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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.


Distribution of METTL3 and METTL14 targeted RNA methylation sites. For both METTL3 and METTL14, their targeted RNA methylation sites are relatively enriched near stop codon of mRNA; however, compared with METTL14 targets, METTL3 targets are relatively enriched on untranslated regions (5′ and 3′UTR). On lncRNA, their targets are unfirmly distributed with slightly enriched on 5′ end.
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fig10: Distribution of METTL3 and METTL14 targeted RNA methylation sites. For both METTL3 and METTL14, their targeted RNA methylation sites are relatively enriched near stop codon of mRNA; however, compared with METTL14 targets, METTL3 targets are relatively enriched on untranslated regions (5′ and 3′UTR). On lncRNA, their targets are unfirmly distributed with slightly enriched on 5′ end.

Mentions: We then checked the distribution of METTL3 and METTL14 targeted RNA methylation sites on mRNA and lncRNA. As shown in Figure 10, the targeted RNA methylation sites of METTL3 and METTL14 are relatively enriched near stop codon of mRNA. Interestingly, compared with METTL14 targets, METTL3 targets are relatively enriched on untranslated regions (5′ and 3′UTR), which is never reported before. Although existing studies suggest METTL3 and METTL14 function as an RNA methylation complex together with WTAP, our observation suggests that they may have their own respective functions as well. On lncRNA, their targets are almost uniformly distributed on the entire RNA with slight enrichment on 5′ end, whose reason is not yet clear.


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)

Distribution of METTL3 and METTL14 targeted RNA methylation sites. For both METTL3 and METTL14, their targeted RNA methylation sites are relatively enriched near stop codon of mRNA; however, compared with METTL14 targets, METTL3 targets are relatively enriched on untranslated regions (5′ and 3′UTR). On lncRNA, their targets are unfirmly distributed with slightly enriched on 5′ end.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig10: Distribution of METTL3 and METTL14 targeted RNA methylation sites. For both METTL3 and METTL14, their targeted RNA methylation sites are relatively enriched near stop codon of mRNA; however, compared with METTL14 targets, METTL3 targets are relatively enriched on untranslated regions (5′ and 3′UTR). On lncRNA, their targets are unfirmly distributed with slightly enriched on 5′ end.
Mentions: We then checked the distribution of METTL3 and METTL14 targeted RNA methylation sites on mRNA and lncRNA. As shown in Figure 10, the targeted RNA methylation sites of METTL3 and METTL14 are relatively enriched near stop codon of mRNA. Interestingly, compared with METTL14 targets, METTL3 targets are relatively enriched on untranslated regions (5′ and 3′UTR), which is never reported before. Although existing studies suggest METTL3 and METTL14 function as an RNA methylation complex together with WTAP, our observation suggests that they may have their own respective functions as well. On lncRNA, their targets are almost uniformly distributed on the entire RNA with slight enrichment on 5′ end, whose reason is not yet clear.

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.