10.18710/ED8HSDNikolaienko, OleksiiOleksiiNikolaienko0000-0002-5910-4934University of BergenDataverseNO2020Computer and Information ScienceMedicine, Health and Life SciencesramrDNA methylationComputational BiologyEpigenomicsNikolaienko, OleksiiOleksiiNikolaienkoUniversity of BergenUniversity of BergenUniversity of BergenUniversity of Bergen2020-11-242023-09-28program source code10.1093/bioinformatics/btab58610.1101/2020.12.01.403501163174455462152132216513405380541163308527013525204550718813500891754920954202724861447673769678735089816031433102231391933936896079436896118536895974136894660436892614178967096108371652761024701036852983689464903689465393689473533689350683689160811977100023796620074099337046951text/plainapplication/jsonapplication/gzipapplication/gzipapplication/gzipapplication/gzipapplication/octet-streamapplication/gziptype/x-r-syntaxtype/x-r-syntaxtype/x-r-syntaxtype/x-r-syntaxtype/x-r-syntaxapplication/gziptext/tab-separated-valuesapplication/pdftype/x-r-syntaxtype/x-r-syntaxtype/x-r-syntaxtype/x-r-syntaxtype/x-r-syntaxapplication/gzipapplication/gzipapplication/gzipapplication/gzipapplication/gzipapplication/pdfapplication/gziptext/tab-separated-valuesapplication/pdfapplication/pdfapplication/pdfapplication/gzipapplication/gzipapplication/gzipapplication/gzipapplication/gzipapplication/gzipapplication/gzipapplication/gzipapplication/pdf2.2CC0 1.0<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>R, 3.6.3comb-p, 0.50.3ramr, 1.1.2