Estimation of the methylation pattern distribution from deep sequencing data

@article{Lin2015EstimationOT,
  title={Estimation of the methylation pattern distribution from deep sequencing data},
  author={Peijie Lin and Sylvain For{\^e}t and Susan R. Wilson and Conrad J. Burden},
  journal={BMC Bioinformatics},
  year={2015},
  volume={16}
}
BackgroundBisulphite sequencing enables the detection of cytosine methylation. The sequence of the methylation states of cytosines on any given read forms a methylation pattern that carries substantially more information than merely studying the average methylation level at individual positions. In order to understand better the complexity of DNA methylation landscapes in biological samples, it is important to study the diversity of these methylation patterns. However, the accurate… 
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6 Citations
Background Citations
3
Methods Citations
2

6 Citations

Quantitative comparison of within-sample heterogeneity scores for DNA methylation data
TLDR
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An information-theoretic approach to the modeling and analysis of whole-genome bisulfite sequencing data
TLDR
A powerful information-theoretic approach for genome-wide modeling and analysis of WGBS data based on the 1D Ising model of statistical physics, which takes into account correlations in methylation by utilizing a joint probability model that encapsulates all information available inWGBS methylation reads and produces accurate results even when applied on single WG BS samples with low coverage.
A smoothed EM-algorithm for DNA methylation profiles from sequencing-based methods in cell lines or for a single cell type
TLDR
A kernel smoothed EM-algorithm is derived, capable of analyzing an entire chromosome at once, and to simultaneously correct for experimental errors arising from either the pre-treatment steps or from the sequencing stage and to take into account spatial correlations between DNA methylation profiles at neighbouring CpG sites.
Quantification of tumour evolution and heterogeneity via Bayesian epiallele detection
TLDR
A Bayesian model is developed to infer which epialleles are present in multiple regions of the same tumour, which enables phylogenetic analyses, identification of differentially expressed epIALleles, and provides a measure of epigenetic heterogeneity.
Exploring DNA Methylation Diversity in the Honey Bee Brain by Ultra-Deep Amplicon Sequencing
TLDR
It is concluded that ultra-deep sequencing of gene-specific amplicons combined with genotyping of differentially methylated epialleles is an effective way to facilitate more advanced neuro-epigenomic studies in honey bees and other insects.
EGFR gene methylation is not involved in Royalactin controlled phenotypic polymorphism in honey bees
TLDR
An extensive analysis of the honeybee egfr locus is conducted and shows that this gene is never methylated, paving the way for contrasting context-dependent regulation of EGFR in three insect species.

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