Unveiling the dynamics in RNA epigenetic regulations

Despite the prevalent studies of DNA/Chromatin related epigenetics, such as, histone modifications and DNA methylation, RNA epigenetics did not receive deserved attention due to the lack of high throughput approach for profiling epitranscriptome. Recently, a new affinity-based sequencing approach MeRIPseq was developed and applied to survey the global mRNA N6-methyladenosine (m ⁶ A) in mammalian cells. As a marriage of ChIPseq and RNAseq, MeRIPseq has the potential to study, for the first time, the transcriptome-wide distribution of different types of post-transcriptional RNA modifications. Yet, this technology introduced new computational challenges that have not been adequately addressed. We have previously developed a MATLAB-based package 'exomePeak' for detection of RNA methylation sites from MeRIPseq data. Here, we extend the features of exomePeak by including a novel computational framework that enables differential analysis to unveil the dynamics in RNA epigenetic regulations. The novel differential analysis monitors the percentage of modified RNA molecules among the total transcribed RNAs, which directly reflects the impact of RNA epigenetic regulations. In contrast, current available software packages developed for sequencing-based differential analysis such as DESeq or edgeR monitors the changes in the absolute amount of molecules, and, if applied to MeRIPseq data, might be dominated by transcriptional gene differential expression. The algorithm is implemented as an R-package 'exomePeak' and freely available. It takes directly the aligned BAM files as input, statistically supports biological replicates, corrects PCR artifacts, and outputs exome-based results in BED format, which is compatible with all major genome browsers for convenient visualization and manipulation. Examples are also provided to depict how exomePeak R-package is integrated with exiting tools for MeRIPseq based peak calling and differential analysis. Particularly, the rationales behind each processing step as well as the specific method used, the best practice, and possible alternative strategies are briefly discussed. The algorithm was applied to the human HepG2 cell MeRIPseq data sets and detects more than 16000 RNA m ⁶ A sites, many of which are differentially methylated under ultraviolet radiation. The challenges and potentials of MeRIPseq in epitranscriptome studies are discussed in the end.