Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TCBB.2019.2910814 | IEEE/ACM transactions on computational biology and bioinformatics |
Keywords | Field | DocType |
Hidden Markov models,DNA,Biochemistry,Maintenance engineering,Data models,Predictive models,Genomics | Computer science,Bisulfite sequencing,DNA methylation,DNA,Genomics,Methylation,DNA sequencing,Artificial intelligence,Computational biology,Hidden Markov model,Machine learning,Epigenetics | Journal |
Volume | Issue | ISSN |
16 | 5 | 1557-9964 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alexander Lück | 1 | 3 | 1.85 |
| Pascal Giehr | 2 | 2 | 1.54 |
| Karl Nordström | 3 | 3 | 1.10 |
| Jörn Walter | 4 | 41 | 5.97 |
| Verena Wolf | 5 | 22 | 7.27 |