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.
Gene expression from bulk RNA-seq studies is an average measurement between two chromosomes and across cell populations. Both allelic and cell-to-cell heterogeneity in gene expression results from promoter bursting patterns that repeatedly alternate between an activated and inactivated state. Increased cell-to-cell heterogeneity in gene expression has been shown as a hallmark of aging, which is potentially induced by the change of promoter bursting patterns. Despite their importance in humans, bursting kinetics studies have been studied only in a limited number of genes within selected model organisms due to technical restrictions in measuring multiple transcript levels over time. Here, we construct a transcriptomic kinetics map using single-cell RNA-seq (scRNA-seq) data and systematically investigate the regulatory effect of genetic variants and transcription factor (TF) binding on transcriptional kinetics. We obtain allelic level expression from scRNA-seq data with phased genotypes for two clonal cell lines and estimate transcriptional bursting kinetics for a single chromosome. We found that among transcriptional kinetics, the transcription initiation rate and burst frequency correlate most with eQTL effect sizes from bulk RNA-seq studies, suggesting that eQTLs affect average gene expression mainly through altering the transcription initiation rate and burst frequency. Notably, eQTL studies focused on mean expression changes cannot identify scenarios where burst size and frequency are regulated in opposite directions, which is common amongst genes from LCL. We further found that ~90% of the variance of burst frequency can be explained by TF occupancy within the core promoter and allele-specific binding events of individual TFs are typically associated with the change of transcription initiation rate and burst frequency. Finally, we demonstrate how a genetic variant alters TF binding, resulting in changes to promoter burst kinetics of HLA-DQA1 to influence multiple hematological traits.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.