Exemplary multiplex bisulfite amplicon data used to demonstrate the utility of Methpat. Issue 1 (December 2015)

• Record Type: Journal Article
• Authors: Wong, Nicholas; Pope, Bernard; Candiloro, Ida; Korbie, Darren; Trau, Matt; Wong, Stephen; Mikeska, Thomas; van Denderen, Bryce; Thompson, Erik; Eggers, Stefanie; Doyle, Stephen; Dobrovic, Alexander
• Is Part Of: GigaScience. Volume 4:Issue 1(2015)
• Page Start: 1
• Page End: 8
• Publication Date: 2015-12
• Subjects: DNA methylation -- Bisulfite sequencing -- PCR -- Visualization -- Epigenetics -- Cancer -- Epialleles; Information storage and retrieval systems -- Research -- Periodicals; Biology -- Research -- Periodicals; Medical sciences -- Research -- Periodicals; Database management -- Periodicals; 570.285
• DOI: 10.1186/s13742-015-0098-x
• Languages: English
• ISSNs: 2047-217X

Abstract

Abstract Background DNA methylation is a complex epigenetic marker that can be analyzed using a wide variety of methods. Interpretation and visualization of DNA methylation data can mask complexity in terms of methylation status at each CpG site, cellular heterogeneity of samples and allelic DNA methylation patterns within a given DNA strand. Bisulfite sequencing is considered the gold standard, but visualization of massively parallel sequencing results remains a significant challenge. Findings We created a program called Methpat that facilitates visualization and interpretation of bisulfite sequencing data generated by massively parallel sequencing. To demonstrate this, we performed multiplex PCR that targeted 48 regions of interest across 86 human samples. The regions selected included known gene promoters associated with cancer, repetitive elements, known imprinted regions and mitochondrial genomic sequences. We interrogated a range of samples including human cell lines, primary tumours and primary tissue samples. Methpat generates two forms of output: a tab-delimited text file for each sample that summarizes DNA methylation patterns and their read counts for each amplicon, and a HTML file that summarizes this data visually. Methpat can be used with publicly available whole genome bisulfite sequencing and reduced representation bisulfite sequencing datasets with sufficient read depths. Conclusions Using Methpat, complex DNA methylation data derived from massively parallel sequencing can be summarized and visualized for biological interpretation. By accounting for allelic DNA methylation states and their abundance in a sample, Methpat can unmask the complexity of DNA methylation and yield further biological insight in existing datasets.