Limits...
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.


Hidden Markov model. In FHB strategy, the “observation” is a binary status reported from FET, and the emission probability is Bernoulli distribution.
© Copyright Policy - open-access
Related In: Results  -  Collection


getmorefigures.php?uid=PMC4537718&req=5

fig3: Hidden Markov model. In FHB strategy, the “observation” is a binary status reported from FET, and the emission probability is Bernoulli distribution.

Mentions: In FHB strategy, we use the binary decisions received from FET as the observation of hidden Markov model. The model essentially evaluates how likely a true differential methylation state can be detected by FET, or if FET reports a DMS with a significance level, how likely it is true after incorporating spatial dependency. We assume that a state can be correctly observed with probability p; and a mistake happens with probability (1 − p). Since the observation from FET is considered as binary, a cut-off threshold should be used to switch the FDR (False Discovery Rate) value to generate the “observed” set of observed variable O = (o1, o2,…, on) with on ∈ {0,1}. Then according to the standard HMM definition, these probabilities consist of an emission matrix B, whose entries are defined as(5)Bij=Pon=j ∣ sn=i=p,i,j∈0,1,  i=j,1−p,i,j∈0,1,  i≠j.The detailed structure of HMM is shown in Figure 3.


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)

Hidden Markov model. In FHB strategy, the “observation” is a binary status reported from FET, and the emission probability is Bernoulli distribution.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig3: Hidden Markov model. In FHB strategy, the “observation” is a binary status reported from FET, and the emission probability is Bernoulli distribution.
Mentions: In FHB strategy, we use the binary decisions received from FET as the observation of hidden Markov model. The model essentially evaluates how likely a true differential methylation state can be detected by FET, or if FET reports a DMS with a significance level, how likely it is true after incorporating spatial dependency. We assume that a state can be correctly observed with probability p; and a mistake happens with probability (1 − p). Since the observation from FET is considered as binary, a cut-off threshold should be used to switch the FDR (False Discovery Rate) value to generate the “observed” set of observed variable O = (o1, o2,…, on) with on ∈ {0,1}. Then according to the standard HMM definition, these probabilities consist of an emission matrix B, whose entries are defined as(5)Bij=Pon=j ∣ sn=i=p,i,j∈0,1,  i=j,1−p,i,j∈0,1,  i≠j.The detailed structure of HMM is shown in Figure 3.

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.