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

You might also read

Related Articles

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

Sort by

Same author

Revealing the genetic regulation of wood traits and secondary cell wall development in <i>Ginkgo biloba</i>: an integrated analysis from the perspectives of GWAS, TWAS, and WGCNA.

Horticulture research·2026

Same author

Distinct and shared synergistic effects of high-fat and high-iron diets on liver tumorigenesis and transcriptomic remodeling in mice.

bioRxiv : the preprint server for biology·2026

Same author

Role of Bevacizumab in Multiple Glioblastomas: A Retrospective Single-Center Analysis.

Neurosciences (Riyadh, Saudi Arabia)·2026

Same author

Germline polygenic score for prostate cancer aggressiveness.

medRxiv : the preprint server for health sciences·2026

Same author

Selective elimination of circulating effector CD8 T cells via LTβR blockade separates anti-CD137 efficacy from toxicity.

Science advances·2026

Same author

The ANXA2P1-hnRNP F-HK2/c-Myc Positive Feedback Loop Promotes Proliferation and Glycolytic Metabolism in Gastric Cancer.

International journal of biological sciences·2026

Same journal

Mild oxidative stress and dietary epigenetic modulators direct DNA methylation remodeling toward stress-resilience pathways.

BMC genomics·2026

Same journal

Integrative ATAC-Seq and RNA-Seq analysis identifies key genes for intramuscular fat content in Laiwu pigs.

BMC genomics·2026

Same journal

A comprehensive long RNA landscape of multi-regional porcine lung-derived small extracellular vesicles.

BMC genomics·2026

Same journal

pGWAS-Portal: a comprehensive online platform for integrative post-genome-wide association study analysis.

BMC genomics·2026

Same journal

Physiological and transcriptomic analyses of Rosa persica in response to drought stress and functional validation of the transcription factor RpERF113-like.

BMC genomics·2026

Same journal

Integrated analysis of chromatin accessibility and transcriptome profiles in granulosa cells of sheep with different FecB genotypes.

BMC genomics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Mar 16, 2026

Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance

Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance

Published on: December 13, 2024

MBDDiff: an R package designed specifically for processing MBDcap-seq datasets.

Yuanhang Liu^1,2, Desiree Wilson², Robin J Leach^2,3

¹Greehey Children's Cancer Research Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA.

|August 25, 2016

Summary

This summary is machine-generated.

A new algorithm, MBDDiff, accurately analyzes DNA methylation data from Methyl-CpG binding domain-based capture followed by high-throughput sequencing (MBDCap-seq). It outperforms existing methods, especially in noisy conditions, aiding cancer research.

Keywords:

DNA methylation Differential methylated regions Differentially methylation MBDCap-seq MBDDiff XBSeq

More Related Videos

Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs

Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs

Published on: September 16, 2019

Exploring m6A and m5C Epitranscriptomes upon Viral Infection: an Example with HIV

Exploring m6A and m5C Epitranscriptomes upon Viral Infection: an Example with HIV

Published on: March 5, 2022

Related Experiment Videos

Last Updated: Mar 16, 2026

Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance

Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance

Published on: December 13, 2024

Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs

Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs

Published on: September 16, 2019

Exploring m6A and m5C Epitranscriptomes upon Viral Infection: an Example with HIV

Exploring m6A and m5C Epitranscriptomes upon Viral Infection: an Example with HIV

Published on: March 5, 2022

Area of Science:

Genomics
Epigenetics
Bioinformatics

Background:

DNA methylation is crucial in biological processes like aging and cancer.
Methyl-CpG binding domain-based capture followed by high-throughput sequencing (MBDCap-seq) is a key method for genome-wide DNA methylation analysis.
Existing MBDCap-seq data processing methods lack region-specific genomic characteristic considerations, impacting signal and noise measurements.

Purpose of the Study:

To develop a novel differential methylation quantification algorithm specifically for MBDCap-seq data.
To address limitations in current MBDCap-seq data analysis software.
To improve the accuracy of DNA methylation analysis, particularly under noisy experimental conditions.

Main Methods:

Developed MBDDiff, a new algorithm for MBDCap-seq data analysis.
Evaluated MBDDiff using simulated datasets with varying noise levels based on negative binomial and Poisson distributions.
Compared MBDDiff's performance against established algorithms (MEDIPS, DESeq) using metrics like AUC, false discoveries, and statistical power.
Applied MBDDiff to analyze prostate, endometrial, and triple-negative breast cancer datasets.

Main Results:

MBDDiff demonstrated superior performance compared to MEDIPS and DESeq in simulations, particularly in terms of AUC and reduced false discoveries across different noise levels.
The algorithm showed improved performance with increased background noise, while maintaining comparable results at lower noise levels.
Application of MBDDiff to cancer datasets identified potential factors contributing to cancer development and recurrence.

Conclusions:

MBDDiff is an accurate differential methylation analysis tool specifically designed for MBDCap-seq data.
The algorithm excels in handling noisy datasets, offering improved accuracy over existing methods.
MBDDiff facilitates the identification of novel epigenetic targets relevant to biological processes and diseases like cancer.