Data description

DNA methylation can be analyzed using a wide range of methods [1], with bisulfite sequencing considered the current gold standard. Current technologies such as whole genome bisulfite sequencing (WGBS) and reduced representation bisulfite sequencing (RRBS) provide unprecedented detail of methylation patterns throughout the genome, but the complexity of DNA methylation patterns is masked when simple summary metrics are used. For example, most studies of DNA methylation rationalize levels to a percentage value, which typically masks allelic patterns when interpreting the data. We have developed Methpat, a tool that summarizes and visualizes complex DNA methylation data collected by massively parallel sequencing of bisulfite DNA [2]. Using this tool, the DNA methylation state of individual CpG sites and the abundance of allelic patterns can be visualized [3]. Furthermore, by measuring the abundance of allelic DNA methylation patterns, cellular heterogeneity in methylation patterns can now be explored [4].

The utility of Methpat was demonstrated by measuring DNA methylation in 86 samples (Table 1) across 48 regions of interest (Table 2). This was achieved by using multiplex PCR on bisulfite converted DNA followed by massively parallel sequencing using an Illumina MiSeq Sequencing platform with v3 chemistry. Each sample was indexed and pooled at equimolar concentrations into a single library pool for sequencing. Data has been deposited into GEO with reference identifiers GSE67856 [5] and GSE71804 [6]. A panel of breast cancer cell lines treated with epidermal growth factor and transforming growth factor beta were also analyzed in parallel [7].

Table 1 Human Samples used in this study
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Table 2 Bisulfite PCR primers used in this study
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Our initial QC assessment indicated high bisulfite conversion efficiency with very low non-CpG Cs in reads. An additional amplicon that corresponded to a sequence containing no CpG sites was also included as a control, from which all cytosines were observed to have converted to thymidine residues [1].

The data included here are the Sequence Read Archive files generated from our experiment. These have been aligned onto the hg38 reference genome using Bismark v0.9.0, from which a BAM file for each sample is generated. Using the Bismark_methylation_extractor command, the methylation status of cytosine residues within each read is output to a tab-delimited file. Methpat then operates on this output file to generate both a summarized tab-delimited file of read pattern counts and a HTML file for visualization. We have included the BAM files, Bismark_methylation_extractor output files and Methpat output files as supporting data. Methpat requires a Browser Extensive Data (BED)-format-like file that contains the coordinates for each amplicon of interest, their size and their primer lengths to extract and summarize DNA methylation pattern counts. The flow of data is summarized in Fig. 1.

Fig. 1
figure 1

Flow of data towards visualization via Methpat. Raw fastq files are aligned to the hg38 reference genome in bisulfite space. a hg38 reference is prepared for Bismark using Bismark_genome_preparation with default parameters. b Bismark is used to align raw reads from fastq files to generate BAM alignment files. c Bismark_methylation_extractor is then used to extract the methylation status of all cytosines in every aligned read and outputs a tab-delimited file that Methpat operates on. Methpat requires this file along with a BED formatted file containing information for each amplicon of interest. This includes the start and end coordinates of the amplicon and the primer lengths for each amplicon. The output of Methpat is a summary tab-delimited file containing read counts of DNA methylation patterns of the amplicons of interest and an HTML file for visualization and publication quality figures

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Our data has the potential to be used to investigate co-methylation [8], given the unprecedented depth of coverage of the amplicons investigated even in a single MiSeq run. We have interrogated a variety of regions of the genome including repetitive elements and the mitochondrial genome, which remain a challenge for most short read aligners. The interpretation of DNA methylation at repetitive sequence elements has always been a challenge and they are assumed to be methylated [9]. However, the dynamics of repetitive element DNA methylation in cancer [10] and development [11] remain areas of interest that can now be properly interpreted with massively parallel sequencing and visualization tools such as Methpat.

Availability of software and requirements

Project name: Methpat

Project home page: http://bjpop.github.io/methpat/

Operating system(s): any POSIX-like operating system (i.e.: Linux, OS X)

Programming language: Python 2.7, HTML and Javascript

Other requirements: Web Browser to view visualization output (HTML file). Suggested browsers include Firefox, Chrome or Safari. Methpat requires output files derived by Bismark (http://www.bioinformatics.babraham.ac.uk/projects/bismark/) and the Bismark_methylation_extractor command. Methpat can be accessed directly from http://bjpop.github.io/methpat/. With further instructions found at the URL.

License: 3-clause BSD License

Any restrictions to use by non-academics: None

A flow diagram of analytical requirements and files can be found in Fig. 1.

Availability of supporting data and materials

Sequence files associated with main research publication deposited in GEO, GSE67856 [5]. Remaining files are deposited in GEO, GSE71804 [6].

BAM files, bismark_methylation_extractor output files and Methpat output files for each sample analyzed in this paper are available in the GigaScience GigaDB repository [12].