The epigenetic pacemaker: Modeling epigenetic states under an evolutionary framework

Colin Farrell, Sagi Snir, Matteo Pellegrini

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)4662-4663
Number of pages2
JournalBioinformatics
Volume36
Issue number17
DOIs
StatePublished - 1 Sep 2020

Bibliographical note

Publisher Copyright:
© The Author(s) 2020. Published by Oxford University Press.

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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