Computational Workflow for Genome-Wide DNA Methylation Profiling and Differential Methylation Analysis.

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.