Reduced representation bisulfite sequencing for comparative high-resolution DNA methylation analysis

By Alexander Meissner, Andreas Gnirke, George W. Bell, B. Ramsahoye, Eric S. Lander, R. Jaenisch. Nucleic Acids Research, 2005

1,167 citations recorded by the source corpus.

We describe a large-scale random approach termed reduced representation bisulfite sequencing (RRBS) for analyzing and comparing genomic methylation patterns. BglII restriction fragments were size-selected to 500–600 bp, equipped with adapters, treated with bisulfite, PCR amplified, cloned and sequenced. We constructed RRBS libraries from murine ES cells and from ES cells lacking DNA methyltransferases Dnmt3a and 3b and with knocked-down (kd) levels of Dnmt1 (Dnmt[1kd,3a−/−,3b−/−]). Sequencing of 960 RRBS clones from Dnmt[1kd,3a−/−,3b−/−] cells generated 343 kb of non-redundant bisulfite sequence covering 66212 cytosines in the genome. All but 38 cytosines had been converted to uracil indicating a conversion rate of >99.9%. Of the remaining cytosines 35 were found in CpG and 3 in CpT dinucleotides. Non-CpG methylation was >250-fold reduced compared with wild-type ES cells, consistent with a role for Dnmt3a and/or Dnmt3b in CpA and CpT methylation. Closer inspection revealed neither a consensus sequence around the methylated sites nor evidence for clustering of residual methylation in the genome. Our findings indicate random loss rather than specific maintenance of methylation in Dnmt[1kd,3a−/−,3b−/−] cells. Near-complete bisulfite conversion and largely unbiased representation of RRBS libraries suggest that random shotgun bisulfite sequencing can be scaled to a genome-wide approach.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/e770d00897f98477520a9a647c37dd04fc609e51

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Citations in the corpus

High density DNA methylation array with single CpG site resolution. (2011, background): “Many microarray and next-generation sequencing based technologies have emerged, and analyses that were previously restricted to specific loci in a limited number of genes can now be performed on a genome-wide scale [1–10].”
Principles and challenges of genome-wide DNA methylation analysis (2010, methodology): “Reduced representation bisulphite sequencing (RRBS) was introduced to reduce sequence redundancy by selecting only some regions of the genome for sequencing by size-fractionation of DNA fragments after Bgl II digestio”
Epigenetics Analysis and Integrated Analysis of Multiomics Data, Including Epigenetic Data, Using Artificial Intelligence in the Era of Precision Medicine (2019, methodology): “Given the high cost and depth of sequencing to analyze methylation status in the whole genome, the RRBS technique was developed in 2005 to reduce the amount of nucleotides required for sequence to 1% of the genome [53].”
Preparation of reduced representation bisulfite sequencing libraries for genome-scale DNA methylation profiling (2011, background): “This should not be a major concern as CpC methylation is absent from somatic cells and also negligible in embryonic stem cells that contain otherwise non-CpG methylation (mostly CpA and some CpT, but essentially no CpC”
SDH mutations establish a hypermethylator phenotype in paraganglioma. (2013, methodology): “We used reduced representation bisulfite sequencing (RRBS) (Meissner et al., 2005) to examine base-pair resolution DNA methylation patterns in two samples of WT cells and in both Sdhb / clones.”
Differential methylation analysis for BS-seq data under general experimental design (2016, methodology): “…reduction of sequencing cost, an increasing number of experiments are now performed under general, multi-factor design, especially from the reduced representation bisulfite sequencing (RRBS) technology (Meissner et al., 2005; Jeddeloh et al., 2008) and enhanced RRBS (Akalin et al., 2012a).”
A Reference Methylome Database and Analysis Pipeline to Facilitate Integrative and Comparative Epigenomics (2013, background): “Alternatively, reduced representation bisulfite sequencing (RRBS) preferentially profiles CpG-rich regions and requires less sequencing [8].”
The mammalian epigenome. (2007, background): “9%) can be achieved; (2) amplification bias appears minimal; and (3) the technique readily scales with increasing sequencing capacity, and is automatable as no locusspecific primers are needed for PCR or sequencing (Meissner et al., 2005).”