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Project Introduction

This this the project website for Blake, Roux, et al., “A comparison of gene expression and DNA methylation patterns across tissues and species”. Many of the main conclusions from each section are quoted directly from the publication. A link to the Github repo can be found here, although the code is much more readable using the links below.

Sample Information and Record of Technical Variables

• Technical variables documented/tested (sample information, RNA-seq, BS-seq, QC, processing, etc.).
  Note: Clicking on this link will lead to the automatic download of a .docx file. For more information about the samples, see Supplemental_Table_S1.xls.
• Main conclusion from this section: All sample processing steps should be recorded in a sample history file that includes anything that happened to any sample. This documentation can help provide evidence that a phenomenon is driven by biological rather than technical factors.

RNA-seq Data

Methylation Data

• Identifying variables related to methylation processing that are potentially confounded with biological variables of interest.
• Identifying genes for which promoters are orthologous across humans and chimpanzees.
• Identifying genes for which promoters are orthologous across humans, chimpanzees, and rhesus macaques.
• Conclusion from this section: I identified 7,725 orthologous genes with expression data and corresponding promoter DNA methylation data in humans and chimpanzees, and 4155 orthologous genes with the same information for all three species.

Differentially methylated regions (DMRs)

Identifying DMRs was computationally intensive using the BSmooth package. Therefore, tissue and species DMRs were identified using the orthologous CpGs on a HPC. The output of each file was either (1) a list of sDMRs where the same tissue was analyzed across species or (2) a list of tDMRs where different tissues were analyzed within a species.

• Obtain statistics about the sizes of DMRs.
• Defining DMRs based on the base pair (bp) overlap.
• Identifying human-specific and conserved DMRs using the package bedr.
• Identifying DMRs with a pattern consistent with tissue specificity. Again, the methylation pattern in 1 tissue is being compared to methylation in only 3 other tissues.
• Enrichment of tDMR overlap using the hypergeometric distribution.
• Overlap between DMRs and H3K27ac, an epigenetic mark often associated with active gene expression.
• Conclusions from this section: Inter-tissue DNA methylation differences within a species tend to be conserved, consistent with the observations of previous studies. In all four tissues, >59% of conserved DMRs are hypomethylated in a tissue-specific manner. We evaluated the overlap between tissue-specific DMRs and genomic regions marked with H3K27ac, a mark often associated with active gene expression (The ENCODE Project Consortium 2012). We found that conserved hypomethylated tissue-specific DMRs were annotated with H3K27ac more frequently than tissue-specific DMRs identified only in humans (P < 0.001, difference of proportions test).

Integration of gene expression levels and methylation levels- interspecies DE genes

What percentage of interspecies gene expression level differences can be explained by variation in DNA methylation levels?

• In humans and chimpanzees with 7125 orthologous genes.
• In human and chimpanzees with 4155 human-chimp-rhesus orthologous genes.
• In humans and rhesus macaques with 4155 orthologous genes.
• Conclusion from this section: When considering DE genes between humans and chimpanzees (in at least one tissue), I found that between 11% and 25% of genes (depending on tissue) showed a difference in the effect of species on gene expression level.

Integration of gene expression levels and methylation levels- intertissue DE genes

Within a species, what percentage of intertissue gene expression level differences can be explained by variation in DNA methylation levels?

• Intertissue in humans.
• Intertissue in chimpanzees.
• Intertissue in rhesus macaques.
• In tissue specific DE genes.
• Conclusion from this section: 9%–17% of inter-tissue gene expression differences could potentially be explained by DNA methylation differences across tissues (FSR 5%).

Integration of gene expression levels and methylation levels- conserved versus non-conserved intertissue DE genes

What percentage of conserved intertissue gene expression level differences can be explained by variation in DNA methylation levels? How does this percentage compare to the percentage of non-conserved intertissue gene expression level differences that can be explained by variation in DNA methylation levels?

• Conserved versus nonconserved DE genes- humans and chimpanzees.
• Conserved versus nonconserved DE genes- humans and rhesus macaques.
• Conclusion from this section: When focusing on regulatory patterns that are most likely to be functional—namely, conserved inter-tissue gene regulatory differences, these differences were more likely to be explained by variation in DNA methylation levels than nonconserved inter-tissue gene expression differences (minimum difference is 7%, P < 0.005 for all comparisons; at FDR <5% and FSR <5%).

Additional figure/table generation

• Correlations of gene expression levels in tissues from matched versus unmatched individuals.
• Figure 1.
• Figure 3.
• Figure 4.
• Figure 5.
• Figures S7-9.
• Figure S10.
• Figure S11.

Revisions

Many thanks to the editor and 3 reviewers who provided helpful comments on this manuscript.

• Additional H3K27ac analysis, including GO enrichment.
• Comparing gene expression levels with reads mapped using the HG19 versus HG38 genome builds.
• Network analysis.
• Obtain the number of genes per tissue.
• Conclusion from this section: I found that genes with conserved tissue-specific expression patterns are more likely to appear as nodes in the networks than genes without tissue-specific expression patterns or genes whose tissue-specific expression patterns are not conserved (P < 10−5).