A novel biclustering algorithm for mining m6A co-methylation patterns based on beta-binomial distribution and data screening strategy

Studies have shown that m6A plays a key role in different life processes such as RNA metabolism, physiology and pathology. However, due to the complexity of life processes, its specific regulatory details are still not revealed. The computational approach based on co-methylation pattern mining of m6A sequencing data can assist in revealing its mechanism and save time and economic cost, however, the current algorithms suffer from the problems of insufficient robustness to low signal-to-noise data and unreliable performance. Based on this, this paper proposes an enhanced beta-binomial distribution biclustering algorithm (EBBM) based on data screening strategy. This algorithm is based on the framework of Bayesian, adopts Gibbs sampling method for parameter inference, and introduces the data screening strategy in the process of parameter inference, which effectively removes the problem that the low signal-to-noise data in the original sequencing data of m6A affects the reliability of the clustering results. The simulation experiment results show that this algorithm can effectively deal with the interference of low signal-to-noise data and accurately mine the co-methylation patterns pre-planted in the data, which is significantly better than the current mainstream biclustering algorithm. In real human m6A sequencing data with 32 samples, this algorithm mined two effective co-methylation patterns, which were enriched to different biological processes, such as negative regulation of phosphorylation and peptidyl lysine methylation, etc. The scoring results of GEO_Score indicate that the results of this algorithm are more biologically meaningful than the clustering results of current mainstream m6A co-methylation pattern mining algorithms.Author summary: Methylation of RNA molecules—specifically a modification known as m⁶A—plays a crucial role in how our cells function, influencing everything from normal development to diseases like cancer. However, studying these modifications is challenging because the sequencing technology used to detect them produces noisy data, making it difficult to distinguish genuine biological signals from technical errors. We developed a new computational approach called EBBM that tackles this problem head-on. Our method works like a smart filter, simultaneously analyzing two complementary datasets generated by sequencing experiments. By incorporating a statistical model that accounts for the unique characteristics of this data, EBBM can identify patterns of co-methylation—groups of RNA sites that are modified together under specific conditions—while effectively discarding sequencing noise. When we tested EBBM on both simulated and real human data, it significantly outperformed existing methods. It successfully uncovered biologically meaningful co-methylation patterns that were linked to processes like cell differentiation and gene regulation. Our work provides researchers with a more reliable tool for studying RNA modifications, potentially accelerating discoveries about how these modifications contribute to health and disease, and opening new avenues for therapeutic development.