title: Spatially Enhanced Differential RNA Methylation Analysis from Affinity-Based Sequencing Data with Hidden Markov Model.
abstract: With the development of new sequencing technology, the entire N6-methyl-adenosine (m(6)A) RNA methylome can now be unbiased profiled with methylated RNA immune-precipitation sequencing technique (MeRIP-Seq), making it possible to detect differential methylation states of RNA between two conditions, for example, between normal and cancerous tissue. However, as an affinity-based method, MeRIP-Seq has yet provided base-pair resolution; that is, a single methylation site determined from MeRIP-Seq data can in practice contain multiple RNA methylation residuals, some of which can be regulated by different enzymes and thus differentially methylated between two conditions. Since existing peak-based methods could not effectively differentiate multiple methylation residuals located within a single methylation site, we propose a hidden Markov model (HMM) based approach to address this issue. Specifically, the detected RNA methylation site is further divided into multiple adjacent small bins and then scanned with higher resolution using a hidden Markov model to model the dependency between spatially adjacent bins for improved accuracy. We tested the proposed algorithm on both simulated data and real data. Result suggests that the proposed algorithm clearly outperforms existing peak-based approach on simulated systems and detects differential methylation regions with higher statistical significance on real dataset.
authors: Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China.
authors: Yu-Chen
authors: YC
authors: Zhang
authors: Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China.
authors: Shao-Wu
authors: SW
authors: Zhang
authors: Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China.
authors: Lian
authors: L
authors: Liu
authors: School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China.
authors: Hui
authors: H
authors: Liu
authors: School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China.
authors: Lin
authors: L
authors: Zhang
authors: Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, TX 78249, USA.
authors: Xiaodong
authors: X
authors: Cui
authors: Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, TX 78249, USA.
authors: Yufei
authors: Y
authors: Huang
authors: XJTLU-WTNC Research Institute, Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China.
authors: Jia
authors: J
authors: Meng
mesh_terms: D000465:Algorithms; D001483:Base Sequence; D019175:DNA Methylation / Q000235:genetics*; D057225:Data Mining; D008390:Markov Chains; D012313:RNA / Q000235:genetics / Q000378:metabolism*; D012333:RNA, Messenger / Q000378:metabolism*; D017423:Sequence Analysis, RNA
chemical_list: D012333:RNA, Messenger; D012313:RNA