Epigenomics

The Enzymatic Methyl-seq kit (EM-seq) for Illumina contains all the components needed tomake libraries that are enzymatically modified to detect 5-methylcytosines (5mC) and 5-hydroxymethylcytosines (5hmC).

Bisulfite sequencing detects 5mC and 5hmC at single-base resolution. However, bisulfite treatment damages DNA, which results in fragmentation, DNA loss, and biased sequencing data. To overcome these problems, enzymatic methyl-seq (EM-seq) was developed. This method detects 5mC and 5hmC using two sets of enzymatic reactions. In the first reaction, TET2 and T4-BGT convert 5mC and 5hmC into products that cannot be deaminated by APOBEC3A. In the second reaction, APOBEC3A deaminates unmodified cytosines by converting them to uracils. Therefore, these three enzymes enable the identification of 5mC and 5hmC. EM-seq libraries were compared with bisulfite-converted DNA, and each library type was ligated to Illumina adaptors before conversion. Libraries were made using NA12878 genomic DNA, cell-free DNA, and FFPE DNA over a range of DNA inputs. The 5mC and 5hmC detected in EM-seq libraries were similar to those of bisulfite libraries. However, libraries made using EM-seq outperformed bisulfite-converted libraries in all specific measures examined (coverage, duplication, sensitivity, etc.). EM-seq libraries displayed even GC distribution, better correlations across DNA inputs, increased numbers of CpGs within genomic features, and accuracy of cytosine methylation calls. EM-seq was effective using as little as 100 pg of DNA, and these libraries maintained the described advantages over bisulfite sequencing. EM-seq library construction, using challenging samples and lower DNA inputs, opens new avenues for research and clinical applications.

The NEBNext Enzymatic Methyl-seq library preparation method is a powerful tool for studying genome-wide DNA methylation patterns. EM-seq utilizes an enzymatic approach to efficiently convert unmethylated cytosines to uracils, allowing for interrogation of DNA methylation across the genome at single-base resolution. NEBNext Enzymatic Methyl-seq has been shown to provide high sensitivity and accuracy for profiling DNA methylation patterns across different genomic context. Furthermore, the method offers a streamlined and automated, workflow enabling the generation of high-quality libraries for next-generation sequencing.

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.

The powerful HiSeq X sequencers with their patterned flowcell technology and fast turnaround times are instrumental for many large-scale genomic and epigenomic studies. However, assessment of DNA methylation by sodium bisulfite treatment results in sequencing libraries of low diversity, which may impact data quality and yield. In this report we assess the quality of WGBS data generated on the HiSeq X system in comparison with data generated on the HiSeq 2500 system and the newly released NovaSeq system. We report a systematic issue with low basecall quality scores assigned to guanines in the second read of WGBS when using certain Real Time Analysis (RTA) software versions on the HiSeq X sequencer, reminiscent of an issue that was previously reported with certain HiSeq 2500 software versions. However, with the HD.3.4.0 /RTA 2.7.7 software upgrade for the HiSeq X system, we observed an overall improved quality and yield of the WGBS data generated, which in turn empowers cost-effective and high quality DNA methylation studies.

Cytosine modifications in DNA such as 5-methylcytosine (5mC) underlie a broad range of developmental processes, maintain cellular lineage specification, and can define or stratify types of cancer and other diseases. However, the wide variety of approaches available to interrogate these modifications has created a need for harmonized materials, methods, and rigorous benchmarking to improve genome-wide methylome sequencing applications in clinical and basic research. Here, we present a multi-platform assessment and cross-validated resource for epigenetics research from the FDA’s Epigenomics Quality Control Group. Each sample is processed in multiple replicates by three whole-genome bisulfite sequencing (WGBS) protocols (TruSeq DNA methylation, Accel-NGS MethylSeq, and SPLAT), oxidative bisulfite sequencing (TrueMethyl), enzymatic deamination method (EMSeq), targeted methylation sequencing (Illumina Methyl Capture EPIC), single-molecule long-read nanopore sequencing from Oxford Nanopore Technologies, and 850k Illumina methylation arrays. After rigorous quality assessment and comparison to Illumina EPIC methylation microarrays and testing on a range of algorithms (Bismark, BitmapperBS, bwa-meth, and BitMapperBS), we find overall high concordance between assays, but also differences in efficiency of read mapping, CpG capture, coverage, and platform performance, and variable performance across 26 microarray normalization algorithms. The data provided herein can guide the use of these DNA reference materials in epigenomics research, as well as provide best practices for experimental design in future studies. By leveraging seven human cell lines that are designated as publicly available reference materials, these data can be used as a baseline to advance epigenomics research. This article serves as recomendations for library preparation, sequencing, and data processing of methylome sequencing data.

With rapidly dropping sequencing cost, the popularity of whole-genome DNA methylation sequencing has been on the rise. Multiple library preparation protocols currently exist. We have performed 22 whole-genome DNA methylation sequencing experiments on snap frozen human samples, and extensively benchmarked common library preparation protocols for whole-genome DNA methylation sequencing, including three traditional bisulfite-based protocols and a new enzyme-based protocol. In addition, different input DNA quantities were compared for two kits compatible with a reduced starting quantity. In addition, we also present bioinformatic analysis pipelines for sequencing data from each of these library types. An assortment of metrics were collected for each kit, including raw read statistics, library quality and uniformity metrics, cytosine retention, and CpG beta value consistency between technical replicates. Overall, the NEBNext Enzymatic Methyl-seq and Swift Accel-NGS Methyl-Seq kits performed quantitatively better than the other two protocols. In addition, the NEB and Swift kits performed well at low-input amounts, validating their utility in applications where DNA is the limiting factor. The NEBNext Enzymatic Methyl-seq kit appeared to be the best option for whole-genome DNA methylation sequencing of high-quality DNA, closely followed by the Swift kit, which potentially works better for degraded samples. Further, a general bioinformatic pipeline is applicable across the four protocols, with the exception of extra trimming needed for the Swift Biosciences’s Accel-NGS Methyl-Seq protocol to remove the Adaptase sequence.