sstar: A Python Package for Detecting Archaic Introgression from Population Genetic Data with S*

S* is a widely used statistic for detecting archaic admixture from population genetic data. Previous studies used freezing-archer to apply S*, which is only directly applicable to the specific case of Neanderthal and Denisovan introgression in Papuans. Here, we implemented sstar for a more general p...

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Detalles Bibliográficos
Autores principales: Huang, Xin, Kruisz, Patricia, Kuhlwilm, Martin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Brief Communications
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9677471/
https://www.ncbi.nlm.nih.gov/pubmed/36181428
http://dx.doi.org/10.1093/molbev/msac212
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author Huang, Xin
Kruisz, Patricia
Kuhlwilm, Martin
author_facet Huang, Xin
Kruisz, Patricia
Kuhlwilm, Martin
author_sort Huang, Xin
collection PubMed
description S* is a widely used statistic for detecting archaic admixture from population genetic data. Previous studies used freezing-archer to apply S*, which is only directly applicable to the specific case of Neanderthal and Denisovan introgression in Papuans. Here, we implemented sstar for a more general purpose. Compared with several tools, including SPrime, SkovHMM, and ArchaicSeeker2.0, for detecting introgressed fragments with simulations, our results suggest that sstar is robust to differences in demographic models, including ghost introgression and two-source introgression. We believe sstar will be a useful tool for detecting introgressed fragments in various scenarios and in non-human species.
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spelling pubmed-96774712022-11-21 sstar: A Python Package for Detecting Archaic Introgression from Population Genetic Data with S* Huang, Xin Kruisz, Patricia Kuhlwilm, Martin Mol Biol Evol Brief Communications S* is a widely used statistic for detecting archaic admixture from population genetic data. Previous studies used freezing-archer to apply S*, which is only directly applicable to the specific case of Neanderthal and Denisovan introgression in Papuans. Here, we implemented sstar for a more general purpose. Compared with several tools, including SPrime, SkovHMM, and ArchaicSeeker2.0, for detecting introgressed fragments with simulations, our results suggest that sstar is robust to differences in demographic models, including ghost introgression and two-source introgression. We believe sstar will be a useful tool for detecting introgressed fragments in various scenarios and in non-human species. Oxford University Press 2022-10-01 /pmc/articles/PMC9677471/ /pubmed/36181428 http://dx.doi.org/10.1093/molbev/msac212 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of Society for Molecular Biology and Evolution. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Brief Communications
Huang, Xin
Kruisz, Patricia
Kuhlwilm, Martin
sstar: A Python Package for Detecting Archaic Introgression from Population Genetic Data with S*
title sstar: A Python Package for Detecting Archaic Introgression from Population Genetic Data with S*
title_full sstar: A Python Package for Detecting Archaic Introgression from Population Genetic Data with S*
title_fullStr sstar: A Python Package for Detecting Archaic Introgression from Population Genetic Data with S*
title_full_unstemmed sstar: A Python Package for Detecting Archaic Introgression from Population Genetic Data with S*
title_short sstar: A Python Package for Detecting Archaic Introgression from Population Genetic Data with S*
title_sort sstar: a python package for detecting archaic introgression from population genetic data with s*
topic Brief Communications
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9677471/
https://www.ncbi.nlm.nih.gov/pubmed/36181428
http://dx.doi.org/10.1093/molbev/msac212
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