A hierarchical model for clustering m6A methylation peaks in MeRIP-seq data

BMC Genomics

Research

Shaowu Zhang¹  Jia Meng²  Xiaodong Cui³  Yufei Huang⁴  Yidong Chen⁵  Manjeet K. Rao⁶ 

[1] College of Automation, Northwestern Polytechnical University, 710072, Xi’an, China;Department of Biological Science, Xi’an Jiaotong-liverpool University, 215123, Suzhou, China;Department of Electrical and Computer Engineering, University of Texas, 78249, San Antonio, TX, USA;Department of Electrical and Computer Engineering, University of Texas, 78249, San Antonio, TX, USA;Depeartment of Epidemiology and Biostatistics, University of Texas Health Science Center, 78229, San Antonio, TX, USA;Depeartment of Epidemiology and Biostatistics, University of Texas Health Science Center, 78229, San Antonio, TX, USA;Greehey Children’s Cancer Research Institute, University of Texas Health Science Center, 78229, San Antonio, TX, USA;Greehey Children’s Cancer Research Institute, University of Texas Health Science Center, 78229, San Antonio, TX, USA;

关键词: Bayesian Information Criterion;    Beta Distribution;    Human HeLa Cell;    Methylation Peak;    Methylation Degree;   

DOI  :  10.1186/s12864-016-2913-x

来源: Springer

【 摘 要 】

BackgroundThe 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 N6-Methyladenosine (m6A) 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 m6A mRNA methylation.ResultsWe developed a novel algorithm and an open source R package (http://compgenomics.utsa.edu/metcluster) 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.ConclusionsIn 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.

【 授权许可】

CC BY   
© The Author(s). 2016

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