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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.


Comparison of differential methylation analysis methods. This figure shows the difference between existing peak-based differential analysis method and the proposed method. Started from aligned reads, the left part of this figure shows how exomePeak conducts differential analysis. It firstly identifies a single methylation site and then decides whether the methylation site as a whole is differentially methylated or not. However, the newly proposed method will split the testing region into multiple adjacent small bins and then will integrate their dependency with HMM for more accurate identification of differential methylation site. In the above example, the RNA methylation site detected using exomePeak method may consist of two methylation residuals, and only the one on the right side is differentially methylated in this case-control study. The proposed FET-HMM method is likely to work better than peak-based exomePeak method under this scenario.
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fig2: Comparison of differential methylation analysis methods. This figure shows the difference between existing peak-based differential analysis method and the proposed method. Started from aligned reads, the left part of this figure shows how exomePeak conducts differential analysis. It firstly identifies a single methylation site and then decides whether the methylation site as a whole is differentially methylated or not. However, the newly proposed method will split the testing region into multiple adjacent small bins and then will integrate their dependency with HMM for more accurate identification of differential methylation site. In the above example, the RNA methylation site detected using exomePeak method may consist of two methylation residuals, and only the one on the right side is differentially methylated in this case-control study. The proposed FET-HMM method is likely to work better than peak-based exomePeak method under this scenario.

Mentions: Affinity-based approaches cannot provide single-base resolution. Since multiple RNA methylation residuals may locate in proximity and cannot be effectively differentiated with peak calling procedure, they can appear as a single broad methylation site in the peak calling result from MACS [27] or exomePeak [19]. In many cases, this discrepancy can be trivial and does not significantly affect relevant study; however, it can be disastrous in differential methylation analysis, because multiple RNA methylation residuals can be regulated by different enzyme complexes and thus may be differentially methylated. Failing to identify the precise location of each methylation residual can lead to large bias in the estimation of its methylation level and in the comparison to a different condition. Currently, all existing methods for RNA differential methylation from MeRIP-Seq data are peak-based. In this paper, based on the rhtest method developed in exomePeak package [21], we proposed FET-HMM, a novel strategy for spatially enhanced differential RNA methylation analysis using hidden Markov model (HMM). When applying to the RNA methylation site detected from a peak calling algorithm, FET-HMM breaks a single site into multiple adjacent small bins and evaluates whether a specific bin is differentially methylated or not between two experimental conditions with spatial dependency incorporated by HMM. Figure 2 shows the comparison between existing and our methods.


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)

Comparison of differential methylation analysis methods. This figure shows the difference between existing peak-based differential analysis method and the proposed method. Started from aligned reads, the left part of this figure shows how exomePeak conducts differential analysis. It firstly identifies a single methylation site and then decides whether the methylation site as a whole is differentially methylated or not. However, the newly proposed method will split the testing region into multiple adjacent small bins and then will integrate their dependency with HMM for more accurate identification of differential methylation site. In the above example, the RNA methylation site detected using exomePeak method may consist of two methylation residuals, and only the one on the right side is differentially methylated in this case-control study. The proposed FET-HMM method is likely to work better than peak-based exomePeak method under this scenario.
© Copyright Policy - open-access
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4537718&req=5

fig2: Comparison of differential methylation analysis methods. This figure shows the difference between existing peak-based differential analysis method and the proposed method. Started from aligned reads, the left part of this figure shows how exomePeak conducts differential analysis. It firstly identifies a single methylation site and then decides whether the methylation site as a whole is differentially methylated or not. However, the newly proposed method will split the testing region into multiple adjacent small bins and then will integrate their dependency with HMM for more accurate identification of differential methylation site. In the above example, the RNA methylation site detected using exomePeak method may consist of two methylation residuals, and only the one on the right side is differentially methylated in this case-control study. The proposed FET-HMM method is likely to work better than peak-based exomePeak method under this scenario.
Mentions: Affinity-based approaches cannot provide single-base resolution. Since multiple RNA methylation residuals may locate in proximity and cannot be effectively differentiated with peak calling procedure, they can appear as a single broad methylation site in the peak calling result from MACS [27] or exomePeak [19]. In many cases, this discrepancy can be trivial and does not significantly affect relevant study; however, it can be disastrous in differential methylation analysis, because multiple RNA methylation residuals can be regulated by different enzyme complexes and thus may be differentially methylated. Failing to identify the precise location of each methylation residual can lead to large bias in the estimation of its methylation level and in the comparison to a different condition. Currently, all existing methods for RNA differential methylation from MeRIP-Seq data are peak-based. In this paper, based on the rhtest method developed in exomePeak package [21], we proposed FET-HMM, a novel strategy for spatially enhanced differential RNA methylation analysis using hidden Markov model (HMM). When applying to the RNA methylation site detected from a peak calling algorithm, FET-HMM breaks a single site into multiple adjacent small bins and evaluates whether a specific bin is differentially methylated or not between two experimental conditions with spatial dependency incorporated by HMM. Figure 2 shows the comparison between existing and our methods.

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.