dc.contributor.author | Nikolaienko, Oleksii | |
dc.contributor.author | Lønning, Per Eystein | |
dc.contributor.author | Knappskog, Stian | |
dc.date.accessioned | 2024-08-05T09:37:47Z | |
dc.date.available | 2024-08-05T09:37:47Z | |
dc.date.created | 2023-12-04T13:16:54Z | |
dc.date.issued | 2023 | |
dc.identifier.issn | 2047-217X | |
dc.identifier.uri | https://hdl.handle.net/11250/3144391 | |
dc.description.abstract | Low-level mosaic epimutations within the BRCA1 gene promoter occur in 5–8% of healthy individuals and are associated with a significantly elevated risk of breast and ovarian cancer. Similar events may also affect other tumor suppressor genes, potentially being a significant contributor to cancer burden. While this opens a new area for translational research, detection of low-level mosaic epigenetic events requires highly sensitive and robust methodology for methylation analysis. We here present epialleleR, a computational framework for sensitive detection, quantification, and visualization of mosaic epimutations in methylation sequencing data. Analyzing simulated and real data sets, we provide in-depth assessments of epialleleR performance and show that linkage to epihaplotype data is necessary to detect low-level methylation events. The epialleleR is freely available at https://github.com/BBCG/epialleleR and https://bioconductor.org/packages/epialleleR/ as an open-source R/Bioconductor package. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Oxford University Press | en_US |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.title | epialleleR: an R/Bioconductor package for sensitive allele-specific methylation analysis in NGS data | en_US |
dc.type | Journal article | en_US |
dc.type | Peer reviewed | en_US |
dc.description.version | publishedVersion | en_US |
dc.rights.holder | Copyright 2023 The Author(s) | en_US |
dc.source.articlenumber | giad087 | en_US |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |
dc.identifier.doi | 10.1093/gigascience/giad087 | |
dc.identifier.cristin | 2208470 | |
dc.source.journal | GigaScience | en_US |
dc.identifier.citation | GigaScience. 2023, 12, giad087. | en_US |
dc.source.volume | 12 | en_US |