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

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)

When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...

You might also read

Related Articles

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

Sort by

Same author

GLYATL1 is associated with metabolic and epigenetic changes and with endocrine resistance in luminal breast cancer.

Clinical epigenetics·2026

Same author

Clinical Validity of Circulating Tumor DNA as a Diagnostic Biomarker for Prostate Cancer: A Systematic Review.

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology·2026

Same author

Clonal evolution and transcriptional plasticity shape metastatic dissemination routes in prostate cancer.

Nature communications·2025

Same author

Prostate cancer cells converge to an inflammatory-like state upon metastatic dissemination.

Nature communications·2025

Same author

Molecular Plasticity Results in Oncofetal Reprogramming and Therapeutic Vulnerabilities in Juvenile Myelomonocytic Leukemia.

Blood cancer discovery·2025

Same author

Pipeline Olympics: continuable benchmarking of computational workflows for DNA methylation sequencing data against an experimental gold standard.

Nucleic acids research·2025

Same journal

Chlorinated VSLSs Surpass HCFCs in CFC-11-Equivalent Emissions for Ozone Layer Depletion in China.

Nature communications·2026

Same journal

Author Correction: Charge transfer in triphenylamine-tetrazine covalent organic frameworks for solar-driven hydrogen peroxide production.

Nature communications·2026

Same journal

Vegetation browning patterns under compound soil and atmospheric dryness in northern permafrost ecosystems.

Nature communications·2026

Same journal

Voltage imaging of CA1 pyramidal cells and SST+ interneurons reveals stability and plasticity mechanisms of spatial firing.

Nature communications·2026

Same journal

Radical-omics reveals the hydrogen-abstraction pathway of isoprene oxidation.

Nature communications·2026

Same journal

Toughening elastomer via sequentially activated multi-pathway energy dissipation.

Nature communications·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 1, 2025

Methyl-binding DNA capture Sequencing for Patient Tissues

Methyl-binding DNA capture Sequencing for Patient Tissues

Published on: October 31, 2016

MethylBERT enables read-level DNA methylation pattern identification and tumour deconvolution using a

Yunhee Jeong¹, Clarissa Gerhäuser², Guido Sauter³

¹Division of Cancer Epigenomics, German Cancer Research Center (DKFZ), Heidelberg, Germany. y.jeong@dkfz-heidelberg.de.

Nature Communications

|January 17, 2025

Summary

This summary is machine-generated.

MethylBERT, a new AI model, accurately identifies cancer DNA methylation patterns from sequencing reads. This advances tumor purity estimation and enables early cancer detection via liquid biopsies.

More Related Videos

DNA Methylation: Bisulphite Modification and Analysis

DNA Methylation: Bisulphite Modification and Analysis

Published on: October 21, 2011

Enhanced Reduced Representation Bisulfite Sequencing for Assessment of DNA Methylation at Base Pair Resolution

Enhanced Reduced Representation Bisulfite Sequencing for Assessment of DNA Methylation at Base Pair Resolution

Published on: February 24, 2015

Related Experiment Videos

Last Updated: Jun 1, 2025

Methyl-binding DNA capture Sequencing for Patient Tissues

Methyl-binding DNA capture Sequencing for Patient Tissues

Published on: October 31, 2016

DNA Methylation: Bisulphite Modification and Analysis

DNA Methylation: Bisulphite Modification and Analysis

Published on: October 21, 2011

Enhanced Reduced Representation Bisulfite Sequencing for Assessment of DNA Methylation at Base Pair Resolution

Enhanced Reduced Representation Bisulfite Sequencing for Assessment of DNA Methylation at Base Pair Resolution

Published on: February 24, 2015

Area of Science:

Genomics
Epigenetics
Bioinformatics

Background:

DNA methylation (DNAm) is a critical epigenetic modification significantly altered in cancer.
Read-level methylome data offers deeper insights than array-based methods due to comprehensive genomic coverage and preservation of rare cell signals.

Purpose of the Study:

To introduce MethylBERT, a Transformer-based model for classifying read-level methylation patterns.
To leverage methylation patterns and genomic sequences for identifying tumor-derived reads and estimating tumor cell fractions in bulk samples.

Main Methods:

Development of MethylBERT, a Transformer-based deep learning model.
Utilizing local genomic sequence and methylation patterns for read classification.
Evaluating MethylBERT against existing deconvolution methods on diverse datasets.

Main Results:

MethylBERT demonstrates superior performance compared to current deconvolution techniques.
The model achieves high accuracy across varying methylation pattern complexities, read lengths, and coverage.
Successful application in cell-type deconvolution and early cancer diagnostics using liquid biopsies.

Conclusions:

MethylBERT signifies a major breakthrough in analyzing read-level methylomes, enabling precise tumor purity estimation.
The model's versatility extends its utility to non-cancerous bulk methylome studies and enhances cancer research.