Comparison of sequencing-based methods to profile DNA methylation and identification of monoallelic epigenetic modifications

By R. A. Harris, Ting Wang, Cristian Coarfa, R. Nagarajan, C. Hong, L. Sara, Downey, Brett E. Johnson, S. Fouse, Allen D. Delaney and 34 more. Nature Biotechnology, 2010

719 citations recorded by the source corpus.

Analysis of DNA methylation patterns relies increasingly on sequencing-based profiling methods. The four most frequently used sequencing-based technologies are the bisulfite-based methods MethylC-seq and reduced representation bisulfite sequencing (RRBS), and the enrichment-based techniques methylated DNA immunoprecipitation sequencing (MeDIP-seq) and methylated DNA binding domain sequencing (MBD-seq). We applied all four methods to biological replicates of human embryonic stem cells to assess their genome-wide CpG coverage, resolution, cost, concordance and the influence of CpG density and genomic context. The methylation levels assessed by the two bisulfite methods were concordant (their difference did not exceed a given threshold) for 82% for CpGs and 99% of the non-CpG cytosines. Using binary methylation calls, the two enrichment methods were 99% concordant and regions assessed by all four methods were 97% concordant. We combined MeDIP-seq with methylation-sensitive restriction enzyme (MRE-seq) sequencing for comprehensive methylome coverage at lower cost. This, along with RNA-seq and ChIP-seq of the ES cells enabled us to detect regions with allele-specific epigenetic states, identifying most known imprinted regions and new loci with monoallelic epigenetic marks and monoallelic expression.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/14ff95d7ff8fe842d40e08d7d160d3bae4cb8286

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Citations in the corpus

From RNA-seq reads to differential expression results (2010, background): “Similarly, combinations of sequencing-based datasets are beginning to provide insights into the mono-allelic associations between expression, histone modifications and DNA methylation [74].”
A unique regulatory phase of DNA methylation in the early mammalian embryo (2012, methodology): “Here, we use new genomic high-resolution methylation profiling strategies 23,24 to gain insight into the underlying mechanisms and regulatory principles of DNA methylation as it functions in early mammalian development.”
A Bayesian hierarchical model to detect differentially methylated loci from single nucleotide resolution sequencing data (2014, methodology): “Current sequencing-based methods for methylation analysis can be classified into two categories: enrichment- (10) and bisulfite-conversion-based methods (11).”
DNA methylome analysis using short bisulfite sequencing data (2012, methodology): “This problem only affects the 3 ′ end of reads when the read length is longer than the fragment to be sequenced (RRBS is probably more affected as fragments are usually size-selected to be 40 to 220 base pairs 24,33 ).”
Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications (2011, neutral): “Its output discriminates between cytosines in CpG, CHG and CHH context and enables bench scientists to visualize and interpret their methylation data soon after the sequencing run is completed.”
Epigenetic Determinants of Cancer. (2016, methodology): “…sequences by either methylcytosine antibodies, antibodies recognizing methylcytosine-binding proteins or their binding domains or sequences generated by methylation-sensitive restriction enzymes, and then identifying these via next-generation sequencing (Harris et al. 2010; Aryee et al. 2013).”