Abstract
Background: The recently emerged technology of methylated RNA immunoprecipitation sequencing (MeRIP-seq) sheds light on the study of RNA epigenetics. This new bioinformatics question calls for effective and robust peaking calling algorithms to detect mRNA methylation sites from MeRIP-seq data. Methods: We propose a Bayesian hierarchical model to detect methylation sites from MeRIP-seq data. Our modeling approach includes several important characteristics. First, it models the zero-inflated and over-dispersed counts by deploying a zero-inflated negative binomial model. Second, it incorporates a hidden Markov model (HMM) to account for the spatial dependency of neighboring read enrichment. Third, our Bayesian inference allows the proposed model to borrow strength in parameter estimation, which greatly improves the model stability when dealing with MeRIP-seq data with a small number of replicates. We use Markov chain Monte Carlo (MCMC) algorithms to simultaneously infer the model parameters in a de novo fashion. The R Shiny demo is available at https://qiwei.shinyapps.io/BaySeqPeak and the R/C ++ code is available at https://github.com/liqiwei2000/BaySeqPeak. Results: In simulation studies, the proposed method outperformed the competing methods exomePeak and MeTPeak, especially when an excess of zeros were present in the data. In real MeRIP-seq data analysis, the proposed method identified methylation sites that were more consistent with biological knowledge, and had better spatial resolution compared to the other methods. Conclusions: In this study, we develop a Bayesian hierarchical model to identify methylation peaks in MeRIP-seq data. The proposed method has a competitive edge over existing methods in terms of accuracy, robustness and spatial resolution. [Figure not available: see fulltext.].
| Original language | English (US) |
|---|---|
| Pages (from-to) | 275-286 |
| Number of pages | 12 |
| Journal | Quantitative Biology |
| Volume | 6 |
| Issue number | 3 |
| DOIs | |
| State | Published - Sep 1 2018 |
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Keywords
- Bayesian inference
- hidden Markov model
- MeRIP-seq data
- RNA epigenomics
- zero-inflated negative binomial
ASJC Scopus subject areas
- Modeling and Simulation
- Biochemistry, Genetics and Molecular Biology (miscellaneous)
- Computer Science Applications
- Applied Mathematics
Cite this
A Bayesian hierarchical model for analyzing methylated RNA immunoprecipitation sequencing data. / Zhang, Minzhe; Li, Qiwei; Xie, Yang.
In: Quantitative Biology, Vol. 6, No. 3, 01.09.2018, p. 275-286.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - A Bayesian hierarchical model for analyzing methylated RNA immunoprecipitation sequencing data
AU - Zhang, Minzhe
AU - Li, Qiwei
AU - Xie, Yang
PY - 2018/9/1
Y1 - 2018/9/1
N2 - Background: The recently emerged technology of methylated RNA immunoprecipitation sequencing (MeRIP-seq) sheds light on the study of RNA epigenetics. This new bioinformatics question calls for effective and robust peaking calling algorithms to detect mRNA methylation sites from MeRIP-seq data. Methods: We propose a Bayesian hierarchical model to detect methylation sites from MeRIP-seq data. Our modeling approach includes several important characteristics. First, it models the zero-inflated and over-dispersed counts by deploying a zero-inflated negative binomial model. Second, it incorporates a hidden Markov model (HMM) to account for the spatial dependency of neighboring read enrichment. Third, our Bayesian inference allows the proposed model to borrow strength in parameter estimation, which greatly improves the model stability when dealing with MeRIP-seq data with a small number of replicates. We use Markov chain Monte Carlo (MCMC) algorithms to simultaneously infer the model parameters in a de novo fashion. The R Shiny demo is available at https://qiwei.shinyapps.io/BaySeqPeak and the R/C ++ code is available at https://github.com/liqiwei2000/BaySeqPeak. Results: In simulation studies, the proposed method outperformed the competing methods exomePeak and MeTPeak, especially when an excess of zeros were present in the data. In real MeRIP-seq data analysis, the proposed method identified methylation sites that were more consistent with biological knowledge, and had better spatial resolution compared to the other methods. Conclusions: In this study, we develop a Bayesian hierarchical model to identify methylation peaks in MeRIP-seq data. The proposed method has a competitive edge over existing methods in terms of accuracy, robustness and spatial resolution. [Figure not available: see fulltext.].
AB - Background: The recently emerged technology of methylated RNA immunoprecipitation sequencing (MeRIP-seq) sheds light on the study of RNA epigenetics. This new bioinformatics question calls for effective and robust peaking calling algorithms to detect mRNA methylation sites from MeRIP-seq data. Methods: We propose a Bayesian hierarchical model to detect methylation sites from MeRIP-seq data. Our modeling approach includes several important characteristics. First, it models the zero-inflated and over-dispersed counts by deploying a zero-inflated negative binomial model. Second, it incorporates a hidden Markov model (HMM) to account for the spatial dependency of neighboring read enrichment. Third, our Bayesian inference allows the proposed model to borrow strength in parameter estimation, which greatly improves the model stability when dealing with MeRIP-seq data with a small number of replicates. We use Markov chain Monte Carlo (MCMC) algorithms to simultaneously infer the model parameters in a de novo fashion. The R Shiny demo is available at https://qiwei.shinyapps.io/BaySeqPeak and the R/C ++ code is available at https://github.com/liqiwei2000/BaySeqPeak. Results: In simulation studies, the proposed method outperformed the competing methods exomePeak and MeTPeak, especially when an excess of zeros were present in the data. In real MeRIP-seq data analysis, the proposed method identified methylation sites that were more consistent with biological knowledge, and had better spatial resolution compared to the other methods. Conclusions: In this study, we develop a Bayesian hierarchical model to identify methylation peaks in MeRIP-seq data. The proposed method has a competitive edge over existing methods in terms of accuracy, robustness and spatial resolution. [Figure not available: see fulltext.].
KW - Bayesian inference
KW - hidden Markov model
KW - MeRIP-seq data
KW - RNA epigenomics
KW - zero-inflated negative binomial
UR - http://www.scopus.com/inward/record.url?scp=85053228281&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85053228281&partnerID=8YFLogxK
U2 - 10.1007/s40484-018-0149-2
DO - 10.1007/s40484-018-0149-2
M3 - Article
AN - SCOPUS:85053228281
VL - 6
SP - 275
EP - 286
JO - Quantitative Biology
JF - Quantitative Biology
SN - 2095-4689
IS - 3
ER -