DNA methylation research has always been a hot topic in disease research and is closely related to gene expression and phenotypic traits. RRBS is an accurate, efficient and economical method for DNA methylation research. Enrichment of promoter and CpG island regions by enzymatic cleavage (Msp I), combined with Bisulfite sequencing, provides high resolution DNA methylation detection.Service Specifications Demo Reports
Applications range from transcriptional regulation to developmental pathways to disease mechanisms and beyond.
For medical research：
- Pathological mechanism
- Tumor-subtypes classification
- Molecular markers
- Human evolution
- Drug target
For agricultural research：
- Agronomic traits
- Unsurpassed data quality: We guarantee that ≥ 80% of bases have a sequencing quality score ≥ Q30, exceeding Illumina’s official guarantee of ≥ 75%.
- Comprehensive data analysis: We use widely-accepted mainstream software such as Bismark and a mature in-house pipeline for mapping, Differentially Methylated Region (DMR) analysis, functional analysis and data visualization.
|Sample Type||Required amount|
|Genomic DNA||≥1.5 μg|
Sequencing Parameter and Analysis
|Platform Type||Illumina Novaseq 6000|
|Read Length||Pair-end 150|
|Recommended Sequencing Depth||≥ 10Gb clean data per sample|
Standard Data Analysis
|Data quality control|
|Alignment to reference genome|
|Quality controls for 5-mC identification|
|mCs detection, methylation level calculation|
|Methylation level and frequency distribution in different structural sequences|
|Differentially methylated regions (DMRs), Differentially Methylated Promoter (DMPs) detection and annotation|
|Function enrichment of DMR-associated genes and DMP-associated genes|
|Visualization of BS seq data|
Sample Quality Control
Library Quality Control
Data Quality Control
Map / Digestion
Library Construction with Bisulfite Treatment
Association of a History of Child Abuse with Impaired Myelination in the Anterior Cingulate Cortex: Convergent Epigenetic, Transcriptional, and Morphological Evidence
Child abuse has devastating and long-lasting consequences, considerably increasing the lifetime risk of negative mental health outcomes such as depression and suicide. In this study, RRBS and RNA-seq were used to explore the underlying mechanisms that increased the risk of mental health disease caused by childhood abuse. The results showed that a history of child abuse was associated with cell type‒specific changes in DNA methylation of oligodendrocyte genes and a global impairment of the myelin-related transcriptional program. These effects were absent in the depressed suicide completers with no history of child abuse, and they were strongly correlated with myelin gene expression changes observed in the animal model.
Sampling & Sequencing Strategy:
Postmortem brain samples from:
• depressed individuals who died by suicide with a history of severe child abuse, N=27
• depressed individuals who died by suicide without a history of severe child abuse, N=25
• psychiatrically healthy control subjects, N=26
2. Library preparation:
RRBS library and RNA-seq library
Illumina platform, PE100 and SE50
4. Bioinformatics analysis:
Differential methylation analysis (RRBS) and Differential expression analysis (RNA sequencing)
Results & Conclusion:
Epigenetic Effects of Child Abuse
1) Widespread differences of DNA methylation were uncovered between child abuse and control group.
Both hyper- and hypomethylation were detected in the child abuse group compared with the control group, suggesting that child abuse bidirectionally regulates epigenetic patterns in the cingulate cortex. The three most significantly differentially methylated regions intersected with genes directly related to myelin and oligodendrocytes: LINGO3, POU3F1 and ITGB1 (Fig. 1).
2) DNA methylation patterns are cell type specific
For both LINGO3 and POU3F1 decreased methylation in the child abuse group was confirmed and found to occur specifically in oligodendrocyte, but not neuronal, nuclei. These effects were absent in the depressed group. However, no significant DNA methylation differences for ITGB1 in either Sox10+ or NeuN+ nuclei between groups (Fig. 2).
Figure 1 Distribution of genome coverage.png
Figure 2 Methylation level distribution in whole genome.png
Figure 3 Circos plots for methylation density on chromosome.png
Figure 4 Heatmap analysis of gene functional region methylation levels-1.png
Figure 5 An overview of methylation level distribution at functional genetic elements.png
Figure 6 KEGG enrichment scatter.png