DNA methylation is a stable epigenetic modification and plays an important role in many biological processes such as embryonic development, genomic imprinting, and cell differentiation. Detection and quantification of methylation are critical to understand gene expression and other processes subjected to epigenetic regulation. Aberrant DNA methylation may be associated with a variety of human diseases represented by cancer, which opens new possibilities in diagnosis and therapy of cancers and other severe diseases. Whole genome bisulfite sequencing (WGBS), as a "gold standard" for a comprehensive view of the methylome, provides single-base resolution of methylated cytosines across the genome. With the improvement of library preparation methods and next-generation sequencing technology over the past decade, WGBS has become an increasingly widespread and informative method for analyzing DNA methylation in epigenomic-wide studies.

Considering the exponentially increasing number of researches based on whole-genome bisulfite sequencing (WGBS), a large amount of WGBS data and knowledge related to methylation studies have been accumulated. Contrary to other techniques such as Infinium 27K, 450k and EPIC, processing WGBS data of large datasets still remains cumbersome considering its resources usage. As a result, most single-base precision methylation databases are no longer available or have not been updated for years. It is essential to establish a dedicated database or platform to deeply integrate these WGBS data.

Here we present MethBank, a comprehensive database of DNA single-base resolution methylation profiles across a variety of species. As one of the core resources in National Genomics Data Center (NGDC), Beijing Institute of Genomics (BIG), Chinese Academy of Science (CAS) & China National Center for Bioinformation (CNCB), MethBank not only integrates 1271 high-quality (>20X) whole genome single-base methylome for 19 species, covering 179 tissues/cell lines and 13 biological contexts, but also provides manually curate knowledge of both featured differentially methylated genes (DMGs) across 11 kinds of biological contexts like disease and methylation tools collection. Besides, MethBank also provides analysis tools including Age Predictor, IDMP and DMR toolkit to help related research of biologists.

High quality raw sequencing data of WGBS samples are acquired from accessible data repositories (SRA/GSA).

The data included in the database were searched according to the following filtering rules:
The data after the initial screening are manually curated again.

WGBS data analysis pipeline includes quality control, trim adapters and low quality bases, align to reference genome, extract CpG methylation, quantify Gene/CpG island average methylation level, High methylated CpG islands analysis (Genomic location enrichment, GO & KEGG enrichment), Genes related to methylated CpG islands analysis, Differentially methylated regions (DMRs) analysis (Genomic location enrichment, GO & KEGG enrichment).

First, All bisulfite sequence were subjected to quality control by FastQC v0.11.7 and trimmed to remove adaptors and low quality bases using Fastq-dump (sratoolkit.2.8.2-1). Next, the reads that passed quality control were mapped to the reference genome of the corresponding species using Bismark-0.22.3. Detailed species reference genome information can be found and downloaded in the Downloads interface. We used the Bismark methylation extractor to extract methylation data from aligned, filtered reads. To visualize the quality of the data, we compute 6 indicators: Mapping rate, Unique mapping rate, Genome coverage, C coverage, Conversion rate and Depth. The corresponding calculations are shown below.

Then, bedtools v2.17.0 and python3 were used to analyze gene methylation profiles of promoter, body and downstream regions in the C/CG/CH context. Subsequently, the relationship between CpG island and gene were studied. From the perspective of CpG island, we calculated the DNA methylation level corresponding to CpG island, and selected highly methylated CpG island (average methylation level >= 0.6) as the research object for downstream analysis including genome enrichment, GO & KEGG enrichment. From the perspective of genes, we provide all CpG islands that overlapped with genes and counted the corresponding location information.

Finally, we identify the differential methylation regions (DMRs) by using DSS R-package for the typical biological contexts in single project, and analyze genomic location enrichment, GO & KEGG enrichment.

The manual curation of metadata are done on 2 levels ('Project' and Sample'). The corresponding review contents and standards are as follows: