A method for identifying differentially methylated regions. First, the data is divided into smaller segments based on genomic distance between consecutive probes. Then, each of these segments is divided into regions with consistent differential methylation patterns. For this, all possible segmentations are considered and the optimal one is chosen according to the minimum description length (MDL) principle. Finally, the significance of differential methylation in each region is assessed using linear mixed models. Using both simulated and large publicly available methylation datasets, we compare seqlm performance to alternative approaches. We demonstrate that it is both more sensitive and specific than competing methods. This is achieved with minimal parameter tuning and, surprisingly, quickest running time of all the tried methods. Finally, we show that the regional differential methylation patterns identified on sparse array data are confirmed by higher resolution sequencing approaches.