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Related Experiment Video

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Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer
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Computational Workflow for Genome-Wide DNA Methylation Profiling and Differential Methylation Analysis.

Pei-Yu Lin1, Guan-Jun Lin1,2, Kuan-Lin Chen1

  • 1Institute of Plant and Microbial Biology, Academia Sinica, Taipei, Taiwan.

Bio-Protocol
|November 13, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a streamlined bioinformatics pipeline for analyzing genome-wide DNA methylation using bisulfite sequencing (BS-seq) and enzymatic methyl sequencing (EM-seq). The workflow enhances reproducibility and flexibility for methylome studies in plants and animals.

Keywords:
BS-seqBioinformaticsBioinformatics pipelineBisulfite sequencingCGmapDMRDNA methylationEM-seqNGS

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Area of Science:

  • Epigenetics and Genomics
  • Bioinformatics and Computational Biology

Background:

  • DNA methylation is a key epigenetic regulator of gene expression.
  • High-throughput sequencing methods like BS-seq and EM-seq provide single-base resolution for methylome profiling.
  • Existing bioinformatics workflows can be complex and lack integration.

Purpose of the Study:

  • To present a comprehensive, user-friendly bioinformatics pipeline for analyzing genome-wide DNA methylation data.
  • To integrate essential analysis steps from raw read quality control to differentially methylated region identification and visualization.
  • To offer a flexible and reproducible workflow adaptable to diverse methylome studies.

Main Methods:

  • Quality control of raw sequencing reads (FASTQ).
  • Read alignment using Bowtie2 and BS-Seeker2.
  • DNA methylation calling to generate CGmap files.
  • Identification of differentially methylated regions (DMRs) using MethylC-analyzer and HOME.
  • Data visualization and post-alignment analyses.

Main Results:

  • A unified bioinformatics pipeline integrating multiple analysis steps.
  • Demonstrated application in an *Arabidopsis thaliana* mutant (*met1*) showing global CG hypomethylation.
  • Successful identification of DMRs and altered gene regulation.

Conclusions:

  • The developed pipeline simplifies and standardizes genome-wide DNA methylation analysis.
  • It provides a robust framework for uncovering methylation-associated regulatory mechanisms in various organisms.
  • The workflow enhances reproducibility and lowers the technical barrier for methylome research.