AFLP-SURV

Written by Xavier Vekemans

Description

AFLP-SURV estimates genetic diversity and population genetic structure from population samples analysed with AFLP or RAPD methods and computes genetic distance matrices between populations. The program starts by estimating allelic frequencies at each marker locus in each population assuming they are dominant and have only two alleles (a dominant marker allele coding for the presence of a band at a given position, and a recessive null allele coding for the absence of the band).

The user has to specify whether Hardy-Weinberg genotypic proportions can be assumed, or in contrast whether the organism is completely homozygous at marker loci (highly self-fertilising species or haploid species), or alternatively whether there are some known deviations from Hardy-Weinberg genotypic proportions. Based on these estimates of allelic frequencies, the program uses the approach of Lynch and Milligan (1994) to estimate genetic diversity and population genetic structure, which uses the average expected heterozygosity of the marker loci, or Nei's gene diversity, as a measure of genetic diversity.

The program also produces matrices of pairwise genetic distances between populations (with bootstraps) and of pairwise relatedness coefficients between individuals, and makes various tests of significance based on random permutations. It also computes the correlation between AFLP fragment sizes and frequencies when fragment sizes are provided in order to test the occurrence of size homoplasy (see Vekemans et al. 2002).

Computer requirement

AFLP-SURV runs on PC under Windows 9x or later versions, or under VirtualPC for Macintosh user's.

How to run AFLP-SURV

Sorry, AFLP-SURV has no nice user-friendly windows but it does not require installation. Just bring the AFLPsurv.exe file and the data files in some folder and double-click on AFLP.exe, the rest is written on the screen, but we suggest that you first have a look at the manual.

1. AFLPsurv.exe program
   

2. AFLPsurv.pdf manual
   

AutocorQ

Developed by Olivier Hardy

Description

AutocorQ is a simple software to characterise and test the spatial autocorrelation of quantitative traits. It computes Moran's I statistics, can provide pairwise autocorrelation coefficient between individuals, and computes the slope of the linear regression of these coefficients on the spatial distance or its logarithm. The significance of the coefficients is assessed by random permutation tests.

Computer requirement

AutocorQ runs on PC under Windows 9x or later versions, or under VirtualPC for Macintosh user's.

How to run AutocorQ

AutocorQ has no nice user-friendly windows but it does not require installation; just bring the AutocorQ.exe file and the data file (that you call "qin.txt") in some folder and double-click on AutocorQ.exe icon, the rest is written on the screen, but we suggest that you first have a look at the manual.

1. AutocorQ.exe program
   

2. AutocorQ.pdf manual
   

3. qin.txt data file for AutocorQ
   

How to cite BiodivR

Hardy, O.J. 2009. AutocorQ. A program to characterise and test the spatial autocorrelation of quantitative traits. http://ebe.ulb.ac.be/ebe/Software.html

BiodivR

A program to compute statistically unbiased indices of species diversity within sample and species similarity between samples using rarefaction principles.

Developed by Olivier Hardy

Description

BiodivR is designed to characterise species diversity within samples as well as species similarity between samples using sub-sampling (rarefaction) procedures. The statistics proposed account for the non-exhaustive sampling of species to get estimates unbiased with respect to the sizes (numbers of individuals recorded) of the samples.

The data required are abundances expressed as number of individuals of each species found in each sample (presence/absence data are not adequate). BiodivR is complementary to EstimateS (Colwell 2005), computing statistics not covered by the latter software. Data file required is compatible with EstimateS.

Regarding diversity statistics, it must be realized that the objective of EstimateS is to get global diversity estimates of a community from which different samples were taken, whereas BiodivR provides diversity estimates for each sample to allow comparisons among samples (for example, estimates can be regressed on explanatory variables or treated by an ANOVA).

Regarding similarity measures between samples, EstimateS and BiodivR have the same objective and BiodivR provides statistics not computed by EstimateS.

Diversity is expressed by Simpson’s diversity and the expected number of species found in a sub-sample of size k: Sk (which is a kind of generalisation of the former). Computation is done by analytical formula (Hurlbert 1971). Each of these diversity indices is also transformed into its "equivalent number of species" (the number of equi-frequent species that would lead to the same diversity index; Hardy, unpublished).

Similarity between samples is expressed by the Morisita or Morisita-Horn index and by their generalisations, the NESS and NNESS indices (Grassle and Smith 1976).

Computer requirement

BiodivR runs on PC under Windows 9x or later versions, or under VirtualPC for Macintosh user's.

How to run BiodivR

BiodivR has no nice user-friendly windows but it does not require installation; just bring the BiodivR.exe file and the data files in some folder and double-click on BiodivR.exe or bring the data file on the BiodivR.exe icon, the rest is written on the screen, but we suggest that you first have a look at the manual.

1. download BiodivR_1-2.rar
   

How to cite BiodivR

Hardy, O.J. 2010. BiodivR 1.2. A program to compute statistically unbiased indices of species diversity within sample and species similarity between samples using rarefaction principles. http://ebe.ulb.ac.be/ebe/Software.html

GCAligner

A program to perform a preliminary alignment of chemical data obtained by gas chromatography.

Developed by Simon Dellicour and Thomas Lecocq

Description

GCAligner 1.0 is a software designed to perform a preliminary alignment of chemical data obtained by gas chromatography. It was created to facilitate the comparison of multiple samples. The alignment algorithm is based on the comparison between each retention time, the following retention time in the same sample and its closest retention times in other samples. GCAligner is a java executable software running on any operating system. The input data file and the single output file containing the align data set are text files with tab delimited pieces of information.

See the software manual for further details.

1. GCAligner 1.0.jar program
   

2. GCAligner 1.0 Java classes.zip source code
   

3. GCAligner 1.0.pdf manual
   

4. GCAligner 1.0 input.txt data file
   

How to cite GCAligner

Dellicour S, Lecocq T (2011) GCAligner 1.0. A program to perform a preliminary alignment of chemical data obtained by gas chromatography. http://ebe.ulb.ac.be/ebe/Software.html

SPACoDi

Written by Olivier Hardy

Description

SPACoDi (Spatial & Phylogenetic Analysis of Community Diversity) is primarily designed to characterise the structure of communities using inventory data in the form of a presence/absence or abundance species-plot matrix. The structure can be interpreted according to the spatial position of plots (subcommunities) and/or according to the phylogenetic or functional relationships between species (community phylogenetic structure), following methods described in Hardy & Senterre (2007) and Hardy (2008). SPACoDi can also characterise the phylogenetic structure of species traits using phylogenetic autocorrrelograms.

SPACoDi is conceptually very similar to SPAGeDi («Spatial Pattern Analysis of Genetic Diversity»; Hardy & Vekemans 2002), a software designed to characterise the genetic structure of populations. Hence, many methods of data analysis are inspired by population genetics theory and practice, bridging a link between community ecology and population genetics.

To describe community structure, SPACoDi can compute various statistics describing (phylogenetic) diversity of plots and (phylogenetic) distances between plots in a pairwise fashion. To analyse how values of pairwise comparisons are related to geographical distances, SPACoDi computes 1°) average values for a set of predefined distance intervals, 2°) linear regressions of pairwise statistics on geographical distances (or their logarithm). The slopes of these regressions provide a synthetic measure of the strength of spatial structuring. SPACoDi can also treat data without spatial information, providing global estimates of plot differentiation and/or matrices of pairwise statistics between plots.

Different permutation procedures allow testing if there is significant plot differentiation, spatial structure, or if phylogenetic (or functional) distance between species carries relevant information about community structure (i.e. significant community phylogenetic structure).

Currently the version is still a beta release (0.10). It is possible that particular data sets cause a program crash. If you encounter such problems, please first check that data files are correctly formatted and that you use the last available version. If the problem persists, send me an e-mail describing it and I will try to solve it if I find time (sorry if you have to wait).

How to run SPACoDi

SPACoDi runs under Microsoft Windows but has no fancy windowing features. You can launch the program in two ways: 1. by a double click on the program which will ask you questions defining the instructions (an instruction file will be produced); 2. using a preset instruction file by dragging the icon of this file onto the icon of the SPACoDi program.

The instruction file is a simple text file specifying: 1. the types of data available and the names of files containing them, 2. the statistical analyses to perform. Each type of instruction is defined by a command (in uppercase letters) starting a new line followed by specific details (e.g. name of a file, computation parameters…).

1. SpaCoDi program for Windows (no installation required) with manual and example of data files
   

How to cite SPACoDi

Hardy OJ (2010) SPACoDi 0.10: a program for Spatial & Phylogenetic Analysis of Community Diversity. http://ebe.ulb.ac.be/ebe/Software.html

SPAGeDi

Developed by Olivier Hardy and Xavier Vekemans

Description

SPAGeDi (Spatial Pattern Analysis of Genetic Diversity) is a computer package primarily designed to characterise the spatial genetic structure of mapped individuals and/or mapped populations using genotype data of any ploidy level.

It can compute various statistics describing relatedness or differentiation between individuals or populations by pairwise comparisons, and analyse how these values are related to geographical distances, 1°) in a way similar to a spatial autocorrelation analysis, 2°) by linear regressions (the slopes of these regressions can be used to obtain indirect estimates of gene dispersal distances parameters such as neighbourhood size). SPAGeDi can also treat data without spatial information, providing global estimates of genetic differentiation and/or matrices of pairwise statistics between individuals or populations. Data from dominant markers such as AFLP or RAPD can also be treated to estimate pairwise kinship or relationship coefficients between individuals (Hardy 2003 pdf).

The statistics computed include Fst, Rst, Nst, Ds (Nei's standard genetic distance), and (delta mu)2 (Goldstein and Pollok 1997) for analyses at the population level and, for analyses at the individual level, pairwise kinship, relatedness and fraternity coefficients (with different estimators for each) as well as Rousset's distance between individuals and a kinship analogue based on allele size. Jackknife over loci gives approximate standard errors. Permutations of locations, individuals or genes provide ad hoc tests of spatial structure, population differentiation or inbreeding, respectively. A new allele size permutation test also allows to check whether microsatellite allele sizes carry a relevant information about genetic structure (Hardy et al. 2003 pdf).

In addition, the actual variance of the statistics can be estimated following the method of Ritland (2000), providing a measure necessary for marker-based inference of the heritability or Qst of quantitative traits.

New version (released 1 April 2009). What's new in SPAGeDi ver 1.3?

Compared to the previous version 1.2, there are several major improvements in this version :

1°) SPAGeDi 1.3 can now be compiled for different platforms including Windows, Unix (Mac) and Linux. A portable source code (Unix port) has been created by Reed A. Cartwright (reed@scit.us). Thanks Reed!

2°) The iterative procedure to estimate gene dispersal parameters has been improved.

3°) A new statistic (Nij) characterises similarity between individuals using “ordered alleles”. It is an analogue of kinship coefficient considering the phylogenetic distance between alleles (or haplotypes). Permutation tests permit to assess whether the allele phylogeny contributes to the genetic structure, providing a test of phylogeographic patterns at the individual level.

4°) Spatial coordinates can now be given as latitudes and longitudes in degrees with decimal (using negative numbers for Southern latitudes or Western longitudes). To this end, the number of spatial coordinates (3rd number of the first line) must be set to -2.

Computer requirement

SPAGeDi runs on different platforms using a simple executable (Windows) or through installers (Windows, Mac, Linux, Unix). Source code is also available to compile versions.

How to run SPAGeDi

SPAGeDi has no nice user-friendly windows; just launch SPAGeDi.exe and you can drag-and-drop the data file into the window, the rest is written on the screen, but we suggest that you first have a look at the manual.

1. SPAGeDi-1.3d.exe program for Windows (no installation required)
   

2. SPAGeDi-1.3a-win32.exe for Windows
   

3. SPAGeDi-1.3a-Mac.dmg for Mac OS X
   

4. SPAGeDi-1.3a-Linux.tar.gz package to compile for Linux (Linux Binary Package, 2.6 kernel)
   

5. SPAGeDi-1.3a-FrBSD.tar.gz package to compile for Unix (FreeBSD Binary Package, 7.1 kernel)
   

6. SPAGeDi_1-3.pdf manual
   

7. data.zip data files for SPAGeDi
   

How to cite SPAGeDi

Hardy OJ, Vekemans X (2002) SPAGeDi: a versatile computer program to analyse spatial genetic structure at the individual or population levels. Molecular Ecology Notes 2: 618-620. Spagedi_MENotes2002.pdf

SPADS

Developed by Simon Dellicour and Patrick Mardulyn

Description

SPADS 1.0 (for “Spatial and Population Analysis of DNA Sequences”) is a population genetics software allowing to perform a multi-loci SAMOVA (spatial analysis of molecular variance; Dupanloup et al. 2002) and a multi-loci clustering with the Monmonier algorithm (Monmonier 1973). The program also proposes classical locus-by-locus SAMOVA as implemented in the software SAMOVA (Dupanloup et al. 2002), locus-by-locus clustering with the Monmonier algorithm as implemented in the software BARRIER 2.2 (Mani et al. 2004), and the computation of several summary statistics used in population genetics and phylogeography.

Tests in progress

1. SPADS 1.0.jar program
   

2. SPADS 1.0 (Java classes).zip source code
   

3. SPADS 1.0.pdf manual
   

4. SPADS 1.0.zip data files
   

How to cite SPADS

Dellicour S, Mardulyn P (2011) SPADS. A program to perform a multi-loci SAMOVA and a multi-loci clustering with the Monmonier algorithm. http://ebe.ulb.ac.be/ebe/Software.html

TOROCOR

A program to assess the association between spatially autocorrelated variables using a torus-translation test on multiple grids.

Developed by Olivier Hardy

Description

TOROCOR is designed (1) to characterise the spatial autocorrelation of quantitative and/or qualitative variables and (2) to test the significance of the association between variables, notably using torus-translation randomisations. The latter procedure removes the bias of classical tests applied on spatially autocorrelated variables where samples cannot be considered as independent (classical tests tend to be liberal, i.e. rejecting too often the null hypothesis that there is no association between variables).

The data required are geographically located sample points where a set of quantitative and/or qualitative variables are defined. In addition, to apply torus-translation tests, each sample must be positioned on one or several rectangular grids (otherwise only spatial autocorrelation analysis and association tests using potentially biased complete randomisation tests can be performed). Missing data are allowed but should occur at low frequency.

The spatial autocorrelation of variables is described using Moran’s I statistic for quantitative variables and an analogue of Moran's I for qualitative variables. To quantify the magnitude of the spatial autocorrelation by a single statistic, autocorrelation values between samples are regressed on the distance between samples, or its logarithm, providing regression slopes. Upon request, these regression slopes can be assessed for a restricted range of distances. Spatial autocorrelation is tested by performing complete randomisations, whereby the values of a variable are randomly shuffled among all sample points. Autocorrelation per distance intervals as well as the regression slopes are recomputed for many randomised data sets to assess their distributions under the null hypothesis that there is no spatial structure. P-vales are estimated by comparing the observed statistics with their respective distributions, and 95% envelopes are constructed. Tests based on torus-translations can also be performed upon request. This allows to control whether the spatial structure is affected by the torus-translation procedure to be used when testing the association between variables.

TOROCOR characterises the association between variables (1) by Pearson’s correlation coefficient for two quantitative variables, (2) by the Khi-square statistic derived from a contingency table for two qualitative variables, (3) by the intra-class correlation coefficient when a quantitative variable is classified by the qualitative variable (ANOVA). Significance of the observed values are established from their distributions obtained from a large number (e.g. 4999 to reach a 3-digit precision on p-values) of torus-translations. Upon request, complete randomisations can also be performed and would be valid to test the association between variables when at least one shows no spatial autocorrelation. Comparison of results under complete randomisations and torus-translations also allow to evaluate the risk of having false positives when applying tests of association ignoring spatial autocorrelation.

Computer requirement

TOROCOR runs on PC under Windows 9x or later versions.

How to run TOROCOR

TOROCOR has no nice user-friendly windows but it does not require installation; just bring the Torocor.exe file and the data files in some folder and drag-and-drop the icon of the data file on the Torocor.exe icon, the rest is written on the screen, but we suggest that you first have a look at the manual.

1. Torocor_1-0.exe program
   

2. Manual_Torocor_1-0.pdf manual
   

3. data.txt data file for TOROCOR
   

How to cite TOROCOR

Hardy, O.J. 2009. TOROCOR: a program to assess the association between spatially autocorrelated variables using a torus-translation test on multiple grids. http://ebe.ulb.ac.be/ebe/Software.html

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