Methods in DNA methylation array dataset analysis: A review

Abstract

Understanding the intricate relationships between gene expression levels and epigenetic modifications in a genome is crucial to comprehending the pathogenic mechanisms of many diseases. With the advancement of DNA Methylome Profiling techniques, the emphasis on identifying Differentially Methylated Regions (DMRs/DMGs) has become crucial for biomarker discovery, offering new insights into the etiology of illnesses. This review surveys the current state of computational tools/algorithms for the analysis of microarray-based DNA methylation profiling datasets, focusing on key concepts underlying the diagnostic/prognostic CpG site extraction. It addresses methodological frameworks, algorithms, and pipelines employed by various authors, serving as a roadmap to address challenges and understand changing trends in the methodologies for analyzing array-based DNA methylation profiling datasets derived from diseased genomes. Additionally, it highlights the importance of integrating gene expression and methylation datasets for accurate biomarker identification, explores prognostic prediction models, and discusses molecular subtyping for disease classification. The review also emphasizes the contributions of machine learning, neural networks, and data mining to enhance diagnostic workflow development, thereby improving accuracy, precision, and robustness.

Keywords: Biomarker identification; Clustering; DMR analysis; Methylation segmentation; Molecular subtyping; Prognostic models.

Graphical abstract

Fig. 1

Visual representation of the step-wise analysis of methylation data with its significance and majorly used algorithms in the reviewed manuscripts. Depending upon the researcher objective, some manuscript demonstrates the application of all the steps within the single paper, while others showcase the application of a few selective steps.

Fig. 2

General concept showing the flow of DNA methylation profiling data from experimental methods to data repositories and providence of DMR analysis algorithms. .

Fig. 3

Figurative approach of diseases diagnosed by different effective algorithms of some related human diseases.