{"title":"AncestryPainter 2.0: Visualizing Ancestry Composition and Admixture History Graph.","authors":"Shuanghui Chen, Chang Lei, Xiaohan Zhao, Yuwen Pan, Dongsheng Lu, Shuhua Xu","doi":"10.1093/gbe/evae249","DOIUrl":null,"url":null,"abstract":"<p><p>The earlier vertion of AncestryPainter is a Perl program to display the ancestry composition of numerous individuals using a rounded graph. Motivated by the requests of users in practical applications, we updated AncestryPainter to version 2.0 by coding in an R package and improving the layout, providing more options and compatible statistical functions for graphing. Apart from improving visualization functions per se in this update, we added an extra graphing module to visualize genetic distance through radial bars of varying lengths surrounding a core. Notably, AncestryPainter 2.0 implements a method admixture history graph (AHG) to infer the admixture sequence of multiple ancestry populations, and allows for multiple pie charts at the center of the graph to display the ancestry composition of more than one target population. We validated the six AHG metrics using both simulated and real data and implemented a Pearson coefficient-based metric with the best performance in AncestryPainter 2.0. Furthermore, a statistical module to merge ancestry proportion matrices. AncestryPainter 2.0 is freely available at https://github.com/Shuhua-Group/AncestryPainterV2 and https://pog.fudan.edu.cn/#/Software.</p>","PeriodicalId":12779,"journal":{"name":"Genome Biology and Evolution","volume":" ","pages":""},"PeriodicalIF":3.2000,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Genome Biology and Evolution","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1093/gbe/evae249","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"EVOLUTIONARY BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
The earlier vertion of AncestryPainter is a Perl program to display the ancestry composition of numerous individuals using a rounded graph. Motivated by the requests of users in practical applications, we updated AncestryPainter to version 2.0 by coding in an R package and improving the layout, providing more options and compatible statistical functions for graphing. Apart from improving visualization functions per se in this update, we added an extra graphing module to visualize genetic distance through radial bars of varying lengths surrounding a core. Notably, AncestryPainter 2.0 implements a method admixture history graph (AHG) to infer the admixture sequence of multiple ancestry populations, and allows for multiple pie charts at the center of the graph to display the ancestry composition of more than one target population. We validated the six AHG metrics using both simulated and real data and implemented a Pearson coefficient-based metric with the best performance in AncestryPainter 2.0. Furthermore, a statistical module to merge ancestry proportion matrices. AncestryPainter 2.0 is freely available at https://github.com/Shuhua-Group/AncestryPainterV2 and https://pog.fudan.edu.cn/#/Software.
期刊介绍:
About the journal
Genome Biology and Evolution (GBE) publishes leading original research at the interface between evolutionary biology and genomics. Papers considered for publication report novel evolutionary findings that concern natural genome diversity, population genomics, the structure, function, organisation and expression of genomes, comparative genomics, proteomics, and environmental genomic interactions. Major evolutionary insights from the fields of computational biology, structural biology, developmental biology, and cell biology are also considered, as are theoretical advances in the field of genome evolution. GBE’s scope embraces genome-wide evolutionary investigations at all taxonomic levels and for all forms of life — within populations or across domains. Its aims are to further the understanding of genomes in their evolutionary context and further the understanding of evolution from a genome-wide perspective.