A Bayesian hierarchical model to detect differentially methylated loci from single nucleotide resolution sequencing data

By Hao Feng, Karen N. Conneely, Hao Wu. Nucleic Acids Research, 2014

501 citations recorded by the source corpus.

DNA methylation is an important epigenetic modification that has essential roles in cellular processes including gene regulation, development and disease and is widely dysregulated in most types of cancer. Recent advances in sequencing technology have enabled the measurement of DNA methylation at single nucleotide resolution through methods such as whole-genome bisulfite sequencing and reduced representation bisulfite sequencing. In DNA methylation studies, a key task is to identify differences under distinct biological contexts, for example, between tumor and normal tissue. A challenge in sequencing studies is that the number of biological replicates is often limited by the costs of sequencing. The small number of replicates leads to unstable variance estimation, which can reduce accuracy to detect differentially methylated loci (DML). Here we propose a novel statistical method to detect DML when comparing two treatment groups. The sequencing counts are described by a lognormal-beta-binomial hierarchical model, which provides a basis for information sharing across different CpG sites. A Wald test is developed for hypothesis testing at each CpG site. Simulation results show that the proposed method yields improved DML detection compared to existing methods, particularly when the number of replicates is low. The proposed method is implemented in the Bioconductor package DSS.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/458c710edad89bdac80086da07a8e19dc2c6002f

How researchers see this paper

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In Genetics

1. Yongseok Park
2. Hao Wu
3. P. Molloy
4. Peng Jin
5. Zhaohui S. Qin
6. Karen N. Conneely
7. Hao Feng

In Molecular Biology

1. Yongseok Park
2. Hao Wu
3. P. Molloy
4. Peng Jin
5. Timothy J. Peters
6. Zhaohui S. Qin
7. M. Buckley
8. Reginald V Lord

Citations in the corpus

• Differential methylation analysis for BS-seq data under general experimental design (2016, methodology): “…several years, a number of statistical methods and software tools have been developed for identifying DML/DMRs from BS-seq data (Hansen et al., 2012; Akalin et al., 2012b; Benoukraf et al., 2013; Feng et al., 2014; Sun et al., 2014; Park et al., 2014; Stockwell et al., 2014; Saito et al., 2014).”
• De novo identification of differentially methylated regions in the human genome (2015, background): “Many DMR-finding methods are available to the bioinformatics community [25-41].”
• Detection of differentially methylated regions from whole-genome bisulfite sequencing data without replicates (2015, methodology): “Borrowing ideas from the methods developed for RNA-seq (16,17), an empirical Bayes estimation procedure has been proposed and demonstrated to have good performance (11).”