Prediction and analysis for transcriptome M6A methylation sites on MeRIP-Seq data

Cui, Xiaodong
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Methylated RNA Immunoprecipitation combined with RNA sequencing (MeRIP-Seq) is revolutionizing the de novo study of RNA epigenomics at a higher resolution. However, this new technology poses unique bioinformatics problems that call for novel and sophisticated statistical computational solutions.

To address these challenges, we developed several comprehensive bioinformatics tools for MeRIP-Seq. First, we developed HEPeak, a Hidden Markov Model (HMM)-based Exome Peak-finding algorithm for predicting transcriptome methylation sites using MeRIP-Seq data. Then, we significantly updated peak-calling algorithm and released another R package – MeTPeak. MeTPeak not only inherits the merits of modeling correlation between bins from HEPeak, but also utilized a hierarchical layer of parameters to model the variances of m6A sites among replicates.

Furthermore, we proposed, MeTDiff, a novel computational tool for predicting differential m6A methylation sites from MeRIP-Seq data. Recently, we just developed a novel algorithm for clustering m6A sites in MeRIP-Seq datasets. This algorithm is designed for uncovering the potential types of m6A methylation by clustering the degree of m6A methylation peaks. This algorithm utilizes a graphical model to model the reads account variance and the underlying clusters of the methylation peaks.

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differential analysis, graphical model, m6A, peak clustering, peak detection, RNA methylation
Electrical and Computer Engineering