title: A hierarchical model for clustering m(6)A methylation peaks in MeRIP-seq data.
abstract: 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.
  
  
  
  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.
  
  
  
  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.
authors.affiliation: Department of Electrical and Computer Engineering, University of Texas, San Antonio, TX, 78249, USA.
authors.forename: Xiaodong
authors.initials: X
authors.lastname: Cui
authors.affiliation: Department of Biological Science, Xi'an Jiaotong-liverpool University, Suzhou, 215123, China.
authors.forename: Jia
authors.initials: J
authors.lastname: Meng
authors.affiliation: College of Automation, Northwestern Polytechnical University, Xi'an, 710072, China.
authors.forename: Shaowu
authors.initials: S
authors.lastname: Zhang
authors.affiliation: Greehey Children's Cancer Research Institute, University of Texas Health Science Center, San Antonio, TX, 78229, USA.
authors.forename: Manjeet K
authors.initials: MK
authors.lastname: Rao
authors.affiliation: Depeartment of Epidemiology and Biostatistics, University of Texas Health Science Center, San Antonio, TX, 78229, USA.|Greehey Children's Cancer Research Institute, University of Texas Health Science Center, San Antonio, TX, 78229, USA.
authors.forename: Yidong
authors.initials: Y
authors.lastname: Chen
authors.affiliation: Department of Electrical and Computer Engineering, University of Texas, San Antonio, TX, 78249, USA. yufei.huang@utsa.edu.|Depeartment of Epidemiology and Biostatistics, University of Texas Health Science Center, San Antonio, TX, 78229, USA. yufei.huang@utsa.edu.
authors.forename: Yufei
authors.initials: Y
authors.lastname: Huang
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D044127:Epigenesis, Genetic / Q000235:genetics; D020869:Gene Expression Profiling; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods* / Q000706:statistics & numerical data; D047468:Immunoprecipitation; D008745:Methylation; D012333:RNA, Messenger / Q000235:genetics; D012984:Software; D059467:Transcriptome / Q000235:genetics*
chemical_list: D012333:RNA, Messenger