Abstract
We consider the problem of understanding how DNA methylation fidelity, i.e. the preservation of methylated sites in the genome, varies across the genome and across different cell types. Our approach uses a stochastic model of DNA methylation across generations and trains it using data obtained through next generation sequencing. By training the model locally, i.e. learning its parameters based on observations in a specific genomic region, we can compare how DNA methylation fidelity varies genome-wide. In the paper, we focus on the computational challenges to scale parameter estimation to the whole-genome level, and present two methods to achieve this goal, one based on moment-based approximation and one based on simulation. We extensively tested our methods on synthetic data and on a first batch of experimental data.
L.B., T.K., L.M., and V.W. are partially funded by the German Research Council (DFG) as part of the Cluster of Excellence on Multimodal Computing and Interaction at Saarland University and the Collaborative Research Center SFB 1027. C.B. was supported by a New Frontiers Group award of the Austrian Academy of Sciences.
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Notes
- 1.
Note that the emission probabilities are dependent on the relative frequencies \(p_{uX}\) and \(p_{mX}\), which are random variables as they depend on the random quantities \(u_X\) and \(m_X\).
- 2.
We avoid the number of measurements C to be set to zero by subtracting 0.5 from the reduced coverage and add 0.5 to the quantity to round (Algorithm 1, line 8).
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Bock, C., Bortolussi, L., Krüger, T., Mikeev, L., Wolf, V. (2015). Model-Based Whole-Genome Analysis of DNA Methylation Fidelity. In: Abate, A., Šafránek, D. (eds) Hybrid Systems Biology. HSB 2015. Lecture Notes in Computer Science(), vol 9271. Springer, Cham. https://doi.org/10.1007/978-3-319-26916-0_8
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