The Epigenetic Pacemaker: modeling epigenetic states under an evolutionary framework

Colin Farrell; Sagi Snir; Matteo Pellegrini

doi:10.1093/bioinformatics/btaa585

The Epigenetic Pacemaker: modeling epigenetic states under an evolutionary framework

Bioinformatics. 2020 Nov 1;36(17):4662-4663. doi: 10.1093/bioinformatics/btaa585.

Authors

Colin Farrell¹, Sagi Snir², Matteo Pellegrini³

Affiliations

¹ Department of Human Genetics, University of California, Los Angeles, CA, USA.
² Department of Evolutionary Biology, University of Haifa, Haifa, Israel.
³ Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, CA, USA.

Abstract

Summary: 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 non-linear 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.

Publication types

Research Support, N.I.H., Extramural

MeSH terms

Aging
Algorithms
Epigenesis, Genetic
Epigenomics*
Humans
Pacemaker, Artificial*
Software

Abstract

Publication types

MeSH terms

Grants and funding