{"title":"PopCluster:一个基于群体遗传学模型的工具集,用于模拟、推断和可视化个体混合和群体结构。","authors":"Jinliang Wang","doi":"10.1111/1755-0998.14058","DOIUrl":null,"url":null,"abstract":"<p><p>In this computer note I introduce software, PopCluster, that implements a new likelihood method for unsupervised population structure analysis from marker data. To infer a coarse population structure, it assumes the mixture model and adopts a simulated annealing algorithm to make a maximum likelihood clustering analysis, partitioning the sampled individuals into a predefined number of clusters. To deduce a fine population structure, it further assumes the admixture model and employs an expectation maximisation algorithm to estimate individual admixture proportions. PopCluster has many features. First, it is one of just a couple of model-based methods that can handle both biallelic and multiallelic markers in the same framework. Second, it is the first population structure analysis method that uses both Message Passing Interface (MPI) and openMP to exploit multiple CPUs with both shared and distributed memories and has the capacity to handle genomic data with millions of individuals and millions of loci. Third, the algorithms for both mixture and admixture analyses are fast, rendering PopCluster favourably in computational efficiency over previous methods in analysing genomic data. Fourth, PopCluster is built for Windows, Linux and Mac platforms, and its Windows version has an integrated GUI that can conveniently visualise analysis results and facilitate data input. Fifth, its Windows version has a built-in simulation module designed to simulate genotype data under admixture, hybridization or migration models. PopCluster provides a valuable toolset for researchers to simulate, infer and visualise individual admixture and population genetic structure, hybridization and migration using marker data.</p>","PeriodicalId":211,"journal":{"name":"Molecular Ecology Resources","volume":" ","pages":"e14058"},"PeriodicalIF":5.5000,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"PopCluster: A Population Genetics Model-Based Toolset for Simulating, Inferring and Visualising Individual Admixture and Population Structure.\",\"authors\":\"Jinliang Wang\",\"doi\":\"10.1111/1755-0998.14058\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>In this computer note I introduce software, PopCluster, that implements a new likelihood method for unsupervised population structure analysis from marker data. To infer a coarse population structure, it assumes the mixture model and adopts a simulated annealing algorithm to make a maximum likelihood clustering analysis, partitioning the sampled individuals into a predefined number of clusters. To deduce a fine population structure, it further assumes the admixture model and employs an expectation maximisation algorithm to estimate individual admixture proportions. PopCluster has many features. First, it is one of just a couple of model-based methods that can handle both biallelic and multiallelic markers in the same framework. Second, it is the first population structure analysis method that uses both Message Passing Interface (MPI) and openMP to exploit multiple CPUs with both shared and distributed memories and has the capacity to handle genomic data with millions of individuals and millions of loci. Third, the algorithms for both mixture and admixture analyses are fast, rendering PopCluster favourably in computational efficiency over previous methods in analysing genomic data. Fourth, PopCluster is built for Windows, Linux and Mac platforms, and its Windows version has an integrated GUI that can conveniently visualise analysis results and facilitate data input. Fifth, its Windows version has a built-in simulation module designed to simulate genotype data under admixture, hybridization or migration models. PopCluster provides a valuable toolset for researchers to simulate, infer and visualise individual admixture and population genetic structure, hybridization and migration using marker data.</p>\",\"PeriodicalId\":211,\"journal\":{\"name\":\"Molecular Ecology Resources\",\"volume\":\" \",\"pages\":\"e14058\"},\"PeriodicalIF\":5.5000,\"publicationDate\":\"2024-12-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Molecular Ecology Resources\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1111/1755-0998.14058\",\"RegionNum\":1,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BIOCHEMISTRY & MOLECULAR BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Molecular Ecology Resources","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1111/1755-0998.14058","RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
PopCluster: A Population Genetics Model-Based Toolset for Simulating, Inferring and Visualising Individual Admixture and Population Structure.
In this computer note I introduce software, PopCluster, that implements a new likelihood method for unsupervised population structure analysis from marker data. To infer a coarse population structure, it assumes the mixture model and adopts a simulated annealing algorithm to make a maximum likelihood clustering analysis, partitioning the sampled individuals into a predefined number of clusters. To deduce a fine population structure, it further assumes the admixture model and employs an expectation maximisation algorithm to estimate individual admixture proportions. PopCluster has many features. First, it is one of just a couple of model-based methods that can handle both biallelic and multiallelic markers in the same framework. Second, it is the first population structure analysis method that uses both Message Passing Interface (MPI) and openMP to exploit multiple CPUs with both shared and distributed memories and has the capacity to handle genomic data with millions of individuals and millions of loci. Third, the algorithms for both mixture and admixture analyses are fast, rendering PopCluster favourably in computational efficiency over previous methods in analysing genomic data. Fourth, PopCluster is built for Windows, Linux and Mac platforms, and its Windows version has an integrated GUI that can conveniently visualise analysis results and facilitate data input. Fifth, its Windows version has a built-in simulation module designed to simulate genotype data under admixture, hybridization or migration models. PopCluster provides a valuable toolset for researchers to simulate, infer and visualise individual admixture and population genetic structure, hybridization and migration using marker data.
期刊介绍:
Molecular Ecology Resources promotes the creation of comprehensive resources for the scientific community, encompassing computer programs, statistical and molecular advancements, and a diverse array of molecular tools. Serving as a conduit for disseminating these resources, the journal targets a broad audience of researchers in the fields of evolution, ecology, and conservation. Articles in Molecular Ecology Resources are crafted to support investigations tackling significant questions within these disciplines.
In addition to original resource articles, Molecular Ecology Resources features Reviews, Opinions, and Comments relevant to the field. The journal also periodically releases Special Issues focusing on resource development within specific areas.