Biochemical modifications to mRNA, especially N6-methyladenosine (m6A) and 
5-methylcytosine (m5C), have been recently shown to be associated with crucial 
biological functions. Despite the intriguing advancements, little is known so 
far about the dynamic landscape of RNA methylome across different cell types and 
how the epitranscriptome is regulated at the system level by enzymes, i.e., RNA 
methyltransferases and demethylases. To investigate this issue, a meta-analysis 
of m6A MeRIP-Seq datasets collected from 10 different experimental conditions 
(cell type/tissue or treatment) is performed, and the combinatorial 
epitranscriptome, which consists of 42 758 m6A sites, is extracted and divided 
into 3 clusters, in which the methylation sites are likely to be hyper- or 
hypo-methylated simultaneously (or co-methylated), indicating the sharing of a 
common methylation regulator. Four different clustering approaches are used, 
including K-means, hierarchical clustering (HC), Bayesian factor regression 
model (BFRM) and nonnegative matrix factorization (NMF) to unveil the 
co-methylation patterns. To validate whether the patterns are corresponding to 
enzymatic regulators, i.e., RNA methyltransferases or demethylases, the target 
sites of a known m6A regulator, fat mass and obesity-associated protein (FTO), 
are identified from an independent mouse MeRIP-Seq dataset and lifted to human. 
Our study shows that 3 out of the 4 clustering approaches used can successfully 
identify a group of methylation sites overlapping with FTO target sites at a 
significance level of 0.05 (after multiple hypothesis adjustment), among which, 
the result of NMF is the most significant (p-value 2.81×10(-06)). We defined a 
new approach evaluating the consistency between two clustering results which 
shows that clustering results of different methods are highly correlated 
strongly indicating the existence of co-methylation patterns. Consistent with 
recent studies, a number of cancer and neuronal disease-related bimolecular 
functions are enriched in the identified clusters, which are biological 
functions that can be regulated at the epitranscriptional level, indicating the 
pharmaceutical prospect of RNA N6-methyladenosine-related studies. This result 
successfully reveals the linkage between the global RNA co-methylation patterns 
embedded in the epitranscriptomic data under multiple experimental conditions 
and the latent enzymatic regulators, suggesting a promising direction towards a 
more comprehensive understanding of the epitranscriptome.