MethPat: a tool for the analysis and visualisation of complex methylation patterns obtained by massively parallel sequencing
Nicholas C Wong, Bernard J Pope, Ida L Candiloro, Darren Korbie, Matt Trau, Stephen Q Wong, Thomas Mikeska, Xinmin Zhang, Mark Pitman, Stefanie Eggers, Stephen R Doyle, Alexander Dobrovic
BMC BIOINFORMATICS | BMC | Published : 2016
BACKGROUND: 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 heterogeneit..View full abstract
Awarded by National Breast Cancer Foundation of Australia (NCBF)
Awarded by Life Sciences Computation Centre (LSCC) at the Victorian Life Sciences Computational Initiative (VLSCI)
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.