A Hybrid HMM Approach for the Dynamics of DNA Methylation

Kyriakopoulos, Charalampos; Giehr, Pascal; Lück, Alexander; Walter, Jörn; Wolf, Verena

doi:10.1007/978-3-030-28042-0_8

Cited by 3 publications

(4 citation statements)

References 32 publications

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“…In this case, the SAN description becomes even more useful as it would be very tedious to generate the transition matrix in other ways. It is also possible to apply the SAN approach to continuous time Markov chains or hybrid models as in [11]. Here, the discrete transition matrix was generated with a Kronecker product, while the continuous generator matrix can be generated with a Kronecker sum.…”

Section: Resultsmentioning

confidence: 99%

A Stochastic Automata Network Description for Spatial DNA-Methylation Models

Lück

Wolf

2020

Lecture Notes in Computer Science

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DNA methylation is an important biological mechanism to regulate gene expression and control cell development. Mechanistic modeling has become a popular approach to enhance our understanding of the dynamics of methylation pattern formation in living cells. Recent findings suggest that the methylation state of a cytosine base can be influenced by its DNA neighborhood. Therefore, it is necessary to generalize existing mathematical models that consider only one cytosine and its partner on the opposite DNA-strand (CpG), in order to include such neighborhood dependencies. One approach is to describe the system as a stochastic automata network (SAN) with functional transitions. We show that single-CpG models can successfully be generalized to multiple CpGs using the SAN description and verify the results by comparing them to results from extensive Monte-Carlo simulations.

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Section: Resultsmentioning

confidence: 99%

A Stochastic Automata Network Description for Spatial DNA-Methylation Models

Lück

Wolf

2020

Lecture Notes in Computer Science

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“…This also eases the modelling of more than three CpGs since we then do not longer assume the same dependence parameters for all CpGs and therefore make the model more flexible. To investigate a potential impact of oxidized cytosine forms on the methylation at neighboring CpG sites we further plan to include the CpG states 5hmC, 5fC and 5caC in our model and use a hybrid approach as presented in [17] in order to omit the necessity of specifying the order of certain events a priori.…”

Section: Resultsmentioning

confidence: 99%

“…However, upon combining them to the full transition matrices in Eq. (16) or (17), the final matrices become dense and therefore have higher memory requirements.…”

Section: Combination Of Transition Matricesmentioning

confidence: 99%

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Hidden Markov Modelling Reveals Neighborhood Dependence of Dnmt3a and 3b Activity

Lück

Giehr

Nordström

et al. 2019

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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DNA methylation is an epigenetic mark whose important role in development has been widely recognized. This epigenetic modification results in heritable information not encoded by the DNA sequence. The underlying mechanisms controlling DNA methylation are only partly understood. Several mechanistic models of enzyme activities responsible for DNA methylation have been proposed. Here, we extend existing Hidden Markov Models (HMMs) for DNA methylation by describing the occurrence of spatial methylation patterns over time and propose several models with different neighborhood dependences. Furthermore, we investigate correlations between the neighborhood dependence and other genomic information. We perform numerical analysis of the HMMs applied to comprehensive hairpin and non-hairpin bisulfite sequencing measurements and accurately predict wild-type data. We find evidence that the activities of Dnmt3a and Dnmt3b responsible for de novo methylation depend on 5' (left) but not on 3' (right) neighboring CpGs in a sequencing string.

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Modeling methylation dynamics with simultaneous changes in CpG islands

Grosser

Metzler

2020

BMC Bioinformatics

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Background In vertebrate genomes, CpG sites can be clustered into CpG islands, and the amount of methylation in a CpG island can change due to gene regulation processes. Thus, single regulatory events can simultaneously change the methylation states of many CpG sites within a CpG island. This should be taken into account when quantifying the amount of change in methylation, for example in form of a branch length in a phylogeny of cell types. Results We propose a probabilistic model (the IWE-SSE model) of methylation dynamics that accounts for simultaneous methylation changes in multiple CpG sites belonging to the same CpG island. We further propose a Markov-chain Monte-Carlo (MCMC) method to fit this model to methylation data from cell type phylogenies and apply this method to available data from murine haematopoietic cells and from human cell lines. Combined with simulation studies, these analyses show that accounting for CpG island wide methylation changes has a strong effect on the inferred branch lengths and leads to a significantly better model fit for the methylation data from murine haematopoietic cells and human cell lines. Conclusion The MCMC based parameter estimation method for the IWE-SSE model in combination with our MCMC based inference method allows to quantify the amount of methylation changes at single CpG sites as well as on entire CpG islands. Accounting for changes affecting entire islands can lead to more accurate branch length estimation in the presence of simultaneous methylation change.

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A Hybrid HMM Approach for the Dynamics of DNA Methylation

Cited by 3 publications

References 32 publications

A Stochastic Automata Network Description for Spatial DNA-Methylation Models

A Stochastic Automata Network Description for Spatial DNA-Methylation Models

Hidden Markov Modelling Reveals Neighborhood Dependence of Dnmt3a and 3b Activity

Modeling methylation dynamics with simultaneous changes in CpG islands

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