A novel statistical method for modeling covariate effects in bisulfite sequencing derived measures of DNA methylation.

ABSTRACT: Identifying disease-associated changes in DNA methylation can help us gain a better understanding of disease etiology. Bisulfite sequencing allows the generation of high-throughput methylation profiles at single-base resolution of DNA. However, optimally modeling and analyzing these sparse and discrete sequencing data is still very challenging due to variable read depth, missing data patterns, long-range correlations, data errors, and confounding from cell type mixtures. We propose a regression-based hierarchical model that allows covariate effects to vary smoothly along genomic positions and we have built a specialized EM algorithm, which explicitly allows for experimental errors and cell type mixtures, to make inference about smooth covariate effects in the model. Simulations show that t

SUBMITTER: Zhao K 

PROVIDER: S-EPMC8359306 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature