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 23, 2026

Methyl-binding DNA capture Sequencing for Patient Tissues

Methyl-binding DNA capture Sequencing for Patient Tissues

Published on: October 31, 2016

A Scalable, Flexible Workflow for MethylCap-Seq Data Analysis.

Benjamin Rodriguez¹, Hok-Hei Tam, David Frankhouser

¹The Ohio State University Comprehensive Cancer Center Columbus, Ohio, USA.

IEEE International Workshop on Genomic Signal Processing and Statistics : [Proceedings]. IEEE International Workshop on Genomic Signal Processing and Statistics

|April 10, 2012

Summary

This summary is machine-generated.

Related Concept Videos

RNA-seq

RNA-seq

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 microarray-based...

You might also read

Related Articles

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

Sort by

Same author

Caregiver experiences of healthcare-related administrative burden: a scoping review.

Health affairs scholar·2026

Same author

3D EM uncovers mitochondrial network remodeling in residual triple negative breast cancer after conventional chemotherapy treatments.

iScience·2026

Same author

The MICOS Complex Regulates Mitochondrial Structure and Oxidative Stress During Age-Dependent Structural Deficits in the Kidney.

Aging cell·2026

Same author

ATF4 Coordinates Transcriptomic and Structural Adaptations in Aging Muscle.

bioRxiv : the preprint server for biology·2026

Same author

Overcoming vascular niche-mediated TKI resistance in acute myeloid leukemia through miR-126 inhibition.

NPJ systems biology and applications·2026

Same author

Nutrient State, Aging, and Diet Modulate SAM50-Dependent Mitochondrial Remodeling and Systemic Metabolic Signatures.

Research square·2026

Same journal

Integrative Analysis of Multi-modal Correlated Imaging-Genomics Data in Glioblastoma.

IEEE International Workshop on Genomic Signal Processing and Statistics : [proceedings]. IEEE International Workshop on Genomic Signal Processing and Statistics·2023

Same journal

Integrative Sparse Bayesian Analysis of High-dimensional Multi-platform Genomic Data in Glioblastoma.

IEEE International Workshop on Genomic Signal Processing and Statistics : [proceedings]. IEEE International Workshop on Genomic Signal Processing and Statistics·2023

Same journal

Improving the Flexibility of RNA-Seq Data Analysis Pipelines.

IEEE International Workshop on Genomic Signal Processing and Statistics : [proceedings]. IEEE International Workshop on Genomic Signal Processing and Statistics·2016

Same journal

An Approach for Assessing RNA-seq Quantification Algorithms in Replication Studies.

IEEE International Workshop on Genomic Signal Processing and Statistics : [proceedings]. IEEE International Workshop on Genomic Signal Processing and Statistics·2016

Same journal

A Bayesian Model for SNP Discovery Based on Next-Generation Sequencing Data.

IEEE International Workshop on Genomic Signal Processing and Statistics : [proceedings]. IEEE International Workshop on Genomic Signal Processing and Statistics·2016

Same journal

A Bayesian Graphical Model for Integrative Analysis of TCGA Data.

IEEE International Workshop on Genomic Signal Processing and Statistics : [proceedings]. IEEE International Workshop on Genomic Signal Processing and Statistics·2015

See all related articles

This study introduces a bioinformatics workflow to address challenges in analyzing DNA methylation data from next-generation sequencing (NGS). The scalable workflow aids researchers in quality control, data analysis, and biological interpretation of methylation profiling projects.

Area of Science:

Bioinformatics
Cancer Research
Genomics

Background:

Whole genome profiling advances have spurred cancer research but introduced significant bioinformatics challenges.
Next-generation sequencing (NGS) presents issues in data storage, computational demands, processing, alignment, statistical analysis, and visualization.
MethylCap-seq, an NGS application, enriches methylated DNA for analysis, requiring specialized bioinformatics pipelines.

Purpose of the Study:

To present a scalable and flexible bioinformatics workflow for MethylCap-seq data analysis.
To provide tools for quality control, secondary and tertiary data analysis, and visualization.
To assist biologists in conducting methylation profiling projects and achieving meaningful biological interpretation.

Main Methods:

Related Experiment Videos

Last Updated: May 23, 2026

Methyl-binding DNA capture Sequencing for Patient Tissues

Methyl-binding DNA capture Sequencing for Patient Tissues

Published on: October 31, 2016

Development of a comprehensive bioinformatics workflow tailored for MethylCap-seq data.
Implementation of modules for quality control, secondary analysis (e.g., alignment, variant calling), and tertiary analysis (e.g., differential methylation analysis across groups).
Integration of data visualization tools for enhanced interpretation of methylation patterns.

Main Results:

A robust and adaptable workflow for MethylCap-seq data processing and analysis has been established.
The workflow effectively addresses key bioinformatics challenges associated with NGS methylation data.
Features facilitate efficient quality control, multi-group comparisons, and clear data visualization.

Conclusions:

The presented workflow provides a valuable resource for researchers utilizing MethylCap-seq.
It simplifies complex bioinformatics tasks, enabling deeper biological insights from methylation profiling.
This tool supports the advancement of cancer research through improved DNA methylation data analysis.