StructHDP 1.1 – Inference of number of Clusters and Population Structure from Admixed Genotype data.

StructHDP 1.1

:: DESCRIPTION

StructHDP is a program for automatically inferring the population structure and number of clusters from a sample of admixed genotype data. It extends the model used by Structure to allow for a potentially infinite number of populations and then chooses the number of populations that best explain the data.

::DEVELOPER

Suyash Shringarpure

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / Windows / MacOsX
C++ Compiler

:: DOWNLOAD

StructHDP

:: MORE INFORMATION

Citation

StructHDP: automatic inference of number of clusters and population structure from admixed genotype data Suyash Shringarpure;
Suyash Shringarpure ,Daegun Won; Eric P. Xing
Bioinformatics 2011 27: i324-i332

Clumpak 20150326 – Model-based Population Structure Analysis

Clumpak 20150326

:: DESCRIPTION

Clumpak (Cluster Markov Packager Across K) is a method that automates the postprocessing of results of model-based population structure analyses.

::DEVELOPER

Mayrose Lab, Tel Aviv University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux /MacOsX
Perl

:: DOWNLOAD

Clumpak

:: MORE INFORMATION

Citation:

Mol Ecol Resour. 2015 Feb 12. doi: 10.1111/1755-0998.12387.
Clumpak: a program for identifying clustering modes and packaging population structure inferences across K.
Kopelman NM1, Mayzel J, Jakobsson M, Rosenberg NA, Mayrose I.

fineSTRUCTURE 4.0.1 – Identify Population Structure using Dense Sequencing Data

fineSTRUCTURE 4.0.1

:: DESCRIPTION

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.

::DEVELOPER

Daniel Lawson

:: SCREENSHOTS

:: REQUIREMENTS

Linux / Windows with MinGW/ MacOsX

:: DOWNLOAD

fineSTRUCTURE

:: MORE INFORMATION

Citation

Lawson, Hellenthal, Myers, and Falush (2012),
“Inference of population structure using dense haplotype data“,
PLoS Genetics, 8 (e1002453).

PCAj – Population Structure Prediction System for Japanese

PCAj

:: DESCRIPTION

PCAj (Principal component analysis for Japanese)predicts population structure of Japanese samples using genome-wide SNP genotypes. It creates a 2D scatterplot of predicted principal components based on the probabilistic PCA.

::DEVELOPER

Kumasaka Natsuhiko

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / MacOsX / Windows
Java

:: DOWNLOAD

PCAj

:: MORE INFORMATION

Citation

Kumasaka et al. (2010)
Establishment of a Standardized System to Perform Population Structure Analyses with Limited Sample Size or with Different Sets of SNP Genotypes.
Journal of Human Genetics, 55(8):525-33.

Structurama – Infer Population Structure from Genetic Data

Structurama

:: DESCRIPTION

Structurama is a program for inferring population structure from genetic data. The program assumes that the sampled loci are in linkage equilibrium and that the allele frequencies for each population are drawn from a Dirichlet probability distribution. The method implements two different models for population structure. First, Structurama implements the method of Pritchard et al. (2000) in which the number of populations is considered fixed. The program also allows the number of populations to be a random variable following a Dirichlet process prior (Pella and Masuda, 2006; Huelsenbeck and Andolfatto, 2007).

::DEVELOPER

Structurama Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Mac OsX / Linux

:: DOWNLOAD

Structurama

:: MORE INFORMATION

Citation:

John P. Huelsenbeck, Peter Andolfatto, and Edna T. Huelsenbeck
Structurama: Bayesian inference of population structure
Evol Bioinform Online. 2011; 7: 55–59.

MANTEL-STRUCT 1.0 – Tests for Population Structure through the use of Mantel Tests

MANTEL-STRUCT 1.0

:: DESCRIPTION

MANTEL-STRUCT is a program that tests for population structure through the use of Mantel Tests.

::DEVELOPER

Mark P. Miller

:: SCREENSHOTS

:: REQUIREMENTS

Windows

:: DOWNLOAD

MANTEL-STRUCT

:: MORE INFORMATION

Citation:

Miller (1999),
“MANTEL-STRUCT: a program for the detection of population structure via mantel tests“,
Journal of Heredity, 90:258-259.

LAPSTRUCT 1.0 – Geometric Approach to Describe Population Structure

LAPSTRUCT 1.0

:: DESCRIPTION

LAPSTRUCT is a free program to describe population structure using biomarker data ( typically SNPs, CNVs etc.) available in a population sample. The main features different from PCA are: (1)geometrically motivated and graphic model based; (2)robustness of outliers.

::DEVELOPER

Jun Zhang

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Windows / Linux / MacOsX
R Package

:: DOWNLOAD

LAPSTRUCT

:: MORE INFORMATION

Citation

Zhang J, Niyogi P and McPeek MS (2009)
“Laplacian eigenfunctions learn population structure“.
PLoS ONE, 4(12): e7928. doi:10.1371/journal.pone.0007928

TIPS 1.0beta – Tree-guided Bayesian inference of Population Structures

TIPS 1.0beta

:: DESCRIPTION

TIPS is a tree based Bayesian detection method of subtle population structures.

::DEVELOPER

Yu Zhang

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux

:: DOWNLOAD

TIPS

:: MORE INFORMATION

Citation

Bioinformatics. 2008 Apr 1;24(7):965-71. Epub 2008 Feb 22.
Tree-guided Bayesian inference of population structures.
Zhang Y.

KING 1.4 – Identify Family and Population Structure

KING 1.4

:: DESCRIPTION

KING (Kinship-based INference for Gwas) is a toolset for identifing family and population structure using genome-wide SNP data. KING can be used to check family relationship and flag pedigree errors by estimating all kinship coefficients for all pairwise relationships. Unrelated pairs can be well separated from close relatives (up to 3rd-degree) and vice versa.

::DEVELOPER

Wei-Min Chen

:: SCREENSHOTS

N/A

::REQUIREMENTS

Linux / Windows / MacOsX

:: DOWNLOAD

KING

:: MORE INFORMATION

Citation

Manichaikul A, Mychaleckyj JC, Rich SS, Daly K, Sale M, Chen WM (2010)
Robust relationship inference in genome-wide association studies.
Bioinformatics 26(22):2867-2873