Replication Data for: "ramr: an R package for detection of rare aberrantly methylated regions"https://doi.org/10.18710/ED8HSDNikolaienko, OleksiiDataverseNO2020-11-302023-09-28T20:53:58Z<p>This data set contains all the necessary data sets (biologically-relevant simulated data sets, preprocessed public data sets) used to evaluate performance and obtain results using <i>ramr</i> (<a href="https://github.com/BBCG/ramr">https://github.com/BBCG/ramr</a>, <a href="http://www.bioconductor.org/packages/ramr/">http://www.bioconductor.org/packages/ramr/</a>) - a new method for identification of aberrantly methylated regions (AMRs). All the necessary R scripts that were used for preparation, testing and analysis of data sets are also provided. For additional information please check <i>ramr</i> package README.md file, vignettes or reference citation.</p> <p></p><p><b>Please use TREE VIEW to browse files efficiently</b></p><p></p><p><b>Abstract</b></p> <p>With recent advances in the field of epigenetics, the focus is widening from large and frequent disease- or phenotype-related methylation signatures to rare alterations transmitted mitotically or transgenerationally (constitutional epimutations). Merging evidence indicate that such constitutional alterations, albeit occurring at a low mosaic level, may confer risk of disease later in life. Given their inherently low incidence rate and mosaic nature, there is a need for bioinformatic tools specifically designed to analyse such events.</p> <p>We have developed a method (<i>ramr</i>) to identify aberrantly methylated DNA regions (AMRs). <i>ramr</i> can be applied to methylation data obtained by array or next-generation sequencing techniques to discover AMRs being associated with elevated risk of cancer as well as other diseases. We assessed accuracy and performance metrics of <i>ramr</i> and confirmed its applicability for analysis of large public data sets. Using <i>ramr</i> we identified aberrantly methylated regions that are known or may potentially be associated with development of colorectal cancer and provided functional annotation of AMRs that arise at early developmental stages.</p>Computer and Information ScienceMedicine, Health and Life SciencesramrDNA methylationComputational BiologyEpigenomicsEnglish<p>Oleksii Nikolaienko, Per Eystein Lønning, Stian Knappskog, <i>ramr</i>: an R/Bioconductor package for detection of rare aberrantly methylated regions, Bioinformatics, 2021;, btab586</p>, doi, 10.1093/bioinformatics/btab586, https://doi.org/10.1093/bioinformatics/btab586<p><i>ramr</i>: an R package for detection of rare aberrantly methylated regions. Oleksii Nikolaienko, Per Eystein Lønning, Stian Knappskog. bioRxiv 2020.12.01.403501</p>, doi, 10.1101/2020.12.01.403501, https://doi.org/10.1101/2020.12.01.4035012020-11-30Nikolaienko, Oleksii2020-11-24<a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE105018">GSE105018</a>: Whole blood DNA methylation profiles in participants of the Environmental Risk (E-Risk) Longitudinal Twin Study at age 18.<a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE51032">GSE51032</a>: This Series contains data from 845 participants (188 men and 657 women) in the EPIC-Italy cohort that was produced at the Human Genetics Foundation (HuGeF) in Turin, Italy.<a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE98149">GSE98149</a>: Reprogramming of H3K9me3-dependent heterochromatin during mammalian early embryo development [ChIP-seq].<a href="https://portal.gdc.cancer.gov/projects/TCGA-COAD">TCGA-COAD</a>: The Cancer Genome Atlas Colon Adenocarcinoma data.program source codemachine-readable datamachine-readable textCC0 1.0