Developer

This pipeline is developed by the BMAP team.

Benchmark Data

A set of human prostate cancer cell line (LNCa, PrEC) datasets that were detected using both WGBS (Illumina HiSeq 2500) and microarray (Infinium MethylationEPIC BeadChip) was used to assess the accuracy of the WGBS analysis pipeline. These original datasets were downloaded from NCBI-SRA/GEO (SRP089722, GSE86833). We randomly selected 1,000 CpG sites from the over 700,000 CpG sites covered by the original dataset and performed a correlation analysis between the results of the user-customized WGBS analysis pipeline and the two reference pipelines from the original publication.

Benchmark Results

The correlation coefficient indicates the accuracy of the customized software. Next, we tested the accuracy of the WGBS analysis pipeline in BMAP and obtained an average correlation of 0.960.

Workflow

The standardized Whole genome bisulfite sequencing (WGBS) analysis pipeline for methylation data was established in BMAP following the guidelines in Krueger[1] and Akalin’s[2]studies.

Reference

[1] Krueger, F. and Andrews, S.R. (2011) Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications. Bioinformatics, 27, 1571-1572.
[2] Akalin, A., Kormaksson, M., Li, S., Garrett-Bakelman, F.E., Figueroa, M.E., Melnick, A. and Mason, C.E. (2012) methylKit: a comprehensive R package for the analysis of genome-wide DNA methylation profiles. Genome Biol, 13, R87.

Introduction

Epigenetic modifications confer stable transcriptional patterns in the brain, and both normal and abnormal brain function involve specialized brain regions.

Methods

We examined DNA methylation by whole-genome bisulfite sequencing in neuronal and non-neuronal populations from four brain regions (anterior cingulate gyrus, hippocampus, prefrontal cortex, and nucleus accumbens) as well as chromatin accessibility in the latter two.

Results

We find pronounced differences in both CpG and non-CpG methylation (CG-DMRs and CH-DMRs) only in neuronal cells across brain regions. Neuronal CH-DMRs were highly associated with differential gene expression, whereas CG-DMRs were consistent with chromatin accessibility and enriched for regulatory regions. These CG-DMRs comprise ~12 Mb of the genome that is highly enriched for genomic regions associated with heritability of neuropsychiatric traits including addictive behavior, schizophrenia, and neuroticism, thus suggesting a mechanistic link between pathology and differential neuron-specific epigenetic regulation in distinct brain regions.

Reference

Rizzardi L F, Hickey P F, DiBlasi V R, et al. Neuronal brain-region-specific DNA methylation and chromatin accessibility are associated with neuropsychiatric trait heritability[J]. Nature neuroscience, 2019, 22(2): 307.

v2.0: Linked up with the repository system. The code and Docker image associated with this pipeline are CRS1078713618578812928 and SRS1078716430473768960, respectively.

v1.2: Added new option -genome version (GRCh37, GRCh38)

v1.1: Added nucleotide coverage calculation function

v1.0: Contains basic analysis steps such as QC, mapping to reference, methylation level calculation, annotation and differential analysis.

Reproducibility

The pipeline utilizes code CRS1078713618578812928 and Docker image SRS1078716430473768960, both of which are stored in the BMAP repositories.

Pipeline

The standardized Whole genome bisulfite sequencing (WGBS) analysis pipeline for methylation data was established in BMAP following the guidelines in Krueger[1] and Akalin’s[2]studies.

Usage

1. Select the application 'DNA Methylation Sequencing Data Analysis'

2. Click on the 'Analysis' button

3. Click on the 'Differential Methylation Analysis'

4. Fill in the request parameters

Note: the R1 and R2 file names should include the subfolder name and the extension name (eg. data/normal_R1.fq.gz).

5. Upload the compressed data in gz or rar format.

6. View the results