MBMM: Moment Estimating Beta Mixture Model-based Clustering Algorithm for m6A Co-methylation Module Mining

Title:MBMM: Moment Estimating Beta Mixture Model-based Clustering Algorithm for m6A Co-methylation Module Mining

Volume: 16 Issue: 10

Author(s): Zhaoyang Liu, Hongsheng Yin*, Shutao Chen, Hui Liu , Jia Meng, HongLei Wang and Lin Zhang *

Affiliation:

• Engineering Research Center of Intelligent Control for Underground Space, Ministry of Education, China University of Mining and Technology, Xuzhou 221116, Jiangsu,China

• Engineering Research Center of Intelligent Control for Underground Space, Ministry of Education, China University of Mining and Technology, Xuzhou 221116, Jiangsu,China

Keywords: m⁶A, methylation, co-methylation module, beta mixture model, clustering, data mining.

Abstract:

Background: m⁶A methylation is a ubiquitous post-transcriptional modification that exists in mammals. MeRIP-seq technology makes the acquisition of m⁶A data in the whole transcriptome under different conditions realizable. The specific regulation of the enzyme will present comethylation module on m⁶A methylation level data. Thus, mining the co-methylation module from which can help to unveil the mechanism of m⁶A methylation modification and its mechanism in the occurrence and development of complex diseases such as cancer.

Objective: To develop a clustering algorithm that can effectively realize the mining of m⁶A comethylation module.

Methods: In this study, a novel beta mixture model-based clustering algorithm named MBMM was proposed, which is based on the EM framework and introduces the method of moment estimating in M-step for parameter estimation to tackle the high-dimensional small sample m⁶A data. Simulation research was employed to evaluate the clustering performance of the proposed algorithm, and by which the co-methylation module mining was done based on real data. Biological significance correlation analysis was employed to explore whether the clustering results are co-methylation modules.

Results and Conclusion: Simulation research demonstrated that MBMM performed out than other clustering algorithms. In real data, seven co-methylation modules were found by MBMM. Six m⁶Arelated pathways specific analysis showed that six co-methylation modules were enriched in the pathway and were different. Five enzymes substrate-specific analysis revealed that seven comethylation modules expressed varying degrees of enrichment. Gene Ontology enrichment analysis indicated that these modules may be regulated by enzymes while having potential functional specificity.

Cite this article as:

Liu Zhaoyang , Yin Hongsheng *, Chen Shutao, Liu Hui , Meng Jia , Wang HongLei and Zhang Lin *, MBMM: Moment Estimating Beta Mixture Model-based Clustering Algorithm for m6A Co-methylation Module Mining, Current Bioinformatics 2021; 16 (10) . https://dx.doi.org/10.2174/1574893616666210629143411