Amado, André; Li, Juan; Bank, Claudia (2023). STUN: forward-time simulation on TUnable fitNess landscapes in recombining populations. Bioinformatics advances, 3(1), vbad164. Oxford University Press 10.1093/bioadv/vbad164
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MOTIVATION
Understanding the population genetics of complex polygenic traits during adaptation is challenging.
RESULTS
Here, we implement a forward-in-time population-genetic simulator (STUN) based on Wright-Fisher dynamics. STUN is a flexible and user-friendly software package for simulating the polygenic adaptation of recombining haploid populations using either new mutations or standing genetic variation. STUN assumes that populations adapt to sudden environmental changes by undergoing selection on a new fitness landscape. With pre-implemented fitness landscape models like Rough Mount Fuji, NK, Block, additive, and House-of-Cards, users can explore the effect of different levels of epistasis (ruggedness of the fitness landscape). Custom fitness landscapes and recombination maps can also be defined. STUN empowers both experimentalists and advanced programmers to study the evolution of complex polygenic traits and to dissect the adaptation process.
AVAILABILITY AND IMPLEMENTATION
STUN is implemented in Rust. Its source code is available at https://github.com/banklab/STUN and archived on Zenodo under doi: 10.5281/zenodo.10246377. The repository includes a link to the software's manual and binary files for Linux, macOS and Windows.
Item Type: |
Journal Article (Original Article) |
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Division/Institute: |
08 Faculty of Science > Department of Biology > Institute of Ecology and Evolution (IEE) > Population Genetics 08 Faculty of Science > Department of Biology > Institute of Ecology and Evolution (IEE) 08 Faculty of Science > Department of Biology > Institute of Ecology and Evolution (IEE) > Theoretical Ecology and Evolution |
UniBE Contributor: |
da Conceição Amado, André, Li, Juan, Bank, Claudia |
Subjects: |
500 Science > 570 Life sciences; biology |
ISSN: |
2635-0041 |
Publisher: |
Oxford University Press |
Language: |
English |
Submitter: |
Pubmed Import |
Date Deposited: |
12 Dec 2023 13:11 |
Last Modified: |
17 Dec 2023 02:33 |
Publisher DOI: |
10.1093/bioadv/vbad164 |
PubMed ID: |
38075480 |
BORIS DOI: |
10.48350/190156 |
URI: |
https://boris.unibe.ch/id/eprint/190156 |
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