The SEQC2 epigenomics quality control (EpiQC) study

Abstract

Background 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. Results 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. Conclusions 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.

Ultra high-throughput whole-genome methylation sequencing reveals trajectories in precancerous polyps to early colorectal adenocarcinoma

“…To compare WGMS data generated by the UG sequencer to WGMS methods we sequenced the standard reference HG001, HG002 and HG005 cell-lines using both EM-seq 25 and Bisulfite 26 conversion methods and compared our data with the recently published Genome in a Bottle (GIAB) EpiQC data 27 . Using the UG sequencer we sequenced an average of 922 million reads per sample for each of the methods with two to five replicates (Supplementary table 1).…”

“…To gain insight on the tissue composition of these samples we applied a computational method for methylation-based cell type deconvolution 27 (methods; Supplementary Fig 3c). This analysis revealed that three of the adenocarcinoma samples had a negligible colon fraction and one of them contained a high fraction of lung cells.…”

“…Prediction of cell type composition in our FAP and CRC data was done using the meth_atlas package (https://github.com/nloyfer/meth_atlas) 27 . To this end, WGMS data was reduced to 450 k array format.…”

DNA 5-methylcytosine detection and methylation phasing using PacBio circular consensus sequencing

“…In humans, most 5mCs occur at CpG sites, which are associated with cancer 2 , embryonic development 3 and aging 4 . Bisulfite sequencing (BS-seq) is now the most widely used methodology for profiling 5mC methylation 5,6 . In a bisulfite treated genomic DNA, unmethylated cytosines are converted to uracils, while methylated cytosines are unchanged 7 .…”

Author Correction: The SEQC2 epigenomics quality control (EpiQC) study

“…Following publication of the original article [ 1 ], the authors identified the following errors: Jonathan Foox, Jessica Nordlund, Claudia Lalancette, Ting Gong, Michelle Lacey and Samantha Lent are co-first authors. Affiliation 21 was incorrectly published.…”