PaintMyChromosomes.com
fineSTRUCTURE v2 & GLOBETROTTER

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© 2012 Daniel Lawson.
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Software available at this site


  • FineSTRUCTURE version 2, a pipeline for running ChromoPainter and FineSTRUCTURE for population inference. A GUI is available for interpretation. Download from the Downloads page.
  • FineSTRUCTURE R scripts, a facility for exploring the results when the GUI is unavailable.
  • GLOBETROTTER, the admixture dating method based on ChromoPainter. Download from the Downloads page.
  • badMIXTURE, An R package to inmterpret the results of ADMIXTURE and STRUCTURE-like mixture models.
  • RADpainter, finestructure and ChromoPainter for RAD tag data used for non-model organisms.
  • Scripts to perform many types of conversion. Included in the main software download from the Downloads page.

  • What this page is

    This page provides information about and downloads for methodology for Chromosome Painting. It is not a facility to analyse your genome. Sorry if you were misled by the punchy name!

    About Chromosome Painting

    Painting is an efficient way of identifying important haplotype information from dense genotype data. It describes ancestry in an efficient way suitable for a range of further analyses, including population identification and admixture dating.
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    The included ChromoPainter tool finds `haplotypes' in sequence data.  Each individual is "painted" as a combination of all other sequences. ChromoPainter can output a range of features, including:
    • Sample haplotypes
    • Expectations of the number of recombination events at all sites
    • A wide range of related features
    It is useful to generate high quality Principal Components Analysis (PCA) from dense data, for clustering individuals with FineSTRUCTURE, for dating admixture events, and much more.

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    About fineSTRUCTURE

    fineSTRUCTURE is a fast and powerful algorithm for identifying population structure using dense sequencing data.  By using the output of ChromoPainter as a (nearly) sufficient summary statistic, it is able to perform model-based Bayesian clustering on large datasets, including full resequencing data, and can handle up to 1000s of individuals. Full assignment uncertainty is given.
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