DNA　methylation　has　long　been　known　as　an　epigenetic　gene　silencing　mechanism.　For　a　motivating　example,　the　methylomes　of　cancer　and　non-cancer　cells　show　a　number　of　methylation　differences,　indicating　that　some　of　cancer　cells　＇　aggressive　properties　could　be　a　result　of　certain　methylation　features.　Robust　methods　for　detecting　differentially　methylated　regions　(DMRs)　could　help　scientists　narrow　down　biologically　important　regions　in　the　genome.　Despite　the　number　of　statistical　methods　developed　for　detecting　DMR,　there　is　no　default　or　strongest　method.　Fisher＇s　exact　test　is　direct,　but　is　inadequate　for　data　with　multiple　replications,　while　regressionbased　methods　often　come　with　a　large　number　of　assumptions.　More　complicated　methods　have　been　proposed,　but　those　are　often　difficult　to　interpret.　In　this　paper,　we　propose　a　three-step　nonparametric　kernel　smoothing　method　that　is　both　flexible　and　straightforward　to　implement　and　interpret.　The　proposed　method　relies　on　local　quadratic　fitting　method　to　find　the　set　of　equilibrium　points　(points　at　which　the　first　derivative　is　0)　and　the　corresponding　set　of　confidence　windows.　The　potential　regions　are　further　refined　using　biological　criteria,　and　selected　based　on　a　Bonferroni　adjusted　t-test　cutoff.　Using　a　comparison　of　three　senescent　and　three　proliferating　cell　lines　to　illustrate　our　method,　we　were　able　to　identify　a　total　of　1,077　DMRs　on　chromosome　21.　©　2020　IEEE.