Identifying Differential Methylation in Cancer Epigenetics via a Bayesian Functional Regression Model.
Farhad ShokoohiDavid A StephensCelia M T GreenwoodPublished in: Biomolecules (2024)
DNA methylation plays an essential role in regulating gene activity, modulating disease risk, and determining treatment response. We can obtain insight into methylation patterns at a single-nucleotide level via next-generation sequencing technologies. However, complex features inherent in the data obtained via these technologies pose challenges beyond the typical big data problems. Identifying differentially methylated cytosines (dmc) or regions is one such challenge. We have developed DMCFB, an efficient dmc identification method based on Bayesian functional regression, to tackle these challenges. Using simulations, we establish that DMCFB outperforms current methods and results in better smoothing and efficient imputation. We analyzed a dataset of patients with acute promyelocytic leukemia and control samples. With DMCFB, we discovered many new dmcs and, more importantly, exhibited enhanced consistency of differential methylation within islands and their adjacent shores. Additionally, we detected differential methylation at more of the binding sites of the fused gene involved in this cancer.
Keyphrases
- genome wide
- dna methylation
- big data
- copy number
- papillary thyroid
- artificial intelligence
- machine learning
- gene expression
- squamous cell
- signaling pathway
- bone marrow
- lymph node metastasis
- squamous cell carcinoma
- molecular dynamics
- childhood cancer
- transcription factor
- cell free
- circulating tumor
- genome wide analysis
- circulating tumor cells