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A Hierarchical Model for Clustering m6A Methylation Peaks in MeRIP-seq Data

Xiaodong Cui, Jia Meng, Shaowu Zhang, Manjeet Rao, Yidong Chen, Yufei Huang

We developed a novel algorithm and an open source R package for uncovering the potential types of m6A methylation by clustering the degree of m6A methylation peaks in MeRIP-Seq data. This algorithm utilizes a hierarchical graphical model to model the reads account variance and the underlying clusters of the methylation peaks. Rigorous statistical inference is performed to estimate the model parameter and detect the number of clusters. MeTCluster is evaluated on both simulated and real MeRIP-seq datasets and the results demonstrate its high accuracy in characterizing the clusters of methylation peaks. Our algorithm was applied to two different sets of real MeRIP-seq datasets and reveals a novel pattern that methylation peaks with less peak enrichment tend to clustered in the 5' end of both in both mRNAs and lncRNAs, whereas those with higher peak enrichment are more likely to be distributed in CDS and towards the 3' end of mRNAs and lncRNAs. This result might suggest that m6A's functions could be location specific.

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