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Updated: Aug 6, 2025
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Targeted DNA Methylation Analysis by Next-generation Sequencing
Published on: February 24, 2015
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Performance comparisons of methylation and structural variants from low-input whole-genome methylation sequencing
Zhifu Sun1, Saurabh Behati1, Panwen Wang1
1Division of Computational Biology, Mayo Clinic, Rochester, MN 55905, USA.
Epigenomics
|March 15, 2023
Summary
EM-sequencing effectively captures DNA methylation, single nucleotide variants (SNVs), and copy number variants (CNVs) from low-input DNA. This method proves reliable for comprehensive genomic analysis in a single sequencing run.
Area of Science:
- Genomics and Molecular Biology
- Epigenetics and DNA Methylation Analysis
- Next-Generation Sequencing (NGS) Technologies
Background:
- Whole-genome sequencing offers simultaneous DNA methylation and structural variant data (SNVs, CNVs).
- Limited data exists on the reliability of obtaining this comprehensive information from low-input DNA using diverse library preparation and sequencing protocols.
Purpose of the Study:
- To evaluate the reliability of obtaining simultaneous DNA methylation, SNV, and CNV information from low-input DNA.
- To compare the performance of different library preparation and sequencing protocols for comprehensive genomic analysis.
Main Methods:
- A HapMap NA12878 sample was utilized for low-input DNA sequencing (10-25 ng).
- Three distinct library preparation and sequencing protocols were compared: EM-sequencing, QIA-sequencing, and Swift-sequencing.
- Performance was assessed based on CpG methylation measurement, SNV detection, and CNV detection.
Main Results:
- EM-sequencing demonstrated superior performance across most metrics, particularly in CpG methylation and SNV detection.
- All tested protocols exhibited similar performance in copy number variant (CNV) detection.
- EM-sequencing successfully captured the highest number of CpGs and true SNVs at low DNA input levels.
Conclusions:
- EM-sequencing is a suitable method for simultaneously detecting DNA methylation, SNVs, and CNVs from low-input DNA.
- This approach enables comprehensive genomic profiling from a single sequencing experiment, enhancing efficiency and data yield.
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