NDLI: Estimating genome-wide DNA methylation heterogeneity with methylation patterns

Content Provider	Springer Nature : BioMed Central
Author	Lin, Pei-Yu Chang, Ya-Ting Huang, Yu-Chun Chen, Pao-Yang
Abstract	Background In a heterogeneous population of cells, individual cells can behave differently and respond variably to the environment. This cellular diversity can be assessed by measuring DNA methylation patterns. The loci with variable methylation patterns are informative of cellular heterogeneity and may serve as biomarkers of diseases and developmental progression. Cell-to-cell methylation heterogeneity can be evaluated through single-cell methylomes or computational techniques for pooled cells. However, the feasibility and performance of these approaches to precisely estimate methylation heterogeneity require further assessment. Results Here, we proposed model-based methods adopted from a mathematical framework originally from biodiversity, to estimate genome-wide DNA methylation heterogeneity. We evaluated the performance of our models and the existing methods with feature comparison, and tested on both synthetic datasets and real data. Overall, our methods have demonstrated advantages over others because of their better correlation with the actual heterogeneity. We also demonstrated that methylation heterogeneity offers an additional layer of biological information distinct from the conventional methylation level. In the case studies, we showed that distinct profiles of methylation heterogeneity in CG and non-CG methylation can predict the regulatory roles between genomic elements in Arabidopsis. This opens up a new direction for plant epigenomics. Finally, we demonstrated that our score might be able to identify loci in human cancer samples as putative biomarkers for early cancer detection. Conclusions We adopted the mathematical framework from biodiversity into three model-based methods for analyzing genome-wide DNA methylation heterogeneity to monitor cellular heterogeneity. Our methods, namely MeH, have been implemented, evaluated with existing methods, and are open to the research community.
Related Links	https://epigeneticsandchromatin.biomedcentral.com/counter/pdf/10.1186/s13072-023-00521-7.pdf
Ending Page	16
Page Count	16
Starting Page	1
File Format	HTM / HTML
ISSN	17568935
DOI	10.1186/s13072-023-00521-7
Journal	Epigenetics & Chromatin
Issue Number	1
Volume Number	16
Language	English
Publisher	BioMed Central
Publisher Date	2023-11-09
Access Restriction	Open
Subject Keyword	Animal Genetics and Genomics Human Genetics Plant Genetics and Genomics Cell Biology Gene Expression Gene Function DNA Methylation pattern Methylation heterogeneity, DNA methylation Mathematical modelling Bisulfite sequencing Enzymatic methyl sequencing Next Generation Sequencing Epigenetics
Content Type	Text
Resource Type	Article
Subject	Genetics Molecular Biology
Journal Impact Factor	4.2/2023
5-Year Journal Impact Factor	4.9/2023

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