Abstract
Methylation patterns present in a cell population can inform us about the way the cells are organized and how the population is sustained. Methylation is inheritable through cell divisions but changes can occur as a result of methylation replication errors. Hence, variation in methylation patterns in a cell population at a given time captures information about the history of the cell population. It is important that the observed methylation patterns are representative of those in the cell population. However, bisulfite sequencing may introduce new patterns and degradation may eliminate rare patterns. We investigate how bisulfite degradation may be expected to affect the data, and how inference could be made in light of this. A model for the data generation process makes it possible to estimate the starting number of distinct methylation patterns more accurately than simply counting the number of distinct patterns observed.
Original language | English |
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Title of host publication | Emerging Trends in Applications and Infrastructures for Computational Biology, Bioinformatics, and Systems Biology: Systems and Applications |
Publisher | Elsevier Inc. |
Pages | 3-15 |
Number of pages | 13 |
ISBN (Electronic) | 9780128042595 |
ISBN (Print) | 9780128042038 |
DOIs | |
Publication status | Published - 22 Mar 2016 |
Keywords
- Lineage tracing
- Markov chain Monte Carlo
- Polymerase chain reaction