DNA methylation data by sequencing: experimental approaches and recommendations for tools and pipelines for data...

Area of Science:

• Genomics and Epigenetics
• Computational Biology
• Bioinformatics

Background:

• DNA methylation is a key epigenetic mark regulating gene expression and implicated in diseases like cancer.
• Advancements in sequencing technologies enable genome-wide DNA methylation analysis at single-nucleotide resolution.
• Analysis of large DNA methylation sequencing datasets presents computational challenges for many researchers.

Purpose of the Study:

• To review the fundamental principles and steps in analyzing DNA methylation sequencing data from mammalian genomes.
• To present and discuss prominent computational pipelines for DNA methylation data analysis.
• To guide scientists with limited computational experience in analyzing DNA methylation and hydroxymethylation data.

Main Methods:

• Description of core DNA methylation sequencing analysis steps: read mapping, methylation level calculation, and identification of differentially methylated positions/regions.
• Overview of various sequencing platforms used for DNA methylation detection (e.g., methylated DNA precipitation, whole-genome bisulfite sequencing).
• Discussion of integrated computational pipelines designed to streamline the analysis workflow.

Main Results:

• Identification of several powerful yet user-friendly computational pipelines for DNA methylation data analysis.
• Guidelines and recommendations for selecting appropriate tools and pipelines based on specific research needs.
• Facilitation of reproducible and accessible analysis of epigenomic data.

Conclusions:

• Accessible computational pipelines are crucial for democratizing DNA methylation data analysis.
• This review serves as a starting point for researchers to confidently analyze their own epigenomic datasets.
• Empowering scientists with limited bioinformatics expertise to leverage sequencing data for biological discovery.