Computational Methods for Analysis of DNA Methylation Data

DNA methylation is the most common epigenetic modification in eukaryotes. DNA methylation has no effect on gene structure but often plays a critical role in gene expression regulation. In mammals, there is substantial evidence that changes in DNA methylation patterns are associated with developmental disorders, cancer and (un)healthy aging. Importantly, DNA methylation alterations are studied as potential predictive markers for preventive medicine. The broad implications of DNA methylation analysis in improving human health strongly demand for computational approaches to detect and measure DNA methylation alterations. 

While technologies for measuring DNA methylation levels are evolving rapidly, microarrays are the most cost-effective option. Thousands of methylation profiles and raw data generated with microarrays are stored in the Gene Expression Omnibus (GEO) repository. A wide variety of statistical approaches have been developed to remove instrumental variability among DNA methylation data provided by different platforms. The limitations of individual techniques may strongly affect interpretation of DNA methylation data, and thus novel approaches are required to improve quality control as well as downstream analyses of the human methylome. Crucially, relations with other omics data, affecting or depending on the methylation layer must be further explored. Therefore, new methods of DNA methylation analysis are vitally important for practical applications. 

This Research Topic collection invites papers describing new methods of DNA methylation analysis, particularly:

• Data pre-processing (quality control, imputation, harmonization, etc.)


• Differential analysis


• Identification of methylomic human age biomarkers


• Integration with other omics data

Review articles and Perspectives will be considered for publication as well.