• DocumentCode
    258134
  • Title

    Differential analysis of RNA methylome with improved spatial resolution

  • Author

    Yu-Chen Zhang ; Shao-Wu Zhang ; Lian Liu ; Lin Zhang ; Hui Liu ; Xiaodong Cui ; Yufei Huang ; Jia Meng

  • Author_Institution
    Key Lab. of Inf. Fusion Technol. of Minist. of Educ., Polytech. Univ., Xi´an, China
  • fYear
    2014
  • fDate
    3-5 Dec. 2014
  • Firstpage
    1372
  • Lastpage
    1375
  • Abstract
    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.
  • Keywords
    biology computing; hidden Markov models; molecular biophysics; public domain software; software packages; HMM; MeRIP-Seq; RHHMM approach; RNA methylation sites detection; RNA methylome; affinity-based approach; base-pair resolution; differential analysis method; hidden Markov model; open source R package; spatial resolution; transcriptome-wide N6-Adenosine profiling; Bioinformatics; DNA; Educational institutions; Hidden Markov models; RNA; Sequential analysis; Spatial resolution;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Signal and Information Processing (GlobalSIP), 2014 IEEE Global Conference on
  • Conference_Location
    Atlanta, GA
  • Type

    conf

  • DOI
    10.1109/GlobalSIP.2014.7032350
  • Filename
    7032350