CCNV: a user-friendly R package enabling large-scale cumulative copy number variation analyses of DNA methylation data.

IF 3.3 3区生物学 Q2 BIOCHEMICAL RESEARCH METHODS

BMC Bioinformatics Pub Date : 2025-09-23 DOI:10.1186/s12859-025-06269-z

Antonia Gocke, Yannis Schumann, Jelena Navolić, Shweta Godbole, Melanie Schoof, Matthias Dottermusch, Julia E Neumann

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引用次数: 0

Abstract

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.

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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。

背景：拷贝数变异（CNV）分析-通常从DNA甲基化数据推断-描述了染色体上DNA数量的改变，并改善了肿瘤的诊断和分类。对于大型病例系列的分析，必须将多个样本的cnv特征结合起来，以可靠地解释肿瘤类型特征。现有的工作流程主要集中在单一样本的分析上，不支持高样本数量的可扩展性。此外，仅考虑了显示畸变频率的图。结果：我们提出了累积CNV (CCNV) R包，它结合了已有的分割方法和一种新实现的算法，以前所未有的可及性进行全面快速的CNV分析。我们的工作是第一个用各自的强度图补充可解释的CNV频率图，以及首次将惩罚最小二乘回归应用于DNA甲基化数据。CCNV在计算时间方面超越了现有的工具，并在模拟和现实世界数据上显示了所有可用阵列类型的高精度，并通过我们新开发的基准测试方法进行了验证。结论：CCNV是一个用户友好的R软件包，可以快速准确地生成和分析累积拷贝数变异图。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

BMC Bioinformatics 生物-生化研究方法

CiteScore

5.70

自引率

3.30%

发文量

506

审稿时长

4.3 months

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.