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Updated: Sep 12, 2025

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GenomicLayers: sequence-based simulation of epi-genomes.

Dave T Gerrard1

  • 1Division of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine & Health, The University of Manchester, Stopford Building, Oxford Road, Manchester, M13 9PT, UK. dave.gerrard@manchester.ac.uk.

BMC Bioinformatics
|August 4, 2025
PubMed
Summary
This summary is machine-generated.

GenomicLayers simulates genome-wide epigenetic changes in eukaryotes, modeling how regulatory signals affect cellular states. This R package allows in silico testing of gene regulation models for diverse organisms.

Keywords:
DevelopmentEpigenomeGenomeRSimulation

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Area of Science:

  • Genomics and Epigenetics
  • Computational Biology
  • Systems Biology

Background:

  • Eukaryotic cellular development relies on gene regulatory decisions, influenced by the epigenome.
  • Existing models lack integration of regulatory signal contingency and whole-genome scope.
  • The epigenome, comprising proteins and RNA associated with the genome, dictates cellular responses.

Purpose of the Study:

  • Introduce GenomicLayers, an R package for whole-genome epigenetic simulations.
  • Enable in silico testing of gene regulation models across eukaryotic organisms.
  • Facilitate refinement and combination of epigenetic models using diverse biological data.

Main Methods:

  • Utilizes rules-based simulations to model epigenetic state changes genome-wide.
  • Simulates accumulation of changes via user-specified binding factors.
  • Generates whole-genome predictions at 1 bp resolution by combining simulation runs.

Main Results:

  • Demonstrates yeast Saccharomyces cerevisiae telomere epigenetic mark models.
  • Presents human reference genome (hg38) model refinement for pluripotency factors.
  • Provides example models and R scripts within the GenomicLayers package and supplementary information.

Conclusions:

  • GenomicLayers empowers scientists to test gene regulation models in silico.
  • Facilitates research on epigenetic silencing, transcription factor binding, and regulator down-regulation.
  • Aims to enhance parameterization and refinement of epigenetic models by leveraging epigenome study data.