MethPat: a tool for the analysis and visualisation of complex methylation patterns obtained by massively parallel sequencing
© Wong et al. 2016
Received: 3 August 2015
Accepted: 15 February 2016
Published: 24 February 2016
DNA methylation at a gene promoter region has the potential to regulate gene transcription. Patterns of methylation over multiple CpG sites in a region are often complex and cell type specific, with the region showing multiple allelic patterns in a sample. This complexity is commonly obscured when DNA methylation data is summarised as an average percentage value for each CpG site (or aggregated across CpG sites). True representation of methylation patterns can only be fully characterised by clonal analysis. Deep sequencing provides the ability to investigate clonal DNA methylation patterns in unprecedented detail and scale, enabling the proper characterisation of the heterogeneity of methylation patterns. However, the sheer amount and complexity of sequencing data requires new synoptic approaches to visualise the distribution of allelic patterns.
We have developed a new analysis and visualisation software tool “Methpat”, that extracts and displays clonal DNA methylation patterns from massively parallel sequencing data aligned using Bismark. Methpat was used to analyse multiplex bisulfite amplicon sequencing on a range of CpG island targets across a panel of human cell lines and primary tissues. Methpat was able to represent the clonal diversity of epialleles analysed at specific gene promoter regions. We also used Methpat to describe epiallelic DNA methylation within the mitochondrial genome.
Methpat can summarise and visualise epiallelic DNA methylation results from targeted amplicon, massively parallel sequencing of bisulfite converted DNA in a compact and interpretable format. Unlike currently available tools, Methpat can visualise the diversity of epiallelic DNA methylation patterns in a sample.
KeywordsDNA methylation software visualization bisulfite targeted amplicon epigenetics epiallele
In mammals, the predominant and most widely studied DNA methylation mark occurs at CpG dinucleotide (CpG) palindromic sequences . The vast majority of methods that investigate DNA methylation utilise bisulfite treatment of genomic DNA followed by PCR amplification to distinguish methylated from unmethylated CpG sites [2–5]. Bisulfite treatment discriminates methylated from unmethylated cytosines by selectively reacting with unmethylated cytosines to generate uracil. During the subsequent first step of PCR amplification, the uracils are read as thymine. Conversely, methylated cytosines do not react with the bisulfite reagent and remain as cytosines after PCR amplification . DNA methylation readouts at single sites employing bisulfite conversion become analogous to genotyping assays by detecting either a cytosine or thymidine at the C position of a CpG site and are interpreted as methylated or unmethylated cytosines respectively.
An epiallele refers to a distinct pattern of methylation, typically over a short genomic region [7, 8]. In addition to the methylation state given for each CpG site, the pattern of DNA methylation of all CpG sites across the epiallelic or clonal template can also be characterised . Indeed, in terms of biological function, CpG methylation should be often considered in an allelic fashion over multiple adjacent CpG sites [9, 10].
However, currently most studies summarise data into average percentage values at each CpG site thus losing the positional pattern information of DNA methylation across each clonal template . Analysis platforms such as the Illumina Infinium BeadArray , bisulfite pyrosequencing  and SEQUENOM™ EpiTYPER™  use bisulfite mediated chemistry to discriminate the methylation state of CpG sites but summarise measurements into percentage values across each CpG site or region of interest. Percentage methylation described in most DNA methylation studies hides important pattern and positional information of DNA methylation with potential functional and regulatory relevance . It is only with clonal sequencing approaches [1, 14, 15], whole genome bisulfite sequencing  or reduced representation bisulfite sequencing , that the methylation state of individual CpG sites within a genomic DNA template can be readily measured in a digital sense, as methylated or not, allele by allele.
Imprinted regions of the genome such as IGF2/H19 and MEST typically display two epialleles, where one is completely methylated and the other is unmethylated. The loss of imprinting at such loci leads to syndromic complications [18, 19]. Average DNA methylation across these loci are typically presented as 50 % methylation but the pattern of DNA methylation at each epiallele is lost .
Heterogeneous DNA methylation describes the phenomenon where different contiguous CpG sites have different levels of methylation. DNA methylation heterogeneity can arise in a variety of ways including but not limited to: (i) more than a single population of cells is analysed that differ in DNA methylation at the locus of interest, (ii) the locus of interest is imprinted i.e. two different epialleles are present in each cell or, (iii) the locus is inherently heterogeneous in its DNA methylation composition. It is only using clonal sequencing approaches with allelic outputs, high resolution melting (HRM) [7, 20], or a novel ligation mediated approach  that heterogeneous DNA methylation can be detected. It is also inferred by varying methylation at CpG sites e.g. from Pyrosequencing. Importantly, the number of methylated alleles can be substantially underestimated unless clonal approaches are used . Clonal sequencing is currently the best method to investigate heterogeneous DNA methylation and the extent of epiallelic methylation patterns that exist within a single sample .
Until recently, it has been cost prohibitive to assess the complexity of methylation patterns, as large number of clones need to be individually sequenced to determine the extent of heterogeneous DNA methylation. As one clone represents a single epiallele, many tens to hundreds of clones need to be sequenced to gain a true representation of different epialleles in a sample. The introduction of massively parallel sequencing enables the sequencing of many thousands of DNA templates from multiple regions simultaneously providing a true representation of the diversity and extent of heterogeneous DNA methylation patterns derived from a given sample. However, as the number of clones sequenced increases, the ability to analyse and present this type of data then becomes a significant challenge, and at this time, there are very few software tools available to manage such data from massively parallel sequencing experiments [21, 22]. Some visualisation and analysis tools are available for Bisulfite Sanger Sequencing including BiQ Analyzer , MethVisual , QUMA , BISMA . However, these tools do not scale up with massively parallel sequencing having been designed for Sanger sequencing. BiQ Analyser HiMod is a tool that enables visualisation of high throughput sequencing of 5-methylcytosine and other methyl-variant modifications  however, results are expressed in percentage methylation values masking allelic methylation patterns.
In this study, we have developed Methpat, a software tool which processes bisulfite sequencing data following Bismark alignment  and summarises DNA methylation according to epiallelic methylation patterns. This software has been used to analyse multiplex bisulfite amplicon PCR coupled to massively parallel deep sequencing on a range of primary haematopoietic tissue samples and model cancer cell lines to observe the extent of heterogeneous DNA methylation. Methpat is also able to create publication-ready, compact visualisations of the summarised data showing heterogeneous DNA methylation patterns in a space efficient and comprehensible manner.
Materials, methods and implementation
Samples, library preparation, sequencing and sequence alignment. Details of sample preparation, library generation, sequencing and sequence alignment protocol employed are summarised in the Additional file 1. Human samples used in this study were approved for research by The Royal Children’s Hospital Human Research Ethics Committee (RCH HREC#27138E).
Methpat—a tool to summarise epiallelic DNA methylation patterns
We have developed the software tool, Methpat to summarise and visualise the resultant epiallelic DNA methylation patterns from multiplex bisulfite amplicon experiments. Source code is available on GitHub (http://bjpop.github.io/methpat/). Methpat takes the output from bismark_methylation_extractor and summarises the methylation state of each CpG site within each amplicon template sequenced. DNA methylation patterns are then counted and their abundance is summarised into a tab delimited text file amenable for further downstream statistical analyses. Methpat also outputs a standalone HTML file that provides a visualisation of the DNA methylation pattern of each amplicon of interest and a visual summary of their abundance in each sample. A range of visualisation settings are customisable so that the end-user can change the settings to facilitate interpretation of the data and generate publication-ready figures. These options include presenting pattern counts as a percentage of the total, as absolute count or log-scaled counts (Additional file 2: Figure S1). Patterns can be arranged in order either by count abundance or by DNA methylation state. Colours within the visualisation can also be modified (Additional file 3: Figure S2), and the image saved as a PNG file for presentation or publication.
Bismark alignment of sequencing data and statistics
After evaluating a range of bisulfite-aware massively parallel alignment software , we decided to use Bismark  with the highest mapping efficiency and highest proportion of concordantly mapped reads across the aligners compared to unique alignments in our previous study . In addition, Bismark produces an output string that enables the processing of epiallelic DNA methylation patterns when parsed. , We developed Methpat to read this output and summarise the data in a compact and interpretable manner.
Mapping statistics of bisulfite amplicon libraries
Total C’s analysed
Furthermore, two amplicons targeting unique regions within the human genome that contain no CpG sites were used to determine the bisulfite conversion efficiency in an orthogonal manner. Of the reads that passed alignment criteria for a subset of samples, we found that all non-CpG cytosines were converted in our experiment (Additional file 4: Figure S3). Mapping efficiency is one of many metrics used to determine the quality of the data and would suggest data from 6c-cd19 was not nominal. However, across all samples analysed, the bisulfite conversion efficiency was very high and was therefore included for visualisation using Methpat.
For the target regions analysed, an overall DNA methylation level ranging from 27.7 to 85.8 % was observed. In the lower ranges, the samples were mainly primary human tissue and non-cancerous cell lines while many model cancer cell lines demonstrated higher overall DNA methylation levels. This observation was expected, given that the amplicons selected for analysis were predominantly from promoter regions of genes known to be hypermethylated in cancer (Additional file 5: Table S2).
Methpat analysis of DNA methylation demonstrates a wide diversity of DNA methylation patterns
DNA methylation of FOXP3 in primary haematopoietic cells
Methpat can visualise imprinted loci
Methpat visualisation of gene promoters associated with cancer
Methpat visualisation of mitochondrial genome DNA methylation
Alternative DNA methylation Analysis and Visualisation Tools
Program Language and Implementation
Experiment Quality Check
Python, pip install, URL available to install files locally
Summarises Bismark output
Interactive HTML and summary text file of epiallele counts. Scalable PNG file
Bismark methylation extractor output, user-defined BED format file
HTML and tab delimited text file
No, leverages Bismark
command line,Python, requires bwa
Performs alignment to bisulfite reference genome
None, generates BAM files for visualisation with SeqMonk or IGV
BAM and tab deliminted text files
Yes calculates C to T conversion
Java/JSP web interface
Visualisation and summarisation of Bismark output
PNG file and UCSC Genome Browser file
Bismark output, fastq files
Text file summary, PNG and UCSC Genome Browser BED file
R library, Bioconductor
Calculates probabilities that epialleles are true
R image outputs
Table of read counts from bisulfite sequencing data
Derived statistics and plots
R library, shiny interactive web application
Visualises beta DNA methylation values
Interactive webpage with setting options to adjust a static image of DNA methylation values for each sample. PNG and PDF output.
Text file containing matrix of sample vs beta value at each CpG of interest
PDF and PNG image file
R library, Bioconductor
Processes summary data from other software for visualisation
Interactive HTML and UCSC Genome browser track hub files. PNG files
R library, Webserver for analysis
For EWAS studies. Analyses derived matrix files
Image files of plots with genomic locations.
Text matrix files
Methpat operates on Bismark output files and further summarizes this data into an interactive visualization that can be quickly interpreted within a web-browser. It can be executed locally to generate an HTML file which can be hosted remotely through the Internet or visualized locally on the most common web browsers (Chrome, Safari, Firefox, Internet Explorer). This feature which is unique to Methpat, is a major advantage. At this stage, Methpat does not have capability as a “genome-browser” to look at DNA methylation patterns at a genome-scale because it was designed for targeted deep sequencing of amplicons, however, we have made the source code available for further development by the research community to further improve Methpat (http://bjpop.github.io/methpat/).
We demonstrated the importance of calculating epiallelic abundance on the imprinted locus MEST, where we showed two predominant populations of epiallelic DNA methylation patterns, one completely methylated and the other completely unmethylated. Such patterns cannot be interpreted with percentage values at each CpG site as heterogeneous DNA methylation or, a sample containing a heterogeneous population of cells with variable DNA methylation states could give rise to the same percentage value . Using Methpat to visualise the diversity of epialleles enables the inference at least of the existence of heterogeneous DNA methylation, or, the detection of heterogeneous populations of cells as demonstrated by investigating FOXP3 in whole blood and subpopulations of the haematopoietic compartment.
Of interest, in some model cancer cell lines, we observed a wide and diverse range of methylated epialleles. Having ruled out to the best of our ability any bisulfite conversion or PCR amplification artefacts, our results suggest that even within apparently homogeneous cell lines, the methylation state at a subset of gene promoters analysed is heterogeneous. This could be due to the nature of cell culture where the phenomenon of increasing DNA methylation is observed with increasing passage [40, 41], plasticity, or the setting of epigenetic memory of a sub-population of cells in the culture . The detection of completely methylated epialleles of the CDKN2A gene promoter in whole blood and in other samples interrogated supports the validity of our approach, and indicates that Methpat provides a new tool to enable the detection of low level DNA methylation [43, 44]. The functional and biological implications of our current findings remain unclear, however, further investigation with appropriate specimens using Methpat is warranted.
We investigated mitochondrial DNA methylation and believe our analysis is one of the first accounts of characterising epiallelic DNA methylation within the D-loop regulatory region of the mitochondrial genome. Our study confirms observations of DNA methylation within the mitochondria [37–39]. Given there can be many thousands of copies of the mitochondrial genome per cell, it is not possible at this stage to determine the providence of the methylation states we have identified. The issue of heteroplasmy for mutations in the mitochondrial genome  apply for DNA methylation and techniques to address heteroplasmy could be applied to investigate DNA methylation within the mitochondrial genome further . By visualising DNA methylation patterns within the mitochondrial genome, Methpat can facilitate insight towards new biomarkers of disease .
While our current strategy and experimental results are unable to resolve PCR amplification artefacts (over-representation of particular sequence reads because of amplification), incorporation of unique molecular identifiers  could resolve this in future studies.
In summary, we demonstrate the feasibility of multiplex bisulfite amplicon deep sequencing to identify the extent of DNA methylation epialleles in a range of human samples. We have developed a software tool, called Methpat, which enables the summarisation and visualisation of DNA methylation sequencing data in the context of epiallelic information.
Availability of data and materials
The raw amplicon sequencing data, Bismark alignments and Methpat output files associated with this manuscript have been published with the DOI https://doi.org/10.1186/s13742-015-0098-x.
Methpat software can be obtained from this URL. (http://bjpop.github.io/methpat/)
Illumina Australia Pty Ltd for a MiSeq Pilot Sequencing Grant for next generation sequencing reagents.
This work was supported, in part, by National Breast Cancer Foundation of Australia (NCBF) grants to AD, DK and MT (CG-08-07, CG-10-04 and CG-12-07), the Cancer Council of Victoria to AD, and by grants from the Victorian Cancer Agency to NW and AD. SW was supported by the Melbourne Melanoma Project funded by the Victorian Cancer Agency Translational Research program and established through support of the Victor Smorgon Charitable Fund. Computation time was granted by the Life Sciences Computation Centre (LSCC) at the Victorian Life Sciences Computational Initiative (VLSCI) under grant VR0002. The Murdoch Childrens Research Institute and the Olivia Newton-John Cancer Research Institute are supported by the Victorian Government Operational and Infrastructure Support Grant.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
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