REW-ISA V2: A Biclustering Method Fusing Homologous Information for Analyzing and Mining Epi-Transcriptome Data

Background: Previous studies have shown that N6-methyladenosine (m⁶A) is related to many life processes and physiological and pathological phenomena. However, the specific regulatory mechanism of m⁶A sites at the systematic level is not clear. Therefore, mining the RNA co-methylation patterns in the epi-transcriptome data is expected to explain the specific regulation mechanism of m⁶A. Methods: Considering that the epi-transcriptome data contains homologous information (the genes corresponding to the m⁶A sites and the cell lines corresponding to the experimental conditions), rational use of this information will help reveal the regulatory mechanism of m⁶A. Therefore, based on the RNA expression weighted iterative signature algorithm (REW-ISA), we have fused homologous information and developed the REW-ISA V2 algorithm. Results: Then, REW-ISA V2 was applied in the MERIP-seq data to find potential local function blocks (LFBs), where sites are hyper-methylated simultaneously across the specific conditions. Finally, REW-ISA V2 obtained fifteen LFBs. Compared with the most advanced biclustering algorithm, the LFBs obtained by REW-ISA V2 have more significant biological significance. Further biological analysis showed that these LFBs were highly correlated with some signal pathways and m⁶A methyltransferase. Conclusion: REW-ISA V2 fuses homologous information to mine co-methylation patterns in the epi-transcriptome data, in which sites are co-methylated under specific conditions.