Antonia Gocke, Yannis Schumann, Jelena Navolić, Shweta Godbole, Melanie Schoof, Matthias Dottermusch, Julia E Neumann
{"title":"CCNV：一个用户友好的R包，可以对DNA甲基化数据进行大规模的累积拷贝数变异分析。","authors":"Antonia Gocke, Yannis Schumann, Jelena Navolić, Shweta Godbole, Melanie Schoof, Matthias Dottermusch, Julia E Neumann","doi":"10.1186/s12859-025-06269-z","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Copy number variation (CNV) analyses-often inferred from DNA-methylation data-depict alterations of DNA quantities across chromosomes and have improved tumour diagnostics and classification. For the analyses of larger case series, CNV-features of multiple samples have to be combined to reliably interpret tumour-type characteristics. Established workflows mainly focus on the analyses of singular samples and do not support scalability to high sample numbers. Additionally, only plots showing the frequency of the aberrations have been considered.</p><p><strong>Results: </strong>We present the Cumulative CNV (CCNV) R package, which combines established segmentation methods and a newly implemented algorithm for thorough and fast CNV analysis at unprecedented accessibility. Our work is the first to supplement well-interpretable CNV frequency plots with their respective intensity plots, as well as showcasing the first application of penalised least-squares regression to DNA methylation data. CCNV exceeded existing tools concerning computing time and displayed high accuracy for all available array types on simulated and real-world data, verified by our newly developed benchmarking method.</p><p><strong>Conclusions: </strong>CCNV is a user-friendly R package, which enables fast and accurate generation and analyses of cumulative copy number variation plots.</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":"26 1","pages":"229"},"PeriodicalIF":3.3000,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12459049/pdf/","citationCount":"0","resultStr":"{\"title\":\"CCNV: a user-friendly R package enabling large-scale cumulative copy number variation analyses of DNA methylation data.\",\"authors\":\"Antonia Gocke, Yannis Schumann, Jelena Navolić, Shweta Godbole, Melanie Schoof, Matthias Dottermusch, Julia E Neumann\",\"doi\":\"10.1186/s12859-025-06269-z\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Copy number variation (CNV) analyses-often inferred from DNA-methylation data-depict alterations of DNA quantities across chromosomes and have improved tumour diagnostics and classification. For the analyses of larger case series, CNV-features of multiple samples have to be combined to reliably interpret tumour-type characteristics. Established workflows mainly focus on the analyses of singular samples and do not support scalability to high sample numbers. Additionally, only plots showing the frequency of the aberrations have been considered.</p><p><strong>Results: </strong>We present the Cumulative CNV (CCNV) R package, which combines established segmentation methods and a newly implemented algorithm for thorough and fast CNV analysis at unprecedented accessibility. Our work is the first to supplement well-interpretable CNV frequency plots with their respective intensity plots, as well as showcasing the first application of penalised least-squares regression to DNA methylation data. CCNV exceeded existing tools concerning computing time and displayed high accuracy for all available array types on simulated and real-world data, verified by our newly developed benchmarking method.</p><p><strong>Conclusions: </strong>CCNV is a user-friendly R package, which enables fast and accurate generation and analyses of cumulative copy number variation plots.</p>\",\"PeriodicalId\":8958,\"journal\":{\"name\":\"BMC Bioinformatics\",\"volume\":\"26 1\",\"pages\":\"229\"},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2025-09-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12459049/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"BMC Bioinformatics\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1186/s12859-025-06269-z\",\"RegionNum\":3,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BIOCHEMICAL RESEARCH METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMC Bioinformatics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1186/s12859-025-06269-z","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
CCNV: a user-friendly R package enabling large-scale cumulative copy number variation analyses of DNA methylation data.
Background: Copy number variation (CNV) analyses-often inferred from DNA-methylation data-depict alterations of DNA quantities across chromosomes and have improved tumour diagnostics and classification. For the analyses of larger case series, CNV-features of multiple samples have to be combined to reliably interpret tumour-type characteristics. Established workflows mainly focus on the analyses of singular samples and do not support scalability to high sample numbers. Additionally, only plots showing the frequency of the aberrations have been considered.
Results: We present the Cumulative CNV (CCNV) R package, which combines established segmentation methods and a newly implemented algorithm for thorough and fast CNV analysis at unprecedented accessibility. Our work is the first to supplement well-interpretable CNV frequency plots with their respective intensity plots, as well as showcasing the first application of penalised least-squares regression to DNA methylation data. CCNV exceeded existing tools concerning computing time and displayed high accuracy for all available array types on simulated and real-world data, verified by our newly developed benchmarking method.
Conclusions: CCNV is a user-friendly R package, which enables fast and accurate generation and analyses of cumulative copy number variation plots.
期刊介绍：
BMC Bioinformatics is an open access, peer-reviewed journal that considers articles on all aspects of the development, testing and novel application of computational and statistical methods for the modeling and analysis of all kinds of biological data, as well as other areas of computational biology.
BMC Bioinformatics is part of the BMC series which publishes subject-specific journals focused on the needs of individual research communities across all areas of biology and medicine. We offer an efficient, fair and friendly peer review service, and are committed to publishing all sound science, provided that there is some advance in knowledge presented by the work.
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