bims-dinmec Biomed News
on DNA methylation in cancer
Issue of 2025–09–28
two papers selected by
Lorena Ancona, Humanitas Research



  1. BMC Bioinformatics. 2025 Sep 23. 26(1): 229
       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.
    Keywords:  Chromosomal aberration; Copy number variation; Cumulative CNV; DNA methylation; EPIC; EPICv2; High-throughput; Segmentation
    DOI:  https://doi.org/10.1186/s12859-025-06269-z
  2. Nucleic Acids Res. 2025 Sep 23. pii: gkaf952. [Epub ahead of print]
      OncoDB was initially developed to advance cancer research by integrating RNA expression profiles, DNA methylation patterns, clinical annotations, and oncoviral signatures derived from the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression datasets. We now present OncoDB 2.0, an extensively expanded platform that offers a more comprehensive and integrated view of cancer omics. The updated version includes an atlas of somatic mutations discovered from combined DNA and RNA sequencing, enabling in-depth investigation of mutation patterns across tumor types and their association with clinical features. Furthermore, we have integrated proteomic data from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and chromatin accessibility data from TCGA, offering new dimensions for oncogene regulation studies. OncoDB 2.0 also introduces advanced multi-omics analysis modules that facilitate the combined exploration of RNA expression, DNA methylation, and somatic mutations, allowing researchers to examine complex cross-omic relationships with greater depth and flexibility. Together, these enhancements make OncoDB 2.0 a robust and invaluable tool for the cancer research community. OncoDB 2.0 is freely available at https://oncodb.org.
    DOI:  https://doi.org/10.1093/nar/gkaf952