Cover Image

Conumee 2.0: enhanced copy-number variation analysis from DNA methylation arrays for humans and mice

Copy-number variations (CNVs) are common genetic alterations in cancer and their detection may impact tumor classification and therapeutic decisions. However, detection of clinically relevant large and focal CNVs remains challenging when sample material or resources are limited. This has motivated u...

Full description

Saved in:
Bibliographic Details
Main Authors: Daenekas, Bjarne (Author) , Pérez, Eilís (Author) , Boniolo, Fabio (Author) , Stefan, Sabina (Author) , Benfatto, Salvatore (Author) , Sill, Martin (Author) , Sturm, Dominik (Author) , Jones, David T. W. (Author) , Capper, David (Author) , Zapatka, Marc (Author) , Hovestadt, Volker (Author)
Format: Article (Journal)
Language:English
Published: February 2024
In: Bioinformatics
Year: 2024, Volume: 40, Issue: 2, Pages: 1-11
ISSN:1367-4811
DOI:10.1093/bioinformatics/btae029
Online Access:Verlag, kostenfrei, Volltext: https://doi.org/10.1093/bioinformatics/btae029
Get full text
Author Notes:Bjarne Daenekas, Eilís Pérez, Fabio Boniolo, Sabina Stefan, Salvatore Benfatto, Martin Sill, Dominik Sturm, David T W Jones, David Capper, Marc Zapatka, Volker Hovestadt
Description
Summary:Copy-number variations (CNVs) are common genetic alterations in cancer and their detection may impact tumor classification and therapeutic decisions. However, detection of clinically relevant large and focal CNVs remains challenging when sample material or resources are limited. This has motivated us to create a software tool to infer CNVs from DNA methylation arrays which are often generated as part of clinical routines and in research settings.We present our R package, conumee 2.0, that combines tangent normalization, an adjustable genomic binning heuristic, and weighted circular binary segmentation to utilize DNA methylation arrays for CNV analysis and mitigate technical biases and batch effects. Segmentation results were validated in a lung squamous cell carcinoma dataset from TCGA (n = 367 samples) by comparison to segmentations derived from genotyping arrays (Pearson’s correlation coefficient of 0.91). We further introduce a segmented block bootstrapping approach to detect focal alternations that achieved 60.9% sensitivity and 98.6% specificity for deletions affecting CDKN2A/B (60.0% and 96.9% for RB1, respectively) in a low-grade glioma cohort from TCGA (n = 239 samples). Finally, our tool provides functionality to detect and summarize CNVs across large sample cohorts.Conumee 2.0 is available under open-source license at: https://github.com/hovestadtlab/conumee2.
Item Description:Veröffentlicht: 19. Januar 2024
Gesehen am 25.06.2024
Physical Description:Online Resource
ISSN:1367-4811
DOI:10.1093/bioinformatics/btae029

Similar Items