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Overview of Methylated DNA Immunoprecipitation Sequencing (MeDIP-seq)

The Introduction to MeDIP-seq

DNA methylation is an epigenetic mark that plays a crucial role in many biological processes, such as embryonic development, gene regulation and disease genesis. The 5-methylcytosine (5mC) is a common methylated form of the DNA base cytosine. The combination of different pretreatment methods followed by different subsequent molecular biology techniques, such as DNA microarrays and next-generation sequencing (NGS), makes DNA methylation mapping feasible throughout the whole genome. Various NGS-based technologies for detecting DNA methylation have been summarized in Table 1 (Su, 2012).

Table 1. NGS based technologies for detecting DNA methylation.

Pretreatment method NGS-based analysis Applications 
Endonuclease digestion Methl-seq Assay a range of genomic elements;
Allow a broader survey of regions than classic methylation studies limited to CpG islands and promoters
HELP-seq Measure repetitive sequences, copy-number variability, allele-specific and smaller fragments (<50bp);
Detect the hypomethylated loci sensitively
MSCC Identify unmethylated regions by pinpointing unmethylated CpGs at single base-pair resolution
Affinity enrichment MeDIP-seq Generate unbiased, cost-effective and full-genome methylation levels without the limitations of restriction sites or CpG islands;
MIRA-seq Analyze recovered or double-stranded methylated DNA on a genome-wide scale;
Be applicable to various clinical and diagnostic situations
MDB-seq Be applied in any biological settings to identify differentially methylated regions at the genomic scale
MethylCap-seq Detect differentially methylated regions with high genome coverage; Detect DMRs in clinical samples
Bisulfite conversion WGBS Sensitively measure cytosine methylation on a genome-wide scale
RRBS Analyze a limited number of gene promoters and regulatory sequence elements in a large number of samples;
Analyze and compare genomic methylation patterns
BSPP Focus on sequencing the most informative genomic regions;
Be applied to Exon capturing and SNP genotyping;
Detect methylation in large genomes
BC-seq Detect site-specific switches in methylation;
Determine DNA methylation frequencies in CGIs sampled from a variety of genomic settings including promoters, exons, introns, and intergenic loci

Methylated DNA immunoprecipitation (MeDIP), which was first described in 2005 (Weber et al., 2005), is a genome-scale purification technique used to enrich methylated DNA fragments with specific antibodies. MeDIP sequencing (MeDIP-Seq) first described in 2008 is a powerful tool used to study 5mC and 5-hydroxymethylcytosine (5hmC) modification by combining MeDIP with high-throughput sequencing (Downet al., 2008). The deep sequencing could provide great genome coverage and reveal the majority of immunoprecipitated methylated DNA.

Workflow of MeDIP-seq

The workflow of MeDIP-seq is illustrated in Figure 1. It begins with the isolation of methylated DNA fragments by 5mC-specific antibodies via immunoprecipitation. Then the enriched methylated DNA is subjected to DNA purification, library preparation and high-throughput sequencing (Taiwo et al., 2012).

Overview of Methylated DNA Immunoprecipitation Sequencing (MeDIP-seq)Figure 1. Workflow of MeDIP-seq.

Advantages of MeDIP-seq

  • Cover CpG and non-CpG 5mC throughout the genome.
  • Be on the Genome-wide scale or in any regions of interest.
  • Avoid nonspecific enrichment for 5hmC by using 5mC specific antibody during the process of MeDIP.
  • Require low input DNA.

Limitations of MeDIP-seq

  • Genomic resolution: resolution of the methylome is lower (~150 bp) compared to single base resolution offered by bisulfite sequencing.
  • Nonspecific interaction: antibody specificity and selectivity must be tested to avoid nonspecific interaction.
  • Antibody-based selection bias: methylated DNA recovery by the antibody is affected by mC/mCG density, making regions of very low (<1.5%) density may be underrepresented or even interpreted as unmethylated (Taiwo et al., 2012). The antibody-based selection is biased towards hypermethylated regions.

Bioinformatics Pipeline for the Analysis of MeDIP-seq Data

A commonly used bioinformatics pipeline for analysis of MeDIP-seq data is shown in Figure 2.

Overview of Methylated DNA Immunoprecipitation Sequencing (MeDIP-seq)Figure 2. Bioinformatics pipeline for MeDIP-seq data analysis.

A number of computational tools have been developed for the analysis of MeDIP data, including Batman, MEDIPS, MeDUSA and MeQA (Table 2). The method to be used largely depends on the purpose of the experiment.

Table 2. Examples of software available for the analysis of MeDIP-seq data

Software Summary
Batman Bayesian tool for methylation analysis of MeDIP profiles
MEDIPS Bioconductor package providing a comprehensive approach for normalizing and analyzing MeDIP-seq data
MeDUSA Perform a full analysis of MeDIP-seq data, including sequence alignment, QC and determination and annotation of DMRs
MeQA Pipeline for the pre-processing, quality assessment, read distribution and methylation estimation for MeDIP-seq datasets

Comparison of MeDIP-chip and MeDIP-seq

The purified methylated DNA via MeDIP can be input into high-throughput DNA detection methods such as high-resolution DNA microarrays (MeDIP-chip) or high-throughput sequencing (MeDIP-Seq). The workflow overview of MeDIP-chip and MeDIP-Seq is illustrated in Figure 3. In MeDIP-chip, a fraction of the input DNA and the enriched methylated DNA are labeled with cyanine-5 (Cy5; red) and cyanine-3 (Cy3; green) respectively to identify sequences that show significant differences at hybridization levels, thereby confirming the sequence of interest is enriched.

Overview of Methylated DNA Immunoprecipitation Sequencing (MeDIP-seq)Figure 3. Workflow overview of the MeDIP procedure followed by (A) array-hybridization or (B) high-throughput sequencing (from wiki)

Different DNA methylation analysis approaches have competing strengths and weaknesses. Array-based identification of MeDIP sequences is limited to the array design. As a result, the resolution is restricted to the probes in the array design, whereas the sequence-based identification of MeDIP sequences is generally applicable to any species for which a reference genome exists. A summary of features and potential sources of bias for various techniques is shown in Table 3 (Laird, 2010).

Table 3. Features and sources of bias for various techniques

MeDIP–chip MeDIP–seq RRBS WGBS
Features Unambiguous identification of CpG measured
In cis co-methylation information
Non-CpG methylation information
Allele-specific measurement capability
Good coverage of regions with low CpG density
Compatible with low amounts of input DNA
Full repeat-masked genome coverage
Potential sources of bias Copy-number variation bias
Fragment size bias
Incomplete bisulfite conversion bias
Bisulfite PCR bias
Cross-hybridization bias
DNA methylation status bias
GC content bias
CpG density bias

RRBS: Reduced representation bisulfite sequencing

WGBS: whole genome bisulfite sequencing

Applications of MeDIP-seq

The advantages of cost-effective and requirement for low-input DNA (Taiwo et al., 2012; Zhao et al., 2014) make MeDIP-seq suitable for studies involving low amount of DNA samples, such as oocytes, early embryos (Zhang et al., 2017), and human tumor biopsies (Kim et al., 2011; Zhao et al., 2014).

At CD Genomics, we are dedicated to providing reliable epigenomics sequencing services, including EpiTYPER DNA methylation analysistargeted bisulfite sequencingreduced representation bisulfite sequencing (RRBS)whole genome bisulfite sequencingMeDIP sequencingChIP-seq, and MethylRAD-seq.

References:

  1. Down, T.A., Rakyan, V.K., Turner, D.J., Flicek, P., Li, H., Kulesha, E., Graf, S., Johnson, N., Herrero, J., Tomazou, E.M., et al. (2008). A Bayesian deconvolution strategy for immunoprecipitation-based DNA methylome analysis. Nature biotechnology 26, 779-785.
  2. Kim, J.H., Dhanasekaran, S.M., Prensner, J.R., Cao, X., Robinson, D., Kalyana-Sundaram, S., Huang, C., Shankar, S., Jing, X., Iyer, M., et al. (2011). Deep sequencing reveals distinct patterns of DNA methylation in prostate cancer. Genome research 21, 1028-1041.
  3. Laird, P.W. (2010). Principles and challenges of genomewide DNA methylation analysis. Nature reviews Genetics 11, 191-203.
  4. Su, J. (2012). Advances in Bioinformatics Tools for High-Throughput Sequencing Data of DNA Methylation. Hereditary Genetics 01.
  5. Taiwo, O., Wilson, G.A., Morris, T., Seisenberger, S., Reik, W., Pearce, D., Beck, S., and Butcher, L.M. (2012). Methylome analysis using MeDIP-seq with low DNA concentrations. Nature protocols 7, 617-636.
  6. Weber, M., Davies, J.J., Wittig, D., Oakeley, E.J., Haase, M., Lam, W.L., and Schubeler, D. (2005). Chromosome-wide and promoter-specific analyses identify sites of differential DNA methylation in normal and transformed human cells. Nature genetics 37, 853-862.
  7. Zhang, Y., Gou, W., Ma, J., Zhang, H., Zhang, Y., and Zhang, H. (2017). Genome methylation and regulatory functions for hypoxic adaptation in Tibetan chicken embryos. PeerJ 5, e3891.
  8. Zhao, M.T., Whyte, J.J., Hopkins, G.M., Kirk, M.D., and Prather, R.S. (2014). Methylated DNA immunoprecipitation and high-throughput sequencing (MeDIP-seq) using low amounts of genomic DNA. Cellular reprogramming 16, 175-184.
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