Comparison of EM-seq and PBAT methylome library methods for low-input DNA

ABSTRACT

DNA methylation is the most studied epigenetic mark involved in regulation of gene expression. For low input samples, a limited number of methods for quantifying DNA methylation genome-wide has been evaluated. Here, we compared a series of input DNA amounts (1–10ng) from two methylome library preparation protocols, enzymatic methyl-seq (EM-seq) and post-bisulfite adaptor tagging (PBAT) adapted from single-cell PBAT. EM-seq takes advantage of enzymatic activity while PBAT relies on conventional bisulfite conversion for detection of DNA methylation. We found that both methods accurately quantified DNA methylation genome-wide. They produced expected distribution patterns around genomic features, high C-T transition efficiency at non-CpG sites and high correlation between input amounts. However, EM-seq performed better in regard to library and sequencing quality, i.e. EM-seq produced larger insert sizes, higher alignment rates and higher library complexity with lower duplication rate compared to PBAT. Moreover, EM-seq demonstrated higher CpG coverage, better CpG site overlap and higher consistency between input series. In summary, our data suggests that EM-seq overall performed better than PBAT in whole-genome methylation quantification of low input samples.

KEYWORDS:

• Low input DNA
• methylome
• EM-seq
• PBAT

Acknowledgments

Sequencing was performed by the SNP&SEQ Technology Platform which is supported by the Swedish Research Council and the Knut and Alice Wallenberg Foundation. Part of the analysis was conducted using the Swedish National Infrastructure for Computing (SNIC) at UPPMAX, partially funded by the Swedish Research Council through grant. We also thank Mohsen Khademi who handled the samples.

Disclosure statement

The authors declare that they have no competing interests.

Supplementary material

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Additional information

Funding

This work was supported by the European Union’s Horizon 2020 research, innovation programme (grant agreement No 733161) and the European Research Council (ERC, grant agreement No 818170), the Swedish Research Council (VR), the Swedish Brain Foundation, the Swedish MS Foundation and the Stockholm County Council (ALF project).