An optimization approach to detect differentially methylated regions from Whole Genome Bisulfite Sequencing data
- Published
- Accepted
- Subject Areas
- Bioinformatics, Computational Biology, Optimization Theory and Computation
- Keywords
- whole genome bisulfite sequencing, differential methylation, LASSO, optimization, dynamic programming
- Copyright
- © 2015 Hesse et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ PrePrints) and either DOI or URL of the article must be cited.
- Cite this article
- 2015. An optimization approach to detect differentially methylated regions from Whole Genome Bisulfite Sequencing data. PeerJ PrePrints 3:e1287v3 https://doi.org/10.7287/peerj.preprints.1287v3
Abstract
Whole genome bisulfite sequencing (WGBS) is the current method of choice to obtain the methylation status of each single CpG dinucleotide in a genome. The typical analysis asks for regions that are differentially methylated (DMRs) between samples of two classes, such as different cell types. However, even with current low sequencing costs, many studies need to cope with few samples and medium coverage to stay within budget. We present a method to conservatively estimate the methylation difference between the two classes. Starting from a Bayesian paradigm, we formulate an optimization problem related to LASSO approaches. We present a dynamic programming approach to efficiently compute the optimal solution and its implementation diffmer. We discuss the dependency of the resulting DMRs on the free parameters of our approach and compare the results to those obtained by other DMR discovery tools (BSmooth and RADMeth). We showcase that our method discovers DMRs that are missed by the other tools.
Author Comment
This work has been presented at the German Conference on Bioinformatics 2015. Indexing correction in Figure 1