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