TY - GEN
T1 - Differential analysis of RNA methylome with improved spatial resolution
AU - Zhang, Yu Chen
AU - Zhang, Shao Wu
AU - Liu, Lian
AU - Zhang, Lin
AU - Liu, Hui
AU - Cui, Xiaodong
AU - Huang, Yufei
AU - Meng, Jia
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/2/5
Y1 - 2014/2/5
N2 - Recent development of MeRIP-Seq enabled the global unbiased profiling of transcriptome-wide N6-Adenosine. With this technique, it is now possible to detect the RNA methylation sites under a specific condition or the differential methylation sites between two experimental conditions. However, as an affinity-based approach, MeRIP-Seq has yet provided base-pair resolution. A single methylation site reported by MeRIP-Seq data may actually contain one or a few methylated RNA residuals, which cannot be differentiated by existing differential analysis methods when the entire RNA methylation site is treated as a single feature. Within this paper, we propose a new approach 'RHHMM' that combines Fisher's exact test and hidden Markov model (HMM) for the detection of differential methylation regions (DMRs) with improved spatial resolution. The results on both simulated and real data demonstrated that, with HMM incorporating local spatial dependency, it is possible to detect differential methylation sites with improved spatial resolution on affinity-based sequencing approach such as MeRIP-Seq. The proposed method is freely available as an open source R package.
AB - Recent development of MeRIP-Seq enabled the global unbiased profiling of transcriptome-wide N6-Adenosine. With this technique, it is now possible to detect the RNA methylation sites under a specific condition or the differential methylation sites between two experimental conditions. However, as an affinity-based approach, MeRIP-Seq has yet provided base-pair resolution. A single methylation site reported by MeRIP-Seq data may actually contain one or a few methylated RNA residuals, which cannot be differentiated by existing differential analysis methods when the entire RNA methylation site is treated as a single feature. Within this paper, we propose a new approach 'RHHMM' that combines Fisher's exact test and hidden Markov model (HMM) for the detection of differential methylation regions (DMRs) with improved spatial resolution. The results on both simulated and real data demonstrated that, with HMM incorporating local spatial dependency, it is possible to detect differential methylation sites with improved spatial resolution on affinity-based sequencing approach such as MeRIP-Seq. The proposed method is freely available as an open source R package.
UR - http://www.scopus.com/inward/record.url?scp=84949928043&partnerID=8YFLogxK
U2 - 10.1109/GlobalSIP.2014.7032350
DO - 10.1109/GlobalSIP.2014.7032350
M3 - Conference Proceeding
AN - SCOPUS:84949928043
T3 - 2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014
SP - 1372
EP - 1375
BT - 2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014
Y2 - 3 December 2014 through 5 December 2014
ER -