MethylMasteR: A Comparison and Customization of Methylation-Based Copy Number Variation Calling Software in Cancers Harboring Large Scale Chromosomal Deletions

Abstract

DNA methylation-based copy number variation (CNV) calling software offers the advantages of providing both genetic (copy-number) and epigenetic (methylation) state information from a single genomic library. This method is advantageous when looking at large-scale chromosomal rearrangements such as the loss of the short arm of chromosome 3 (3p) in renal cell carcinoma and the codeletion of the short arm of chromosome 1 and the long arm of chromosome 19 (1p/19q) commonly seen in histologically defined oligodendrogliomas. Herein, we present MethylMasteR: a software framework that facilitates the standardization and customization of methylation-based CNV calling algorithms in a single R package deployed using the Docker software framework. This framework allows for the easy comparison of the performance and the large-scale CNV event identification capability of four common methylation-based CNV callers. Additionally, we incorporated our custom routine, which was among the best performing routines. We employed the Affymetrix 6.0 SNP Chip results as a gold standard against which to compare large-scale event recall. As there are disparities within the software calling algorithms themselves, no single software is likely to perform best for all samples and all combinations of parameters. The employment of a standardized software framework via creating a Docker image and its subsequent deployment as a Docker container allows researchers to efficiently compare algorithms and lends itself to the development of modified workflows such as the custom workflow we have developed. Researchers can now use the MethylMasteR software for their methylation-based CNV calling needs and follow our software deployment framework. We will continue to refine our methodology in the future with a specific focus on identifying large-scale chromosomal rearrangements in cancer methylation data.

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

“…Whole-genome next generation sequencing and genotyping microarrays are considered gold standards for CNV analysis, as they provide the most extensive genome coverage ( Mariani et al 2022 ). Another genome-wide assay that is widely applied in biological research and clinical settings are DNA methylation microarrays (i.e.…”

Methods in DNA methylation array dataset analysis: A review

“…One of the primary advantages of DNA methylation-based CNV approaches is their ability to incorporate epigenomic (methylation) information and genomic (copy number) information. In 2022, Mariani and colleagues introduced MethylMasteR, an R software package that incorporates CNV calling algorithms based on DNA methylation, making it easier to standardize, compare, and customize CNV investigations [28] . MethylMasteR enables performance evaluation, comparing runtime and memory usage, and assessing the detection of large-scale CNVs in cancer samples using four well-known methylation-based CNV algorithms: ChAMP [24] , SeSAMe [29] , Epicopy [30] , and a modified version of cnAnalysis450k [31] .…”

Identification and verification of prognostic cancer subtype based on multi-omics analysis for kidney renal papillary cell carcinoma

“…At the same time, each omics data has its own advantages. For example, methylation chip data and lncRNA expression matrix have good tissue conservation, which can be used as efficient markers for the early diagnosis of specific tumor tissues ( 14 ). miRNA data are characterized by dissociation and can be used for non-invasive diagnosis and dynamic detection of disease ( 15 ).…”