LAUSR

Abstract RNA methylation has emerged recently as an active research domain to study post-transcriptional alteration in gene expression regulation. Various types of RNA methylation, including N6-methyladenosine (m6A), are involved in human disease development. As a newly developed sequencing biotechnology to quantify the m6A level on a transcriptome-wide scale, MeRIP-seq expands RNA epigenetics study in both basic and clinical applications, with an upward trend. One of the fundamental questions in RNA methylation data analysis is to identify the Differentially Methylated Regions (DMRs), by contrasting cases and controls. Multiple statistical approaches have been recently developed for DMR detection, but there is a lack of a comprehensive evaluation for these analytical methods. Here, we thoroughly assess all eight existing methods for DMR calling, using both synthetic and real data. Our simulation adopts a Gamma–Poisson model and logit linear framework, and accommodates various sample sizes and DMR proportions for benchmarking. For all methods, low sensitivities are observed among regions with low input levels, but they can be drastically boosted by an increase in sample size. TRESS and exomePeak2 perform the best using metrics of detection precision, FDR, type I error control and runtime, though hampered by low sensitivity. DRME and exomePeak obtain high sensitivities, at the expense of inflated FDR and type I error. Analyses on three real datasets suggest differential preference on identified DMR length and uniquely discovered regions, between these methods.