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).
Figure 1. Workflow of MeDIP-seq.
Advantages of MeDIP-seq
Limitations of MeDIP-seq
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.
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
|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.
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
|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||√||√|
|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 analysis, targeted bisulfite sequencing, reduced representation bisulfite sequencing (RRBS), whole genome bisulfite sequencing, MeDIP sequencing, ChIP-seq, and MethylRAD-seq.