Epigenetic rates of change, much as evolutionary mutation rate along a lineage, vary during lifetime. Accurate estimation of the epigenetic state has vast medical and biological implications. To account for these nonlinear epigenetic changes with age, we recently developed a formalism inspired by the Pacemaker model of evolution that accounts for varying rates of mutations with time. Here, we present a python implementation of the Epigenetic Pacemaker (EPM), a conditional expectation maximization algorithm that estimates epigenetic landscapes and the state of individuals and may be used to study non-linear epigenetic aging. Availability and Implementation: The EPM is available at https://pypi.org/project/EpigeneticPacemaker/ under the MIT license. The EPM is compatible with python version 3.6 and above.
Bibliographical notePublisher Copyright:
© The Author(s) 2020. Published by Oxford University Press.
- Epigenesis, Genetic
- Pacemaker, Artificial
ASJC Scopus subject areas
- Computational Mathematics
- Molecular Biology
- Statistics and Probability
- Computer Science Applications
- Computational Theory and Mathematics