FET-HMM – for spatially enhanced detection of differentially methylated region from MeRIP-Seq data

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.


Comparison of the differential methylation analysis in DNA and RNA. The first column shows the DNA related differential analysis in ChIP-Seq or MeDIP-Seq, where the total DNA is often considered the same under two experimental conditions, so the differential analysis can be performed by directly comparing the absolute amount of methylated RNAs in the two IP samples. In contrast, for RNA (the second column), the background is total RNA, which can vary significantly under different conditions, and therefore, the absolute amount of methylated RNA for a specific site does not necessarily correlate with the degree of methylation.

Since existing peak-based methods could not effectively differentiate multiple methylation residuals located within a single methylation site, researchers from Northwestern Polytechnical University, China 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. The researchers 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.


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.

Availability – The open source R package implementing the proposed algorithm on MeRIP-Seq data is freely available from GitHub: https://github.com/lzcyzm/RHHMM.

Zhang YC, Zhang SW, Liu L, Liu H, Zhang L, Cui X, Huang Y, Meng J. (2015) Spatially Enhanced Differential RNA Methylation Analysis from Affinity-Based Sequencing Data with Hidden Markov Model. Biomed Res Int 2015:852070. [article]

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