A novel approach toward optimal workflow selection for DNA methylation biomarker discovery.

ABSTRACT: DNA methylation is a major epigenetic modification involved in many physiological processes. Normal methylation patterns are disrupted in many diseases and methylation-based biomarkers have shown promise in several contexts. Marker discovery typically involves the analysis of publicly available DNA methylation data from high-throughput assays. Numerous methods for identification of differentially methylated biomarkers have been developed, making the need for best practices guidelines and context-specific analyses workflows exceedingly high. To this end, here we propose TASA, a novel method for simulating methylation array data in various scenarios. We then comprehensively assess different data analysis workflows using real and simulated data and suggest optimal start-to-finish analysis wor

SUBMITTER: Nazer N 

PROVIDER: S-EPMC10804576 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature