Persistent Identifier
|
doi:10.18710/2BQTJP |
Publication Date
|
2022-05-05 |
Title
| Replication Data for: "epialleleR: an R/BioC package for quantifying and analysing low-frequency DNA methylation" |
Author
| Nikolaienko, Oleksii (University of Bergen) - ORCID: 0000-0002-5910-4934 |
Point of Contact
|
Use email button above to contact.
Nikolaienko, Oleksii (University of Bergen) |
Description
| This data set contains necessary data sets (simulated next-generation sequencing data, preprocessed public next-generation sequencing data, precomputed analysis results) used to evaluate performance of epialleleR (https://github.com/BBCG/epialleleR, http://www.bioconductor.org/packages/epialleleR/) - a computational framework for sensitive detection, quantification and visualisation of low-frequency, mosaic epimutations in methylation sequencing data. All the supplementary R scripts that were used for preparation, testing and analysis of data sets are also provided. For additional information please check epialleleR package README.md file, vignettes or reference citation.
Please use TREE VIEW to browse files efficiently (2022-04-28)
Abstract
Constitutional epigenetic silencing of tumour suppressor genes has been detected in a small number of cancer patients. Recent finding have indicated that low-level mosaic methylation of the BRCA1 gene promoter occurs in 5-10% of healthy individuals and is associated with a significantly elevated risk of breast and ovarian cancer. This further suggests that similar mosaic constitutional methylation may occur in other tumour suppressor genes as well, potentially being a significant contributor to cancer burden. However, 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 visualisation of low-frequency, mosaic epimutations in methylation sequencing data. We provide in-depth analysis of epialleleR performance using simulated and real data sets, as compared to the other three commonly applied tools for methylation assessment, and conclude that linkage to epihaplotype data allows very sensitive detection of low-frequency methylation events. (2022-04-28) |
Subject
| Medicine, Health and Life Sciences; Computer and Information Science |
Keyword
| epialleleR
DNA methylation
Computational Biology (MeSH) https://www.ncbi.nlm.nih.gov/mesh/68019295
Epigenomics (MeSH) https://www.ncbi.nlm.nih.gov/mesh/68057890 |
Related Publication
| epialleleR: an R/Bioconductor package for sensitive allele-specific methylation analysis in NGS data. Oleksii Nikolaienko, Per Eystein Lønning, Stian Knappskog. GigaScience, Volume 12, 2023, giad087 doi: 10.1093/gigascience/giad087 https://doi.org/10.1093/gigascience/giad087
epialleleR: an R/BioC package for sensitive allele-specific methylation analysis in NGS data. Oleksii Nikolaienko, Per Eystein Lønning, Stian Knappskog. bioRxiv 2022.06.30.498213 doi: 10.1101/2022.06.30.498213 https://doi.org/10.1101/2022.06.30.498213 |
Language
| English |
Producer
| University of Bergen (UiB) https://www.uib.no/en |
Distributor
| University of Bergen (University of Bergen) https://dataverse.no/dataverse/uib |
Depositor
| Nikolaienko, Oleksii |
Deposit Date
| 2022-04-28 |
Data Type
| program source code; machine-readable data; machine-readable text |
Software
| R, Version: 4.1.2
epialleleR, Version: 1.3.5
Sherman Bisulfite FastQ Read Simulator, Version: 0.1.8
Illumina DRAGEN Bio-IT Platform, Version: 3.9.5
methclone, Version: 1.0 |
Related Material
| epialleleR package code, version 1.3.5 |
Related Dataset
| GSE201690: Methylation analysis of promoter regions for selected tumour suppressor genes in DNA from white blood cells. |
Other Reference
| https://github.com/BBCG/epialleleR; http://www.bioconductor.org/packages/epialleleR/; https://doi.org/10.18129/B9.bioc.epialleleR |