10.18710/ED8HSD Nikolaienko, Oleksii Oleksii Nikolaienko https://orcid.org/0000-0002-5910-4934 University of Bergen DataverseNO 2020 Computer and Information Science Medicine, Health and Life Sciences ramr DNA methylation Computational Biology Epigenomics University of Bergen University of Bergen University of Bergen Nikolaienko, Oleksii Oleksii Nikolaienko University of Bergen 2020-11-24 2020-11-30 2023-09-28 program source code;machine-readable data;machine-readable text 10.1093/BIOINFORMATICS/BTAB586 10.1101/2020.12.01.403501 10.18710/ED8HSD/M7MHZB 10.18710/ED8HSD/PN0JQI 10.18710/ED8HSD/GHXOQF 10.18710/ED8HSD/NRMCKB 10.18710/ED8HSD/JYV30Q 10.18710/ED8HSD/TN0SKC 10.18710/ED8HSD/E6IYWL 10.18710/ED8HSD/IQMCIN 10.18710/ED8HSD/W5EREX 10.18710/ED8HSD/BX8E2A 10.18710/ED8HSD/T2X1OF 10.18710/ED8HSD/4GC9KD 10.18710/ED8HSD/CKQOAK 10.18710/ED8HSD/XHCTWT 10.18710/ED8HSD/6UWG69 10.18710/ED8HSD/ZLD0KQ 10.18710/ED8HSD/O4NSIN 10.18710/ED8HSD/OHUKFT 10.18710/ED8HSD/KMKI2C 10.18710/ED8HSD/J7NL8C 10.18710/ED8HSD/ID3ZOQ 10.18710/ED8HSD/A39C1D 10.18710/ED8HSD/OASNJG 10.18710/ED8HSD/ZC3SL8 10.18710/ED8HSD/PS5UGE 10.18710/ED8HSD/Y6C3RG 10.18710/ED8HSD/YB4C4J 10.18710/ED8HSD/UJXO1U 10.18710/ED8HSD/X64KU6 10.18710/ED8HSD/XB7GAQ 10.18710/ED8HSD/XGR7SO 10.18710/ED8HSD/CFUARQ 10.18710/ED8HSD/QVDNUK 10.18710/ED8HSD/VGNS57 10.18710/ED8HSD/JZSZWE 10.18710/ED8HSD/HHFGXU 10.18710/ED8HSD/0ZEUYI 10.18710/ED8HSD/B8WZTI 10.18710/ED8HSD/2QT3OB 10.18710/ED8HSD/B3O7HR 10.18710/ED8HSD/CSTQJU 4861 17549 20954 20272 4476 4455462 197710002 379662007 73769 368946490 7046951 368916081 368935068 50898 67873 368946539 368947353 3085270135 5380541163 1521322 1881350089 1651340 409933 252045507 9721 368946604 165276 10368 368960794 1022 9339 102470 368926141 31391 1603 610837 368961185 7896709 1433 5298 368959741 16317 type/x-r-syntax type/x-r-syntax type/x-r-syntax type/x-r-syntax type/x-r-syntax application/json application/gzip application/gzip application/gzip application/gzip application/pdf application/gzip application/gzip application/pdf text/tab-separated-values application/gzip application/gzip application/gzip application/gzip application/gzip application/gzip application/gzip application/gzip application/octet-stream text/plain application/gzip text/tab-separated-values application/pdf application/gzip type/x-r-syntax type/x-r-syntax application/pdf application/gzip type/x-r-syntax type/x-r-syntax application/gzip application/gzip application/pdf type/x-r-syntax application/pdf application/gzip text/plain 2.2 Creative Commons CC0 1.0 Universal Public Domain Dedication. <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, 3.6.3 comb-p, 0.50.3 ramr, 1.1.2