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SLiM 5: Eco-evolutionary Simulations Across Multiple Chromosomes and Full Genomes.

Benjamin C Haller1, Peter L Ralph2,3, Philipp W Messer1

  • 1Department of Computational Biology, Cornell University, Ithaca, NY 14853, USA.

Molecular Biology and Evolution
|November 26, 2025
PubMed
Summary
This summary is machine-generated.

SLiM 5 now supports simulating up to 256 chromosomes of various types, including sex chromosomes and organelle DNA. This major update enhances evolutionary simulations for population genetics and evolutionary ecology research.

Keywords:
Drosophilafull-genomemulti-chromosomesex chromosomessimulationwhole-genome

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Area of Science:

  • Population Genetics
  • Evolutionary Ecology
  • Computational Biology

Background:

  • Evolutionary simulations are crucial for population genetics and evolutionary ecology.
  • The SLiM framework was previously limited to single-chromosome simulations, hindering multi-chromosome and full-genome studies.
  • Modeling diverse chromosome types like sex chromosomes was cumbersome.

Purpose of the Study:

  • To introduce SLiM 5, a significant extension of the SLiM simulation framework.
  • To enable realistic evolutionary simulations of multiple chromosomes, including sex chromosomes and organelle DNA.
  • To overcome previous limitations in simulating complex genomic architectures.

Main Methods:

  • Extended SLiM's core functionality to support up to 256 chromosomes.
  • Integrated multi-chromosome support across reproduction, inheritance, and data I/O (e.g., VCF).
  • Enhanced SLiMgui for multi-chromosome model visualization and provided new manual recipes.

Main Results:

  • SLiM 5 now allows modeling of diverse chromosome types: autosomes (diploid/haploid), sex chromosomes (X, Y, Z, W), and organelle DNA (mitochondrial, chloroplast).
  • Full-genome simulations with complex chromosomal arrangements are now feasible.
  • Tree-sequence recording and data output support multiple chromosomes.

Conclusions:

  • SLiM 5 removes a major barrier to full-genome evolutionary simulations.
  • The enhanced capabilities enable greater realism and complexity in population genetics and evolutionary ecology models.
  • This advancement opens new possibilities for studying genome evolution.