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dc.contributor.authorWong, NC
dc.contributor.authorPope, BJ
dc.contributor.authorCandiloro, IL
dc.contributor.authorKorbie, D
dc.contributor.authorTrau, M
dc.contributor.authorWong, SQ
dc.contributor.authorMikeska, T
dc.contributor.authorZhang, X
dc.contributor.authorPitman, M
dc.contributor.authorEggers, S
dc.contributor.authorDoyle, SR
dc.contributor.authorDobrovic, A
dc.date.accessioned2021-02-05T01:03:00Z
dc.date.available2021-02-05T01:03:00Z
dc.date.issued2016-02-24
dc.identifierpii: 10.1186/s12859-016-0950-8
dc.identifier.citationWong, N. C., Pope, B. J., Candiloro, I. L., Korbie, D., Trau, M., Wong, S. Q., Mikeska, T., Zhang, X., Pitman, M., Eggers, S., Doyle, S. R. & Dobrovic, A. (2016). MethPat: a tool for the analysis and visualisation of complex methylation patterns obtained by massively parallel sequencing. BMC BIOINFORMATICS, 17 (1), https://doi.org/10.1186/s12859-016-0950-8.
dc.identifier.issn1471-2105
dc.identifier.urihttp://hdl.handle.net/11343/260252
dc.description.abstractBACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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.
dc.languageEnglish
dc.publisherBMC
dc.titleMethPat: a tool for the analysis and visualisation of complex methylation patterns obtained by massively parallel sequencing
dc.typeJournal Article
dc.identifier.doi10.1186/s12859-016-0950-8
melbourne.affiliation.departmentPaediatrics (RCH)
melbourne.affiliation.departmentSurgery (Austin & Northern Health)
melbourne.affiliation.department
melbourne.affiliation.facultyMedicine, Dentistry & Health Sciences
melbourne.source.titleBMC Bioinformatics
melbourne.source.volume17
melbourne.source.issue1
dc.rights.licenseCC BY
melbourne.elementsid1042102
melbourne.contributor.authorPope, Bernard
melbourne.contributor.authorCandiloro, Ida
melbourne.contributor.authorDobrovic, Alexander
melbourne.contributor.authorEGGERS, STEFANIE
melbourne.contributor.authorWong, Nicholas
dc.identifier.eissn1471-2105
melbourne.accessrightsOpen Access


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