Evidence map›Paper›PMID 36181428›Full record

ArticleMolecular biology and evolution2022

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

Xin Huang, Patricia Kruisz, Martin Kuhlwilm

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Article in Molecular biology and evolution, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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0cells of the map it votes in
4citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

3 authors.

Xin HuangDepartment of Evolutionary Anthropology, University of Vienna, Djerassiplatz 1, 1030 Vienna, Austria.ORCID 0000-0002-9918-9602
Patricia KruiszDepartment of Bio Data Science, Faculty of Engineering, University of Applied Sciences Wiener Neustadt, Biotech Campus Tulln, Konrad Lorenz-Straße 10, 3430 Tulln, Austria.
Martin KuhlwilmDepartment of Evolutionary Anthropology, University of Vienna, Djerassiplatz 1, 1030 Vienna, Austria.ORCID 0000-0002-0115-1797

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Genome, HumanNeanderthalsAnimalsGenetics, PopulationHumansarchaic admixtureintrogressionPythonS*

Identifiers

PMID36181428
PMCPMC9677471

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.