MethylationToActivity: a deep-learning framework that reveals promoter activity landscapes from DNA methylomes in individual tumors.
Justin WilliamsBeisi XuDaniel PutnamAndrew ThrasherChunliang LiJun YangXiang ChenPublished in: Genome biology (2021)
Although genome-wide DNA methylomes have demonstrated their clinical value as reliable biomarkers for tumor detection, subtyping, and classification, their direct biological impacts at the individual gene level remain elusive. Here we present MethylationToActivity (M2A), a machine learning framework that uses convolutional neural networks to infer promoter activities based on H3K4me3 and H3K27ac enrichment, from DNA methylation patterns for individual genes. Using publicly available datasets in real-world test scenarios, we demonstrate that M2A is highly accurate and robust in revealing promoter activity landscapes in various pediatric and adult cancers, including both solid and hematologic malignant neoplasms.
Keyphrases
- dna methylation
- genome wide
- deep learning
- convolutional neural network
- machine learning
- artificial intelligence
- gene expression
- copy number
- circulating tumor
- single molecule
- cell free
- climate change
- transcription factor
- big data
- loop mediated isothermal amplification
- high resolution
- genome wide identification
- circulating tumor cells
- single cell