{"@context":{"@language":"en","@vocab":"https://schema.org/","citeAs":"cr:citeAs","column":"cr:column","conformsTo":"dct:conformsTo","cr":"http://mlcommons.org/croissant/","rai":"http://mlcommons.org/croissant/RAI/","data":{"@id":"cr:data","@type":"@json"},"dataType":{"@id":"cr:dataType","@type":"@vocab"},"dct":"http://purl.org/dc/terms/","ddi-stats":"http://rdf-vocabulary.ddialliance.org/cv/SummaryStatisticType/2.1.2/","examples":{"@id":"cr:examples","@type":"@json"},"extract":"cr:extract","field":"cr:field","fileProperty":"cr:fileProperty","fileObject":"cr:fileObject","fileSet":"cr:fileSet","format":"cr:format","includes":"cr:includes","isLiveDataset":"cr:isLiveDataset","jsonPath":"cr:jsonPath","key":"cr:key","md5":"cr:md5","parentField":"cr:parentField","path":"cr:path","recordSet":"cr:recordSet","references":"cr:references","regex":"cr:regex","repeated":"cr:repeated","replace":"cr:replace","samplingRate":"cr:samplingRate","sc":"https://schema.org/","separator":"cr:separator","source":"cr:source","subField":"cr:subField","transform":"cr:transform"},"@type":"sc:Dataset","conformsTo":"http://mlcommons.org/croissant/1.1","name":"Replication Data for: "ramr: an R package for detection of rare aberrantly methylated regions"","url":"https://doi.org/10.18710/ED8HSD","creator":[{"@type":"Person","givenName":"Oleksii","familyName":"Nikolaienko","affiliation":{"@type":"Organization","name":"University of Bergen"},"sameAs":"https://orcid.org/0000-0002-5910-4934","@id":"https://orcid.org/0000-0002-5910-4934","identifier":"https://orcid.org/0000-0002-5910-4934","name":"Nikolaienko, Oleksii"}],"description":"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 ramr (https://github.com/BBCG/ramr, http://www.bioconductor.org/packages/ramr/) - 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 ramr package README.md file, vignettes or reference citation. Please use TREE VIEW to browse files efficiently\nAbstract 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. We have developed a method (ramr) to identify aberrantly methylated DNA regions (AMRs). ramr 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 ramr and confirmed its applicability for analysis of large public data sets. Using ramr 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.","keywords":["Computer and Information Science","Medicine, Health and Life Sciences","ramr","DNA methylation","Computational Biology","Epigenomics"],"license":"http://creativecommons.org/publicdomain/zero/1.0","datePublished":"2020-11-30","dateModified":"2023-09-28","includedInDataCatalog":{"@type":"DataCatalog","name":"DataverseNO","url":"https://dataverse.no"},"publisher":{"@type":"Organization","name":"DataverseNO"},"version":"2.2","citeAs":"@data{ED8HSD_2020,author = {Nikolaienko, Oleksii},publisher = {DataverseNO},title = {Replication Data for: \"ramr: an R package for detection of rare aberrantly methylated regions\"},year = {2020},url = {https://doi.org/10.18710/ED8HSD}}","citation":[{"@type":"CreativeWork","name":"Oleksii Nikolaienko, Per Eystein Lønning, Stian Knappskog, ramr: an R/Bioconductor package for detection of rare aberrantly methylated regions, Bioinformatics, 2021;, btab586","@id":"https://doi.org/10.1093/bioinformatics/btab586","identifier":"https://doi.org/10.1093/bioinformatics/btab586","url":"https://doi.org/10.1093/bioinformatics/btab586"},{"@type":"CreativeWork","name":"ramr: an R package for detection of rare aberrantly methylated regions. Oleksii Nikolaienko, Per Eystein Lønning, Stian Knappskog. bioRxiv 2020.12.01.403501","@id":"https://doi.org/10.1101/2020.12.01.403501","identifier":"https://doi.org/10.1101/2020.12.01.403501","url":"https://doi.org/10.1101/2020.12.01.403501"}],"distribution":[{"@type":"cr:FileObject","@id":"00-README.txt","name":"00-README.txt","encodingFormat":"text/plain","md5":"eb13e96e7bbfc83eb3e63272dbc11b1d","contentSize":"16317","description":"The README file","contentUrl":"https://dataverse.no/api/access/datafile/:persistentId?persistentId=doi:10.18710/ED8HSD/M7MHZB"},{"@type":"cr:FileObject","@id":"data/GSE105018/GSE105018.data.Rdata","name":"GSE105018.data.Rdata","encodingFormat":"application/gzip","md5":"fa05d2ac06de7cb282a47b1830319f70","contentSize":"5380541163","description":"An R data file containing the following objects: 1. geo.ranges: a GRanges object containing beta values for 430802 CpGs across 1658 samples from GSE105018 Illumina HumanMethylation 450 data set (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE105018). 2. sample.info: a list containing sample metadata for all samples in the GSE105018 data set. 3. sample.ids: a character vector of sample IDs.","contentUrl":"https://dataverse.no/api/access/datafile/:persistentId?persistentId=doi:10.18710/ED8HSD/JYV30Q"},{"@type":"cr:FileObject","@id":"data/GSE51032/GSE51032.data.Rdata","name":"GSE51032.data.Rdata","encodingFormat":"application/gzip","md5":"3bca6c45a39974282b261a4e1d69c88a","contentSize":"3085270135","description":"An R data file containing the following objects: 1. geo.ranges: a GRanges object containing beta values for 485512 CpGs across 845 samples from GSE51032 Illumina HumanMethylation 450 data set (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE51032). 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This data set was used to find epimutations undergoing positive selection during carcinogenesys.","contentUrl":"https://dataverse.no/api/access/datafile/:persistentId?persistentId=doi:10.18710/ED8HSD/2QT3OB"},{"@type":"cr:FileObject","@id":"data/TCGA-COAD/tcga.coad.AMRs.Rdata","name":"tcga.coad.AMRs.Rdata","encodingFormat":"application/gzip","md5":"b07cf6d556a8cb446028d58fca583ca8","contentSize":"197710002","description":"An R data file containing the following objects: 1. tcga.coad.ranges: GRanges object with TCGA-COAD subset of methylation beta values for adjacent and tumour tissue pairs. 2. tcga.coad.ramr.hg19: GRanges object with aberrantly methylated regions found in adjacent TCGA-COAD tissue samples.","contentUrl":"https://dataverse.no/api/access/datafile/:persistentId?persistentId=doi:10.18710/ED8HSD/B8WZTI"},{"@type":"cr:FileObject","@id":"R/PoC.results.GSE105018.R","name":"PoC.results.GSE105018.R","encodingFormat":"type/x-r-syntax","md5":"18ca9c6334dbfdf28ea192ce4c1ac37b","contentSize":"17549","description":"an R script to reproduce all findings from GSE105018 data set.","contentUrl":"https://dataverse.no/api/access/datafile/:persistentId?persistentId=doi:10.18710/ED8HSD/W5EREX"},{"@type":"cr:FileObject","@id":"R/PoC.results.GSE51032.R","name":"PoC.results.GSE51032.R","encodingFormat":"type/x-r-syntax","md5":"e63a711a730716f94e45c8f3590ac427","contentSize":"20954","description":"an R script to reproduce all findings from GSE51032 data set.","contentUrl":"https://dataverse.no/api/access/datafile/:persistentId?persistentId=doi:10.18710/ED8HSD/BX8E2A"},{"@type":"cr:FileObject","@id":"R/PoC.results.SIMULATED.R","name":"PoC.results.SIMULATED.R","encodingFormat":"type/x-r-syntax","md5":"8d0ec67128de120205491f21593039c5","contentSize":"20272","description":"an R script that evaluates method performance for ramr and other methods across all test scenarios. 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