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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...

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Updated: May 17, 2026

Methyl-binding DNA capture Sequencing for Patient Tissues

Methyl-binding DNA capture Sequencing for Patient Tissues

Published on: October 31, 2016

Methods for high-throughput MethylCap-Seq data analysis.

Benjamin A T Rodriguez¹, David Frankhouser, Mark Murphy

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

|November 9, 2012

Summary

This summary is machine-generated.

This study introduces a new bioinformatics workflow for analyzing MethylCap-sequencing (MethylCap-seq) data. The workflow efficiently processes large datasets, enabling comprehensive methylation profiling and biological interpretation for cancer research.

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Last Updated: May 17, 2026

Methyl-binding DNA capture Sequencing for Patient Tissues

Methyl-binding DNA capture Sequencing for Patient Tissues

Published on: October 31, 2016

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq)

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq)

Published on: October 5, 2018

In Vitro Selection of Engineered Transcriptional Repressors for Targeted Epigenetic Silencing

In Vitro Selection of Engineered Transcriptional Repressors for Targeted Epigenetic Silencing

Published on: May 5, 2023

Area of Science:

Bioinformatics
Genomics
Cancer Research

Background:

Whole genome profiling via next-generation sequencing (NGS) presents bioinformatics challenges.
Existing workflows lack efficiency for large MethylCap-seq datasets with multiple sample groups.

Purpose of the Study:

To present a scalable and flexible workflow for MethylCap-seq data analysis.
To address challenges in data storage, processing, alignment, statistical analysis, and visualization.

Main Methods:

The workflow integrates MethylCap-seq experimental quality control (QC).
It includes sequence processing, alignment, differential methylation analysis, hierarchical clustering, and genome-wide pattern assessment.
Data visualization is facilitated using the Anno-J application.

Main Results:

Demonstrated QC procedures with an ovarian cancer dataset, proposing parameters for identifying problematic experiments.
Performed promoter methylation profiling and hierarchical clustering on acute myeloid leukemia (AML) patient groups.
Introduced a Global Methylation Indicator (GMI) for assessing genome-wide methylation changes.

Conclusions:

The developed workflow assists biologists in methylation profiling projects.
It facilitates meaningful biological interpretation of complex genomic data.