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

Search research articles

Related Experiment Video

Updated: May 10, 2026

Methodology for Accurate Detection of Mitochondrial DNA Methylation

Methodology for Accurate Detection of Mitochondrial DNA Methylation

Published on: May 20, 2018

An optimized algorithm for detecting and annotating regional differential methylation.

Sheng Li¹, Francine E Garrett-Bakelman, Altuna Akalin

¹Department of Physiology and Biophysics,Weill Cornell Medical College, 1305 York Ave., New York, NY 10065, USA.

BMC Bioinformatics

|June 6, 2013

Summary

This summary is machine-generated.

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

Deep dynamical models of single-cell multiomic velocities predict loss-of-function and rescue perturbations in B cells.

bioRxiv : the preprint server for biology·2026

Same author

Deep Learning-Based Analysis of Gene Expression Data and Gene-Related Information in Pediatric Surgical Oncology: A Scoping Review.

Cancer medicine·2026

Same author

Transcriptome sequencing of Hodgkin lymphoma Hodgkin and Reed-Sternberg cells reveals escape from NK cell recognition and an unfolded protein response.

Blood cancer journal·2026

Same author

Altered cholesterol immunometabolism activates the macrophage NLRP3-inflammasome in lung fibrosis.

American journal of respiratory cell and molecular biology·2026

Same author

Multiplexed biomarkers dynamically detect heterogeneous residual neuroblastoma cell clone activity in the bone marrow niche.

Cancer letters·2026

Same author

Author Correction: BCL6 enables Ph<sup>+</sup> acute lymphoblastic leukaemia cells to survive BCR-ABL1 kinase inhibition.

Nature·2026

Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026

Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026

Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026

Same journal

SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint.

BMC bioinformatics·2026

Same journal

Dual channel drug-drug interactions extraction based on cross attention.

BMC bioinformatics·2026

Same journal

FeSseqdb: a curated sequence-level database and interpretable machine learning framework for identifying iron-sulfur proteins.

BMC bioinformatics·2026

See all related articles

We developed eDMR, an open-source tool for identifying differentially methylated regions (DMRs) in DNA methylation data. This method accurately analyzes sequencing data and shows clinical relevance in stratifying cancer subtypes.

Area of Science:

Genomics
Epigenetics
Bioinformatics

Background:

DNA methylation profiling identifies differentially methylated regions (DMRs) crucial for development and disease.
Existing tools for regional analysis of bisulfite sequencing data are limited, despite its increasing prevalence.
High-throughput sequencing data requires robust methods for analyzing epigenetic modifications.

Purpose of the Study:

To introduce an open-source, optimized method for determining empirically based DMRs (eDMR) from high-throughput sequencing data.
To provide a scalable and accurate approach for identifying DMRs in various epigenomic datasets.
To develop a tool applicable to enriched whole-genome methylation profiling and other globally enriched epigenetic modification data.

Main Methods:

More Related Videos

Comprehensive DNA Methylation Analysis Using a Methyl-CpG-binding Domain Capture-based Method in Chronic Lymphocytic Leukemia Patients

Comprehensive DNA Methylation Analysis Using a Methyl-CpG-binding Domain Capture-based Method in Chronic Lymphocytic Leukemia Patients

Published on: June 16, 2017

Methyl-binding DNA capture Sequencing for Patient Tissues

Methyl-binding DNA capture Sequencing for Patient Tissues

Published on: October 31, 2016

Related Experiment Videos

Last Updated: May 10, 2026

Methodology for Accurate Detection of Mitochondrial DNA Methylation

Methodology for Accurate Detection of Mitochondrial DNA Methylation

Published on: May 20, 2018

Comprehensive DNA Methylation Analysis Using a Methyl-CpG-binding Domain Capture-based Method in Chronic Lymphocytic Leukemia Patients

Comprehensive DNA Methylation Analysis Using a Methyl-CpG-binding Domain Capture-based Method in Chronic Lymphocytic Leukemia Patients

Published on: June 16, 2017

Methyl-binding DNA capture Sequencing for Patient Tissues

Methyl-binding DNA capture Sequencing for Patient Tissues

Published on: October 31, 2016

Utilized a bimodal distribution model and weighted cost function for optimized regional methylation analysis.

Incorporated the spatial distribution of CpGs into the algorithm for accurate DMR boundary definition.

Developed a method for dependent adjustment of regional p-value combination and DMR annotation.

Main Results:

The eDMR method accurately defines boundaries of regions with significant epigenetic modifications.
The algorithm accounts for CpG spatial distribution, optimizing empirical region definition.
Demonstrated clinical relevance by correctly stratifying Acute Myeloid Leukemia (AML) tumor subtypes based on DMRs.

Conclusions:

The eDMR algorithm extends existing R pipelines (methylKit) and offers a valuable epigenomics resource.
Provides an accurate and scalable solution for finding DMRs in high-throughput methylation sequencing experiments.
eDMR is an open-source tool available for download, facilitating broader research applications.