Detection of differentially methylated regions in whole genome bisulfite sequencing data using local Getis-Ord statistics

• DOI: 10.1093/bioinformatics/btw497
• Publication Date: 2016-08-05T01:54:16Z
• Authors: Yalu Wen; Fushun Chen; Qingzheng Zhang; Yan Zhuang; Zhiguang Li
• Keywords: Bisulfite sequencing; Differentially methylated regions; Bisulfite; CpG site; Statistic

Motivation: DNA methylation is an important epigenetic modification that has essential role in gene regulation, cell differentiation and cancer development. Bisulfite sequencing a widely used technique to obtain genome-wide profiles, one of the key tasks analyzing bisulfite data detect differentially methylated regions (DMRs) among samples under different treatment conditions. Although numerous tools have been proposed single CpG site (DMC) between samples, methods for direct DMR detection, especially complex study designs, are largely limited. Results: We present new software, GetisDMR, detection. use beta-binomial regression model whole-genome data, where variations levels confounding effects accounted for. employ region-wise test statistic, which derived from local Getis-Ord statistics considers spatial correlation nearby sites, DMRs. Unlike existing methods, attempt infer DMRs DMCs based on empirical criteria, we provide statistical inference Through extensive simulations application two mouse datasets, demonstrate GetisDMR achieves better sensitivities, positive predictive values, more exact locations agreement with current biological knowledge. Availability Implementation: It available at https://github.com/DMU-lilab/GetisDMR. Contacts: [e-mail] or [e-mail] Supplementary information: Bioinformatics online.