MOTIVATION: m6A methylation is a highly prevalent post-transcriptional modification in eukaryotes. MeRIP-seq or m6A-seq, which comprises immunoprecipitation of methylation fragments , is the most common method for measuring methylation signals. Existing computational tools for analyzing MeRIP-seq data sets and identifying differentially methylated genes/regions are not most optimal. They either ignore the sparsity or dependence structure of the methylation signals within a gene/region. Modeling the methylation signals using univariate distributions could also lead to high type I error rates and low sensitivity. In this paper, we propose using mean vector testing (MVT) procedures for testing differential methylation of RNA at the gene level. MVTs use a distribution-free test statistic with proven ability to control type I error even for extremely small sample sizes. We performed a comprehensive simulation study comparing the MVTs to existing MeRIP-seq data analysis tools. Comparative analysis of existing MeRIP-seq data sets is presented to illustrate the advantage of using MVTs. RESULTS: Mean vector testing procedures are observed to control type I error rate and achieve high power for detecting differential RNA methylation using m6A-seq data. Results from two data sets indicate that the genes detected identified as having different m6A methylation patterns have high functional relevance to the study conditions. AVAILABILITY: The dimer software package for differential RNA methylation analysis is freely available at https://github.com/ouyang-lab/DIMER. SUPPLEMENTARY INFORMATION: Supplementary data are available at Briefings in Bioinformatics online.

BACKGROUND: RNA secondary structure has broad impact on the fate of RNA metabolism. The reduced stability of secondary structures near the translation initiation site/start codon of the coding region promotes the efficiency of translation in both prokaryotic and eukaryotic species. However, the inaccuracy of in silico folding and the focus on the coding region limit our understanding of the global relationship between the whole mRNA structure and translation efficiency. Leveraging high-throughput RNA structure probing data in the transcriptome, we aim to systematically investigate the role of RNA structure in regulating translation efficiency. RESULTS: Here, we analyze the influences of hundreds of sequence and structural features on translation efficiency in the mouse embryonic stem cells (mESCs) and zebrafish developmental stages. Our findings reveal that overall in vivo RNA structure has a higher relative importance in predicting translation efficiency than in vitro RNA structure in both mESCs and zebrafish. Also, RNA structures in 3' untranslated region (UTR) have much stronger influence on translation efficiency compared to those in coding regions or 5' UTR. Furthermore, strong alternation between in vitro and in vivo structures in 3' UTR are detected in highly translated mRNAs in mESCs but not zebrafish. Instead, moderate alteration between in vitro and in vivo RNA structures in the 5' UTR and proximal coding regions are detected in highly translated mRNAs in zebrafish. CONCLUSIONS: Our results suggest the openness of the 3' UTR promotes the translation efficiency in both mice and zebrafish, with the in vivo structure in 3' UTR more important in mice than in zebrafish. This reveals a novel role of RNA secondary structure on translational regulation.

RNA-protein interaction plays important roles in post-transcriptional regulation. Recent advancements in cross-linking and immunoprecipitation followed by sequencing (CLIP-seq) technologies make it possible to detect the binding peaks of a given RNA binding protein (RBP) at transcriptome scale. However, it is still challenging to predict the functional consequences of RBP binding peaks. In this study, we propose the Protein-RNA Association Strength (PRAS), which integrates the intensities and positions of the binding peaks of RBPs for functional mRNA targets prediction. We illustrate the superiority of PRAS over existing approaches on predicting the functional targets of two related but divergent CELF (CUGBP, ELAV-like factor) RBPs in mouse brain and muscle. We also demonstrate the potential of PRAS for wide adoption by applying it to the enhanced CLIP-seq (eCLIP) datasets of 37 RNA decay related RBPs in two human cell lines. PRAS can be utilized to investigate any RBPs with available CLIP-seq peaks. PRAS is freely available at http://ouyanglab.jax.org/pras/.