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Title: Supporting data and materials for "Exemplary multiplex bisulfite amplicon data used to demonstrate the utility of Methpat"      
dateReleased:
11-22-2015
privacy:
not applicable
aggregation:
instance of dataset
dateCreated:
11-22-2015
refinement:
curated
ID:
doi:10.5524/100167
creators:
Wong, Nicholas C
Pope, Bernard J
Candiloro, Ida
Korbie, Darren
Trau, Matt
Wong, Stephen Q
Mikeska, Thomas
Denderen, Bryce J Van
Thompson, Erik W
Eggers, Stefanie
Doyle, Stephen
Dobrovic, Alexander
availability:
available
types:
sequence
description:
DNA methylation is a complex epigenetic marker that can be analysed using a wide variety of methods. Interpretation and visualisation 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, however visualisation of massively parallel sequencing results remains a significant challenge. We created a program called Methpat that facilitates visualisation 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 summarises DNA methylation patterns and their read counts for each amplicon and a HTML file that summarises this data visually. Methpat can be used with publicly available whole genome bisulfite sequencing (WGBS) and reduced representation bisulfite sequencing (RRBS) datasets with sufficient read depths. Using Methpat, complex DNA methylation data derived from massively parallel sequencing can be summarised and visualised 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 reveal further biological insight in existing datasets.
accessURL: https://doi.org/10.5524/100167
storedIn:
GigaScience Database
qualifier:
not compressed
format:
HTML
accessType:
landing page
primary:
true
authentication:
none
authorization:
none
abbreviation:
GigaDB
homePage: http://gigadb.org/
ID:
SCR:006565
name:
Giga Science Database

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