Statistical methods for detecting differentially methylated loci and regions

Frontiers in Genetics

Charity W Law¹  Mark D Robinson¹  Abdullah eKahraman¹  Malgorzata eNowicka¹  Lukas M Weber¹  Xiaobei eZhou¹  Helen eLindsay¹ 

[1] SIB Swiss Institute of Bioinformatics;University of Zurich;

关键词: DNA Methylation;    Epigenomics;    Genetics;    statistics;    bioinformatic tools and databases;    differentially methylated regions;   

DOI  :  10.3389/fgene.2014.00324

来源: DOAJ

【 摘 要 】

DNA methylation, the reversible addition of methyl groups at CpG dinucleotides, represents an important regulatory layer associated with gene expression. Changed methylation status has been noted across diverse pathological states, including cancer.The rapid development and uptake of microarrays and large scale DNA sequencing has prompted an explosion of data analytic methods for processing and discovering changes in DNA methylation across varied data types.In this mini-review, we present a compact and accessible discussion of many of the salient challenges, such as experimental design, statistical methods for differential methylation detection, critical considerations such as cell type composition and the potential confounding that can arise from batch effects.From a statistical perspective, our main interests include the use of empirical Bayes or hierarchical models, which have proved immensely powerful in genomics, and the procedures by which false discovery control is achieved.

【 授权许可】

Unknown