scholarly journals Differential methylation analysis of reduced representation bisulfite sequencing experiments using edgeR

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 2055 ◽  
Author(s):  
Yunshun Chen ◽  
Bhupinder Pal ◽  
Jane E. Visvader ◽  
Gordon K. Smyth
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Bisulfite Sequencing ◽  
Differential Methylation ◽  
Complete Analysis ◽  
Methylation Analysis ◽  
Model Framework ◽  
Experimental Conditions ◽  
Reduced Representation ◽  
Reduced Representation Bisulfite Sequencing

Studies in epigenetics have shown that DNA methylation is a key factor in regulating gene expression. Aberrant DNA methylation is often associated with DNA instability, which could lead to development of diseases such as cancer. DNA methylation typically occurs in CpG context. When located in a gene promoter, DNA methylation often acts to repress transcription and gene expression. The most commonly used technology of studying DNA methylation is bisulfite sequencing (BS-seq), which can be used to measure genomewide methylation levels on the single-nucleotide scale. Notably, BS-seq can also be combined with enrichment strategies, such as reduced representation bisulfite sequencing (RRBS), to target CpG-rich regions in order to save per-sample costs. A typical DNA methylation analysis involves identifying differentially methylated regions (DMRs) between different experimental conditions. Many statistical methods have been developed for finding DMRs in BS-seq data. In this workflow, we propose a novel approach of detecting DMRs using edgeR. By providing a complete analysis of RRBS profiles of epithelial populations in the mouse mammary gland, we will demonstrate that differential methylation analyses can be fit into the existing pipelines specifically designed for RNA-seq differential expression studies. In addition, the edgeR generalized linear model framework offers great flexibilities for complex experimental design, while still accounting for the biological variability. The analysis approach illustrated in this article can be applied to any BS-seq data that includes some replication, but it is especially appropriate for RRBS data with small numbers of biological replicates.

scholarly journals Differential methylation analysis of reduced representation bisulfite sequencing experiments using edgeR

F1000Research ◽  
2018 ◽  
Vol 6 ◽  
pp. 2055 ◽  
Author(s):  
Yunshun Chen ◽  
Bhupinder Pal ◽  
Jane E. Visvader ◽  
Gordon K. Smyth
Keyword(s):  
Dna Methylation ◽  
Bisulfite Sequencing ◽  
Epigenetic Modification ◽  
Differential Methylation ◽  
Design Matrix ◽  
Rna Seq ◽  
Methylation Analysis ◽  
Reduced Representation ◽  
Reduced Representation Bisulfite Sequencing ◽  
Differential Methylation Analysis

Cytosine methylation is an important DNA epigenetic modification. In vertebrates, methylation occurs at CpG sites, which are dinucleotides where a cytosine is immediately followed by a guanine in the DNA sequence from 5' to 3'. When located in the promoter region of a gene, DNA methylation is often associated with transcriptional silencing of the gene. Aberrant DNA methylation is associated with the development of various diseases such as cancer. Bisulfite sequencing (BS-seq) is the current "gold-standard" technology for high-resolution profiling of DNA methylation. Reduced representation bisulfite sequencing (RRBS) is an efficient form of BS-seq that targets CpG-rich DNA regions in order to save sequencing costs. A typical bioinformatics aim is to identify CpGs that are differentially methylated (DM) between experimental conditions. This workflow demonstrates that differential methylation analysis of RRBS data can be conducted using software and methodology originally developed for RNA-seq data. The RNA-seq pipeline is adapted to methylation by adding extra columns to the design matrix to account for read coverage at each CpG, after which the RRBS and RNA-seq pipelines are almost identical. This approach is statistically natural and gives analysts access to a rich collection of analysis tools including generalized linear models, gene set testing and pathway analysis. The article presents a complete start to finish case study analysis of RRBS profiles of different cell populations from the mouse mammary gland using the Bioconductor package edgeR. We show that lineage-committed cells are typically hyper-methylated compared to progenitor cells and this is true on all the autosomes but not the sex chromosomes. We demonstrate a strong negative correlation between methylation of promoter regions and gene expression as measured by RNA-seq for the same cell types, showing that methylation is a regulatory mechanism involved in epithelial linear commitment.

Genome-Wide DNA Methylation Analysis Shows Enrichment of Differential Methylation in “Open Seas” and Enhancers and Reveals Hypomethylation in DNMT3A Mutated Cytogenetically Normal AML (CN-AML)

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 653-653 ◽  
Author(s):  
Ying Qu ◽  
Andreas Lennartsson ◽  
Verena I. Gaidzik ◽  
Stefan Deneberg ◽  
Sofia Bengtzén ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Cpg Island ◽  
Cpg Islands ◽  
Differential Methylation ◽  
Methylation Analysis ◽  
Cpg Sites ◽  
Genome Wide ◽  
Genomic Regions ◽  
Methylation Patterns

Abstract Abstract 653 DNA methylation is involved in multiple biologic processes including normal cell differentiation and tumorigenesis. In AML, methylation patterns have been shown to differ significantly from normal hematopoietic cells. Most studies of DNA methylation in AML have previously focused on CpG islands within the promoter of genes, representing only a very small proportion of the DNA methylome. In this study, we performed genome-wide methylation analysis of 62 AML patients with CN-AML and CD34 positive cells from healthy controls by Illumina HumanMethylation450K Array covering 450.000 CpG sites in CpG islands as well as genomic regions far from CpG islands. Differentially methylated CpG sites (DMS) between CN-AML and normal hematopoietic cells were calculated and the most significant enrichment of DMS was found in regions more than 4kb from CpG Islands, in the so called open sea where hypomethylation was the dominant form of aberrant methylation. In contrast, CpG islands were not enriched for DMS and DMS in CpG islands were dominated by hypermethylation. DMS successively further away from CpG islands in CpG island shores (up to 2kb from CpG Island) and shelves (from 2kb to 4kb from Island) showed increasing degree of hypomethylation in AML cells. Among regions defined by their relation to gene structures, CpG dinucleotide located in theoretic enhancers were found to be the most enriched for DMS (Chi χ2<0.0001) with the majority of DMS showing decreased methylation compared to CD34 normal controls. To address the relation to gene expression, GEP (gene expression profiling) by microarray was carried out on 32 of the CN-AML patients. Totally, 339723 CpG sites covering 18879 genes were addressed on both platforms. CpG methylation in CpG islands showed the most pronounced anti-correlation (spearman ρ =-0.4145) with gene expression level, followed by CpG island shores (mean spearman rho for both sides' shore ρ=-0.2350). As transcription factors (TFs) have shown to be crucial for AML development, we especially studied differential methylation of an unbiased selection of 1638 TFs. The most enriched differential methylation between CN-AML and normal CD34 positive cells were found in TFs known to be involved in hematopoiesis and with Wilms tumor protein-1 (WT1), activator protein 1 (AP-1) and runt-related transcription factor 1 (RUNX1) being the most differentially methylated TFs. The differential methylation in WT 1 and RUNX1 was located in intragenic regions which were confirmed by pyro-sequencing. AML cases were characterized with respect to mutations in FLT3, NPM1, IDH1, IDH2 and DNMT3A. Correlation analysis between genome wide methylation patterns and mutational status showed statistically significant hypomethylation of CpG Island (p<0.0001) and to a lesser extent CpG island shores (p<0.001) and the presence of DNMT3A mutations. This links DNMT3A mutations for the first time to a hypomethylated phenotype. Further analyses correlating methylation patterns to other clinical data such as clinical outcome are ongoing. In conclusion, our study revealed that non-CpG island regions and in particular enhancers are the most aberrantly methylated genomic regions in AML and that WT 1 and RUNX1 are the most differentially methylated TFs. Furthermore, our data suggests a hypomethylated phenotype in DNMT3A mutated AML. Disclosures: No relevant conflicts of interest to declare.

Preparation of reduced representation bisulfite sequencing libraries for genome-scale DNA methylation profiling

2011 ◽  
Vol 6 (4) ◽  
pp. 468-481 ◽  
Author(s):  
Hongcang Gu ◽  
Zachary D Smith ◽  
Christoph Bock ◽  
Patrick Boyle ◽  
Andreas Gnirke ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Bisulfite Sequencing ◽  
Reduced Representation ◽  
Reduced Representation Bisulfite Sequencing ◽  
Dna Methylation Profiling ◽  
Genome Scale ◽  
Methylation Profiling

5 Gene expression analysis and DNA methylation patterns of porcine somatic cell nuclear transfer blastocysts with high and low incidence of apoptosis

2019 ◽  
Vol 31 (1) ◽  
pp. 128
Author(s):  
L. Moley ◽  
R. Jones ◽  
R. Kaundal ◽  
A. Thomas ◽  
A. Benninghoff ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Enrichment Analysis ◽  
Epigenetic Reprogramming ◽  
Reduced Representation ◽  
False Discovery ◽  
Reduced Representation Bisulfite Sequencing ◽  
Methylation Patterns ◽  
Dna Methylation Analysis ◽  
Go Terms

Somatic cell NT (SCNT) efficiency remains poor, preventing the technology from being regularly used in the agricultural industry. It is believed that faulty epigenetic reprogramming of SCNT embryos leads to the low overall success. A clear apoptotic signature is associated with inappropriate gene expression and epigenomic aberrancies in many experimental cell culture systems, and we hypothesised that an apoptosis biomarker could be used to effectively separate properly reprogrammed porcine SCNT embryos from those that are destined to fail due to incomplete reprogramming. Therefore, our objective was to evaluate global gene expression and DNA methylation patterns in high- and low-apoptosis individual embryos in an effort to characterise the extent of genomic reprogramming that had taken place. Porcine SCNT blastocysts on Day 6 of development were stained with a nontoxic, noninvasive caspase activity reporter, and the top and bottom 20% of detected caspase activity were classified as high and low apoptosis, respectively (3 replicate cloning sessions; n=13 embryos per group). Genomic DNA and total RNA were isolated from each individual blastocyst. The RNA sequencing libraries were prepared using the Ovation SoLo RNA-Seq system (NuGen, San Carlos, CA, USA). Reduced representation bisulfite sequencing libraries were prepared for DNA methylation analysis using a modification of the single-cell reduced representation bisulfite sequencing global DNA methylation analysis approach detailed by Guo et al. (2015 Nat. Protoc. 10, 645-59). The RNA sequencing analysis using EdgeR (https://bioconductor.org/packages/release/bioc/html/edgeR.html) revealed 175 total differentially expressed genes (fold change ≥1.5; false discovery rate ≤0.05) between the high- and low-apoptosis SCNT embryos. This list of differentially expressed genes was used to perform enrichment analysis to identify overrepresented Gene Ontology (GO) terms or Kyoto Encyclopedia of Genes and Genomes pathways (DAVID Ease version 6.8 (https://david.ncifcrf.gov/) against the Sus scrofa background genome). However, no significantly enriched GO terms or pathways were identified (false discovery rate P&gt;0.05). Analysis of global DNA methylation patterns between high- and low-apoptosis SCNT embryos using MethylKit (Akalin et al. 2012Genome Biol. 13, R87) revealed 335 differentially methylated 100-bp regions with at least 25% difference in methylation (adjusted P ≤ 0.01). Gene transcription start sites associated with these regions were used for enrichment analysis; again, no significant enrichment of GO terms or Kyoto Encyclopedia of Genes and Genomes pathways was identified. Principal component analysis of CpG methylation showed the low-apoptosis embryos clustering more tightly than the high-apoptosis embryos, which were highly scattered. Ongoing comparisons of high- and low-apoptosis cloned embryos with naturally fertilized embryos produced invivo may provide more information about which embryos were properly reprogrammed. Although we are still pursuing a link between reprogramming and gene expression in high- and low-apoptosis embryos, we conclude that these data support a model of stochastic epigenetic reprogramming following SCNT and reinforce the necessity of identifying embryos most likely to be successful due to proper epigenetic reprogramming in order to increase SCNT efficiency.

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