The progress on the estimation of DNA methylation level and the detection of abnormal methylation

Abstract

Background: DNA methylation is a key heritable epigenetic modification that plays a crucial role in transcriptional regulation and therefore a broad range of biological processes. The complex patterns of DNA methylation highlight the significance of the profiling the DNA methylation landscape.

Results: In this review, the main high-throughput detection technologies are summarized, and then the three trends of computational estimation of DNA methylation levels were analyzed, especially the expanding of the methylation data with lower coverage. Furthermore, the detection methods of differential methylation patterns for sequencing and array data were presented.

Conclusions: More and more research indicated the great importance of DNA methylation changes across different diseases, such as cancers. Although a lot of enormous progress has been made in understanding the role of DNA methylation, only few methylated genes or functional elements serve as clinically relevant cancer biomarkers. The bottleneck in DNA methylation advances has shifted from data generation to data analysis. Therefore, it is meaningful to develop machine learning models for computational estimation of methylation profiling and identify the potential biomarkers.

Author summary

With the development of experimental profiling approach for both pooled and single cells, the research on computational methods for methylome analysis is also a hot field for the understanding of epigenomic code. The computational estimation of DNA methylation levels, especially, the expanding methods for methylome data with lower coverage was intensively analyzed. With the broader range of DNA methylation landscapes both in coverage and sample size, it provides better opportunity for the identification of the potential biomarkers.

Graphical abstract

Keywords

DNA methylation / genome-wide profiling / computational estimation / single-cell methylome / differential methylation detection

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ACKNOWLEDGEMENTS

We thank Prof. Wei Wang at UCSD for insightful comments during manuscript preparation. This work was supported by the National Natural Science Foundation of China (No. 61872063) and Shenzhen Science and Technology Program (No. JCYJ20210324140407021).

COMPLIANCE WITH ETHICS GUIDELINES

The authors Shicai Fan, Likun Wang, Liang Liang, Xiaohong Cao, Jianxiong Tang and Qi Tian declare that they have no conflict of interests.

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