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

【 图 表 】

12864_2015_1944_Article_IEq10.gif

12864_2016_2913_Article_IEq43.gif

12864_2017_4359_Article_IEq1.gif

12864_2017_3605_Article_IEq3.gif

12864_2016_2913_Article_IEq40.gif

12864_2016_2913_Article_IEq39.gif

12864_2017_4133_Article_IEq18.gif

12864_2017_3487_Article_IEq38.gif

12864_2015_2055_Article_IEq32.gif

12864_2016_3353_Article_IEq41.gif

12864_2017_4132_Article_IEq27.gif

12864_2017_3670_Article_IEq13.gif

12864_2017_4071_Article_IEq5.gif

12864_2015_2304_Article_IEq10.gif

12864_2017_4071_Article_IEq4.gif

12864_2017_3920_Article_IEq1.gif

12864_2016_2580_Article_IEq1.gif

12894_2016_184_Article_IEq3.gif

12864_2017_3683_Article_IEq1.gif

12864_2015_2129_Article_IEq12.gif

12864_2017_3605_Article_IEq1.gif

12864_2016_2913_Article_IEq23.gif

12864_2016_3353_Article_IEq29.gif

12864_2016_2913_Article_IEq21.gif

12864_2016_3074_Article_IEq2.gif

12864_2017_3771_Article_IEq14.gif

12864_2016_3353_Article_IEq26.gif

12864_2016_2913_Article_IEq17.gif

12888_2016_877_Article_IEq16.gif

12864_2016_3353_Article_IEq23.gif

12864_2017_3487_Article_IEq20.gif

12864_2016_2791_Article_IEq1.gif

12864_2016_2913_Article_IEq12.gif

12864_2017_3733_Article_IEq6.gif

12864_2017_3600_Article_IEq3.gif

12864_2016_2913_Article_IEq9.gif

12864_2016_2682_Article_IEq48.gif

12864_2016_3098_Article_IEq57.gif

12864_2017_4030_Article_IEq7.gif

12864_2017_4030_Article_IEq6.gif

12864_2017_4030_Article_IEq5.gif

12864_2016_2682_Article_IEq32.gif

12864_2016_2682_Article_IEq31.gif

12888_2017_1557_Article_IEq7.gif