Abstract
There are multiple chemical modifications of cytosine that are important to the regulation and ultimately the functional expression of the genome. To date no single experiment can capture these separate modifications, and integrative experimental designs are needed to fully characterize cytosine methylation and chemical modification. This chapter describes a generative probabilistic model, Lux, for integrative analysis of cytosine methylation and its oxidized variants. Lux simultaneously analyzes partially orthogonal bisulfite sequencing data sets to estimate proportions of different cytosine methylation modifications and estimate multiple cytosine modifications for a single sample by integrating across experimental designs composed of multiple parallel destructive genomic measurements. Lux also considers the variation in measurements introduced by different imperfect experimental steps; the experimental variation can be quantified by using appropriate spike-in controls, allowing Lux to deconvolve the measurements and recover accurately the underlying signal.
Key words
- DNA methylation
- Bayesian analysis
- Hierarchical generative modeling
- 5-methylcytosine oxidation
- Bisulfite sequencing
- BS-seq/oxBS-seq/TAB-seq/fCAB-seq/CAB-seq/redBS-seq/MAB-seq
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Äijö, T., Bonneau, R., Lähdesmäki, H. (2018). Generative Models for Quantification of DNA Modifications. In: Mamitsuka, H. (eds) Data Mining for Systems Biology. Methods in Molecular Biology, vol 1807. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8561-6_4
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DOI: https://doi.org/10.1007/978-1-4939-8561-6_4
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