Wemics: A Single-Base Resolution Methylation Quantification Method for Enhanced Prediction of Epigenetic Regulation.
Yi LiuJiani YiPin WuJun ZhangXufan LiJia LiLiyuan ZhouYong LiuHaiming XuEnguo ChenHonghe ZhangMingyu LiangPengyuan LiuXiaoqing PanYan LuPublished in: Advanced science (Weinheim, Baden-Wurttemberg, Germany) (2024)
DNA methylation, an epigenetic mechanism that alters gene expression without changing DNA sequence, is essential for organism development and key biological processes like genomic imprinting and X-chromosome inactivation. Despite tremendous efforts in DNA methylation research, accurate quantification of cytosine methylation remains a challenge. Here, a single-base methylation quantification approach is introduced by weighting methylation of consecutive CpG sites (Wemics) in genomic regions. Wemics quantification of DNA methylation better predicts its regulatory impact on gene transcription and identifies differentially methylated regions (DMRs) with more biological relevance. Most Wemics-quantified DMRs in lung cancer are epigenetically conserved and recurrently occurred in other primary cancers from The Cancer Genome Atlas (TCGA), and their aberrant alterations can serve as promising pan-cancer diagnostic markers. It is further revealed that these detected DMRs are enriched in transcription factor (TF) binding motifs, and methylation of these TF binding motifs and TF expression synergistically regulate target gene expression. Using Wemics on epigenomic-transcriptomic data from the large lung cancer cohort, a dozen novel genes with oncogenic potential are discovered that are upregulated by hypomethylation but overlooked by other quantification methods. These findings increase the understanding of the epigenetic mechanism by which DNA methylation regulates gene expression.
Keyphrases
- dna methylation
- genome wide
- gene expression
- copy number
- transcription factor
- single cell
- papillary thyroid
- dna binding
- genome wide identification
- single molecule
- rna seq
- poor prognosis
- big data
- cell free
- binding protein
- high resolution
- squamous cell carcinoma
- electronic health record
- risk assessment
- climate change
- childhood cancer
- quality improvement