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Spatially Enhanced Differential RNA Methylation Analysis from Affinity-Based Sequencing Data with Hidden Markov Model.

Zhang YC, Zhang SW, Liu L, Liu H, Zhang L, Cui X, Huang Y, Meng J - Biomed Res Int (2015)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China.

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.

No MeSH data available.


Boxplot of AUCs for different thresholds applied to switch FDR to the binary state. This figure shows that with the variation of thresholds, the performance of FHB outperforms exomePeak in AUC on 100 datasets. exomePeak does not use the cut-off threshold so its performance remains the same. The performance is evaluated at bin level rather than peak level in all experiments.
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fig5: Boxplot of AUCs for different thresholds applied to switch FDR to the binary state. This figure shows that with the variation of thresholds, the performance of FHB outperforms exomePeak in AUC on 100 datasets. exomePeak does not use the cut-off threshold so its performance remains the same. The performance is evaluated at bin level rather than peak level in all experiments.

Mentions: In the first experiment, we tested the impact of cut-off threshold on the FHB strategy. As shown in Figure 5, although the choice of threshold does affect the performance of the algorithm, by incorporating spatial dependency, the proposed FHB strategy effectively improves the DMRs detection performance under all cut-off thresholds tested.


Spatially Enhanced Differential RNA Methylation Analysis from Affinity-Based Sequencing Data with Hidden Markov Model.

Zhang YC, Zhang SW, Liu L, Liu H, Zhang L, Cui X, Huang Y, Meng J - Biomed Res Int (2015)

Boxplot of AUCs for different thresholds applied to switch FDR to the binary state. This figure shows that with the variation of thresholds, the performance of FHB outperforms exomePeak in AUC on 100 datasets. exomePeak does not use the cut-off threshold so its performance remains the same. The performance is evaluated at bin level rather than peak level in all experiments.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4537718&req=5

fig5: Boxplot of AUCs for different thresholds applied to switch FDR to the binary state. This figure shows that with the variation of thresholds, the performance of FHB outperforms exomePeak in AUC on 100 datasets. exomePeak does not use the cut-off threshold so its performance remains the same. The performance is evaluated at bin level rather than peak level in all experiments.
Mentions: In the first experiment, we tested the impact of cut-off threshold on the FHB strategy. As shown in Figure 5, although the choice of threshold does affect the performance of the algorithm, by incorporating spatial dependency, the proposed FHB strategy effectively improves the DMRs detection performance under all cut-off thresholds tested.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China.

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.

No MeSH data available.