Same author

Genome-wide profiling of GRAS genes in flax (<i>Linum usitatissimum</i> L.) reveals <i>LuGRAS30</i> as a key regulator of drought stress resistance.

GM crops & food·2025

Search research articles

Related Experiment Video

Updated: May 30, 2025

DamID-seq: Genome-wide Mapping of Protein-DNA Interactions by High Throughput Sequencing of Adenine-methylated DNA Fragments

DamID-seq: Genome-wide Mapping of Protein-DNA Interactions by High Throughput Sequencing of Adenine-methylated DNA Fragments

Published on: January 27, 2016

Epigenetic Impacts of Non-Coding Mutations Deciphered Through Pre-Trained DNA Language Model at Single-Cell

Zhe Liu¹, An Gu¹, Yihang Bao¹

¹Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200230, China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)

|January 31, 2025

Summary

Methven, a deep learning tool, predicts non-coding mutation effects on DNA methylation at single-cell resolution. It accurately identifies disease-associated epigenetic changes, aiding in understanding complex diseases and personalized medicine.

Keywords:

DNA methylation SNP‐CpG interactions deep learning non‐coding mutations single‐cell resolution

More Related Videos

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

Published on: August 21, 2016

Immunostaining for DNA Modifications: Computational Analysis of Confocal Images

Immunostaining for DNA Modifications: Computational Analysis of Confocal Images

Published on: September 7, 2017

Related Experiment Videos

Last Updated: May 30, 2025

DamID-seq: Genome-wide Mapping of Protein-DNA Interactions by High Throughput Sequencing of Adenine-methylated DNA Fragments

DamID-seq: Genome-wide Mapping of Protein-DNA Interactions by High Throughput Sequencing of Adenine-methylated DNA Fragments

Published on: January 27, 2016

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

Published on: August 21, 2016

Immunostaining for DNA Modifications: Computational Analysis of Confocal Images

Immunostaining for DNA Modifications: Computational Analysis of Confocal Images

Published on: September 7, 2017

Area of Science:

Genomics
Epigenetics
Computational Biology

Background:

DNA methylation is crucial for gene regulation and disease, especially in non-coding regions.
Predicting epigenetic effects of non-coding mutations at single-cell resolution is challenging.
Current tools lack capacity for dynamic, cell-type-specific regulatory change prediction.

Purpose of the Study:

To present Methven, a deep learning framework for predicting non-coding mutation effects on DNA methylation at single-cell resolution.
To model SNP-CpG interactions and regulatory changes with high accuracy.
To identify disease-associated epigenetic alterations and pathways.

Main Methods:

Methven integrates DNA sequence and single-cell ATAC-seq data.
It employs a divide-and-conquer approach and a pre-trained DNA language model.
Models SNP-CpG interactions over 100 kbp genomic distances.

Main Results:

Methven accurately predicts short- and long-range regulatory interactions.
It outperforms existing methods and generalizes to monocyte datasets.
Identified CpG sites linked to rheumatoid arthritis and immune regulation pathways.

Conclusions:

Methven offers a powerful tool for understanding non-coding mutations and epigenetic changes in complex diseases.
It provides insights into gene regulation and disease progression.
Demonstrates potential for basic research, clinical applications, and personalized medicine.