Coverage recommendations for methylation analysis by whole-genome bisulfite sequencing
- PMID: 25362363
- PMCID: PMC4344394
- DOI: 10.1038/nmeth.3152
Coverage recommendations for methylation analysis by whole-genome bisulfite sequencing
Abstract
Whole-genome bisulfite sequencing (WGBS) allows genome-wide DNA methylation profiling, but the associated high sequencing costs continue to limit its widespread application. We used several high-coverage reference data sets to experimentally determine minimal sequencing requirements. We present data-derived recommendations for minimum sequencing depth for WGBS libraries, highlight what is gained with increasing coverage and discuss the trade-off between sequencing depth and number of assayed replicates.
Figures
Heatmap showing the pairwise Pearson correlation coefficients (PCC) for genome-wide methylation profiles of the samples used in this study (n=14). Average methylation levels were estimated in 1kb tiling windows.
Distribution of DMR sizes (x-axis) and average methylation difference (y-axis) for DMRs found at 30× comparing hESCs to human cortex (red), CD184 to liver (grey) and CD4 to CD8 (blue) using 2 replicates each. Black dot indicates median and ellipsoids span from the 25th to the 75th percentile in each dimension.
True positive rate (TPR, y-axis) as a function of coverage (x-axis) for hESC vs. cortex (red), CD184 vs. liver (grey) and CD4 vs. CD8 (blue) using 2 replicates for each group. True positive rate is defined as the fraction of high coverage (30×) reference DMRs recovered at the coverage level indicated. Grey box indicates coverage range where change in TPR exhibits the largest drop.
Distribution of DMR sizes (x-axis) and average methylation difference (y-axis) for DMRs discovered at 1× (grey) and additional DMRs discovered when increasing the coverage from 1× to 5× (dark red), 5× to 10× (light red) and 10× to 30× (orange) in the hESCs vs. human cortex comparison using 2 replicates each. Black dots indicate median and ellipsoids span from the 25th to the 75th percentile in each dimension.
False discovery rate (FDR, y-axis) as function of coverage (x-axis) for DMRs exhibiting a methylation difference of 20% or greater when comparing hESCs to human cortex (red), CD184 to liver (grey) or CD4 to CD8 (blue) using two replicates for each group.
True positive rate (TPR, y-axis) as a function of coverage (x-axis) comparing hESCs to human cortex using 1, 2 or 3 replicates per group for DMRs with a methylation difference greater than 20%.
False discovery rate (FDR, y-axis) as a function of coverage (x-axis) comparing hESCs to human cortex using 1, 2 or 3 replicates per group for DMRs with a methylation difference greater than 20%.
Percentage of 3 replicate based, reference DMRs with a methylation difference greater than 20% that are recovered as a function of total coverage used for the entire experiment. Lines indicate whether total experimental coverage is distributed across 1, 2 or 3 replicates
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References
-
- Okano M, Bell DW, Haber DA, Li E. DNA methyltransferases Dnmt3a and Dnmt3b are essential for de novo methylation and mammalian development. Cell. 1999;99:247–257. - PubMed
-
- Bird A. DNA methylation patterns and epigenetic memory. Genes Dev. 2002;16:6–21. - PubMed
-
- Reik W. Stability and flexibility of epigenetic gene regulation in mammalian development. Nature. 2007;447:425–432. - PubMed
-
- Mehta T, Tanik M, Allison DB. Towards sound epistemological foundations of statistical methods for high-dimensional biology. Nat Genet. 2004;36:943–947. - PubMed
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