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DNA methylome profiling of human tissues identifies global and tissue-specific methylation patterns

Kaie Lokk123, Vijayachitra Modhukur4, Balaji Rajashekar4, Kaspar Märtens4, Reedik Mägi5, Raivo Kolde4, Marina Koltšina1, Torbjörn K Nilsson6, Jaak Vilo4, Andres Salumets789* and Neeme Tõnisson125*

Author Affiliations

1 Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia

2 Department of Genetics, United Laboratories, Tartu University Hospital, Tartu, Estonia

3 Estonian Biocentre, Tartu, Estonia

4 Institute of Computer Science, University of Tartu, Tartu, Estonia

5 Estonian Genome Center, University of Tartu, Tartu, Estonia

6 Department of Medical Biosciences, Clinical Chemistry, Umeå University, Umeå, Sweden

7 Competence Centre on Reproductive Medicine and Biology, Tartu, Estonia

8 Department of Obstetrics and Gynecology, University of Tartu, Tartu, Estonia

9 Institute of Bio- and Translational Medicine, University of Tartu, Tartu, Estonia

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Genome Biology 2014, 15:r54  doi:10.1186/gb-2014-15-4-r54

Published: 1 April 2014

Additional files

Additional file 1:

Methylation validation using Sanger sequencing. For validation of the methylation data from BeadChip, 17 genes were chosen, including unmethylated sites (n = 1), fully methylated sites (n = 2), and genes with tDMRs (n = 14) representing 36 CpG sites altogether. The x-axis shows DNA methylation beta-values obtained from BeadChip, and the y-axis shows beta values from Sanger sequencing.

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Additional file 2:

Global distribution of methylation. The plot represents the methylation distribution of all specimens (70 samples) analyzed, as well as the controls of unmethylated (0%, negative control) and fully methylated (100%, positive control). The global distribution of methylated CpGs shows that most are either unmethylated or fully methylated in somatic tissues.

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Additional file 3:

Description of tDMRs found by the simple linear model method of best fit according to MDL.

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Additional file 4:

Variance in tissues explained by gene regions and CGI regions. (a) The figure is showing the distributions of the R squared statistic, which describes the variance explained by different gene regions and intergenic area. It is clear that gene body and intergenic areas are more variable than gene promoter areas. (b) Distribution of R squared statistic describes the variance explained by CpG island shores, shelves, and non-island regions. Figure shows, that CpG islands are the least variable among these groups.

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Additional file 5:

GO analysis of hypermethylated tDMRs in specific tissue types.

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Additional file 6:

Hierarchical clustering of all the samples studied. Hierarchical clustering of all the samples studied shows that the similarity between different tissues was much higher than between individuals, as tissues are mostly clustering together.

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Additional file 7:

Gene expression datasets used for correlation analyses.

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Additional file 8:

Correlation analysis of tDMRs and gene expression for methylations in the CGI-promoter and gene body regions. (a) tDMR genes with low expression show high levels of methylation at CGI-promoter. (b) Gene body methylation in tDMRs is not correlated with gene expression. (a, b) The x-axis shows DNA methylation beta values, and the y-axis shows gene expression values. The different tissues studied are represented by the following symbols: aorta (•), coronary artery (●), bladder (), bone and joint cartilage (), bone marrow (), lymph node (), medulla oblongata (+), and tonsils (×).

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Additional file 9:

PCR primers used in the methylation validation analysis.

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Additional file 10:

Correlations between consecutive probes. Figure shows the correlation between methylation beta values of consecutive probes and how it depends on the distance between these probes.

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