An integrative analysis of DNA methylation and RNA-Seq data for human heart, kidney and liver
Open accessAbstract
Background: Many groups, including our own, have proposed the use of DNA methylation profiles as biomarkers for various disease states. While much research has been done identifying DNA methylation signatures in cancer vs. normal etc., we still lack sufficient knowledge of the role that differential methylation plays during normal cellular differentiation and tissue specification. We also need thorough, genome level studies to determine the meaning of methylation of individual CpG dinucleotides in terms of gene expression. Results: In this study, we compiled unique DNA methylation signatures from normal human heart, lung, and kidney using the Illumina Infinium 27K methylation arrays and compared those to gene expression by RNA sequencing. We have identified unique signatures of global DNA methylation for human heart, kidney and liver, and showed that DNA methylation data can be used to correctly classify various tissues. It indicates that DNA methylation reflects tissue specificity and may play an important role in tissue differentiation. The integrative analysis of methylation and RNA-Seq data showed that gene methylation and its transcriptional levels were comprehensively correlated. The location of methylation markers in terms of distance to transcription start site and CpG island showed no effects on the regulation of gene expression by DNA methylation in normal tissues. Conclusions: This study showed that an integrative analysis of methylation array and RNA-Seq data can be utilized to discover the global regulation of gene expression by DNA methylation and suggests that DNA methylation plays an important role in normal tissue differentiation via modulation of gene expression.
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Pairwise DNA-methylation and RNA-seq integration has mostly consisted of the analysis of the correlation between DEGs and methylation patterns.
“Pairwise DNA-methylation and RNA-seq integration, for the most part, has consisted of the analysis of the correlation between DEGs and methylation patterns [138–140].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [140]
Statistically significant correlations observed between DNA methylation and gene expression accounted for relatively small effects.
“The statistically significant correlations that were observed, however, accounted for relatively small effects.”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [140]
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- Authors
- Xie, L., Weichel, B., Ohm, J. E., & Zhang, K.
- Affiliations
- University of North Dakota (US)
- Corresponding author
- Ohm, Joyce Ellen
- Zhang, Ke
- Venue
- BMC Systems Biology
- Published
- 2011-12
- Volume
- 5
- Issue
- S3
- Pages
- S4-S4
- ISSN
- 1752-0509 (ISSN-L)
- PMC
- PMC3287572
- OpenAlex
- W2165685453
- Cited by
- 38
- References
- 38
- Citations by year
- 2025: 1 · 2023: 1 · 2022: 1 · 2021: 3 · 2019: 2 · 2018: 4 · 2017: 4 · 2016: 5 · 2015: 1 · 2014: 5 · 2013: 10
- FWCI
- 2.08
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- BMC Systems Biology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Institutes of Health
- National Center for Research Resources (P20RR016471)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- DNA methylation, gene expression, integrative analysis, histogenesis, heart, kidney, liver, lung, CpG islands, transcription start sites, gene expression regulation, biomarkers
- MeSH terms
- Animals, Gene Expression Regulation, Humans, Kidney, Liver, Myocardium, Organ Specificity, Sequence Analysis, RNA, DNA Methylation, Computational Biology
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