Whole-genome bisulfite sequencing

Epigenetics has developed into a rapidly growing and technologically diverse discipline over the last decades. In addition, the introduction of massively parallel next-generation sequencing (NGS) in the early 2000s has dramatically increased the accessibility and efficiency of the method, which has spurred the development of bisulfite-based technologies. Advances in bisulfite sequencing have expanded methylation research from selected regions to whole genome bisulfite sequencing (WGBS) (Stevens M et al. 2013). More recently, novel sequencing protocols such as oxidative bisulfite conversion (TruMethyl oxBS), enzyme-based conversion (EM-seq) and target-enrichment-based bisulfite conversion (Illumina EPIC Capture) have further advanced methylation research (Gong T et al. 2022).

Whole-genome bisulfite sequencing (WGBS) consists of three steps: library preparation, sequencing and alignment, and quality control. The basic step is library preparation, where the bisulfite conversion reaction from unmethylated cytosine to uracil takes place. The treated DNA is then sequenced using an NGS platform to generate numerous short reads. The final step involves processing the raw reads using various bioinformatics methods to remove poor quality data and downstream analysis to explore the biological processes. This method currently represents the gold standard for the assessment of DNA methylation.

DMRs: Differential methylation region detection (DMR detection) is one of the most important methylation analyses in practice and involves the analysis of genomic regions in multiple samples. The most common application is to find abnormal methylation regions between pathological samples and normal samples, which can serve as biomarkers or provide information about the biology of the disease. Implementation approaches based on the DMR statistical framework vary between different software programs. software programs.

For example, BSmooth uses a local probability smoothing approach to identify DMRs in a sample-specific methylation determination (Hansen KD et al. 2012). It applies the Welch t-test, an adaptation of the Student t-test to compare multiple samples.

1. Doskocil J et al. (1962) Distribution of 5-methylcytosine in pyrimidine sequences of deoxyribonucleic acids. Biochim Biophys Acta 55:953-959.
2. Ehrlich M et al. (1981) 5-Methylcytosine in eukaryotic DANN. Science 212:1350-1357.
3. Gibson F et al. (2022) Epigenetic Dysregulation in Autoimmune and Inflammatory Skin Diseases. Clin Rev Allergy Immunol 63:447-471.
4. Gong T et al. (2022) Analysis and Performance Assessment of the Whole Genome Bisulfite Sequencing Data Workflow: Currently Available Tools and a Practical Guide to Advance DNA Methylation Studies. Small Methods 6:e2101251.
5. Greenberg MVC et al. (2019) The diverse roles of DNA methylation in mammalian development and disease, Nat Rev Mol Cell Biol 20:590-607.
6. Grönniger E et al. (2024) Skin Rejuvenation by Modulation of DNA Methylation. Exp Dermatol 33:e70005.
7. Hansen KD et al. (2012) BSmooth: from whole genome bisulfite sequencing reads to differentially methylated regions, Genome Biol 13:R83.
8. Mervis JS et al. (2020) DNA methylation and inflammatory skin diseases. Arch Dermatol Res 312:461-466.
9. Stevens M et al. (2013) Estimating absolute methylation levels at single-CpG resolution from methylation enrichment and restriction enzyme sequencing methods, Genome Res 23:1541-1553.
10. Younesian S et al. (2022) The DNA Methylation in Neurological Diseases. Cells 11:3439.