
A Practical Guide to the Measurement and Analysis of DNA Methylation
Author(s) -
Benjamin D. Singer
Publication year - 2019
Publication title -
american journal of respiratory cell and molecular biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.469
H-Index - 161
eISSN - 1535-4989
pISSN - 1044-1549
DOI - 10.1165/rcmb.2019-0150tr
Subject(s) - dna methylation , bisulfite sequencing , bisulfite , illumina methylation assay , biology , computational biology , epigenetics , methylated dna immunoprecipitation , methylation , epigenomics , dna , genetics , gene , gene expression
DNA methylation represents a fundamental epigenetic mark that is associated with transcriptional repression during development, maintenance of homeostasis, and disease. In addition to methylation-sensitive PCR and targeted deep-amplicon bisulfite sequencing to measure DNA methylation at defined genomic loci, numerous unsupervised techniques exist to quantify DNA methylation on a genome-wide scale, including affinity enrichment strategies and methods involving bisulfite conversion. Both affinity-enriched and bisulfite-converted DNA can serve as input material for array hybridization or sequencing using next-generation technologies. In this practical guide to the measurement and analysis of DNA methylation, the goal is to convey basic concepts in DNA methylation biology and explore genome-scale bisulfite sequencing as the current gold standard for assessment of DNA methylation. Bisulfite conversion chemistry and library preparation are discussed in addition to a bioinformatics approach to quality assessment, trimming, alignment, and methylation calling of individual cytosine residues. Bisulfite-converted DNA presents challenges for standard next-generation sequencing library preparation protocols and data-processing pipelines, but these challenges can be met with elegant solutions that leverage the power of high-performance computing systems. Quantification of DNA methylation, data visualization, statistical approaches to compare DNA methylation between sample groups, and examples of integrating DNA methylation data with other -omics data sets are also discussed. The reader is encouraged to use this article as a foundation to pursue advanced topics in DNA methylation measurement and data analysis, particularly the application of bioinformatics and computational biology principles to generate a deeper understanding of mechanisms linking DNA methylation to cellular function.