A hierarchical model for clustering m<SUP>6</SUP>A methylation peaks in MeRIP-seq data



Cui, Xiaodong, Meng, Jia ORCID: 0000-0003-3455-205X, Zhang, Shaowu, Rao, Manjeet K, Chen, Yidong and Huang, Yufei
(2016) A hierarchical model for clustering m<SUP>6</SUP>A methylation peaks in MeRIP-seq data. BMC GENOMICS, 17 (Suppl ). 520-.

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Abstract

<h4>Background</h4>The recent advent of the state-of-art high throughput sequencing technology, known as Methylated RNA Immunoprecipitation combined with RNA sequencing (MeRIP-seq) revolutionizes the area of mRNA epigenetics and enables the biologists and biomedical researchers to have a global view of N (6)-Methyladenosine (m(6)A) on transcriptome. Yet there is a significant need for new computation tools for processing and analysing MeRIP-Seq data to gain a further insight into the function and m(6)A mRNA methylation.<h4>Results</h4>We developed a novel algorithm and an open source R package ( http://compgenomics.utsa.edu/metcluster ) for uncovering the potential types of m(6)A methylation by clustering the degree of m(6)A 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 m(6)A's functions could be location specific.<h4>Conclusions</h4>In this paper, a novel hierarchical graphical model based algorithm was developed for clustering the enrichment of methylation peaks in MeRIP-seq data. MeTCluster is written in R and is publicly available.

Item Type: Article
Uncontrolled Keywords: RNA, Messenger, Cluster Analysis, Gene Expression Profiling, Immunoprecipitation, Epigenesis, Genetic, Methylation, Algorithms, Software, High-Throughput Nucleotide Sequencing, Transcriptome
Depositing User: Symplectic Admin
Date Deposited: 05 Feb 2019 16:27
Last Modified: 09 Oct 2023 22:01
DOI: 10.1186/s12864-016-2913-x
Open Access URL: https://doi.org/10.1186/s12864-016-2913-x
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URI: https://livrepository.liverpool.ac.uk/id/eprint/3032304