Software

Python implementation and exemplary input files of the Gibbs sampling approach for accurate age estimation in small-scale societies presented in Diekmann et al., PNAS, 2017. The source code is work in progress, we are currently working on an R package with extended functionality and proper documentation that will allow for easier use."

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R script to perform age-at-death data analyses as published in GERBAULT, GILLIS, VIGNE, TRESSET, BREHARD, THOMAS, 2016. "Statistically Robust Representation and Comparison of Mortality Profiles in Archaeozoology", Journal of Archaeological Science (2016) 

SUMMARY: We propose the use of the Dirichlet distribution and present an approach to perform age-at-death multivariate graphical comparisons. We show that the Dirichlet distribution in age-at-death analysis can be used: (i) to generate Bayesian credible intervals around each age class of a mortality profile, even when not all age classes are observed; and (ii) to create 95% kernel density contours around each age-at-death frequency distribution when multiple sites are compared using correspondence analysis. The statistical procedure we present is applicable to the analysis of any categorical count data and particularly well-suited to archaeological data (e.g. potsherds, arrow heads) where sample sizes are typically small.

 For age at death analysis, this R script is provided with the following files:

LaplacesDemon_15.03.19.tar.gz is one of the R libraries used that SHOULD be installed prior to running. PayneAgeClasses.csv is a text file containing the age class definition and is OPTIONAL, only useful to perform age-at-death analyses from tooth wear and development from sheep/goat data. ageAtDeath_Dirichlet_CA_DATATABLE.txt is an example file, the one that was used in Gerbault et al. JAS 2016 to exemplify this approach.

This is a set of Matlab/Octave functions to estimate Time to Most Recent Common Ancestor in a clade for which linked microsatellite data are available. The methods assume that the haplotype of the root ancestor is known. This software was first described in Behar et al. (2003), AJHG 73: 768-79

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This is a set of functions to test for a significant difference in genetic diversity h between two populations, based on samples of haplotypes at a single locus. Both frequentist and Bayesian solutions are presented, and the relative merits discussed. The functions are implemented both in Matlab/Octave and in R/S-Plus. This software was first described in Thomas et al. (2002), AJHG 70: 1411-1420

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FLIP stands for "Flexible Likelihood Inference on Populations". It is made up of a series of Matlab/Octave functions that implement the "Monte Carlo Likelihood" method outlined in the paper "Y chromosome evidence for Anglo-Saxon mass migration" by M.E. Weale, D.A. Weiss, R.F. Jager, N. Bradman and M.G. Thomas, Molecular Biology and Evolution 19: 1008-1021.

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This is an R program to run a Monte Carlo method to test departure from expected phenotype levels under the null hypothesis that this is caused by complete association with a fully penetrant binary locus, together with phenotyping error. This method was first described in Mulcare et al. (2004), AJHG 74:1102-1110.

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This is a set of Matlab/Octave functions to analyse haplotype frequency data from several populations, calculate statistics that measure the degree of diversity within populations and the genetic distance between populations, and perform bootstrap tests to look for significant differences in these statistics among different populations.

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This is an R routine to perform Mantel-type permutation testing of the partial correlation of matrix A on B given C. User defines the form of A, B and C. This software was first described in Nadkarni et al. (2005), J Biol Rhythms 20:490-9.

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This is a set of Matlab routines for selecting and evaluating tagging SNPs. The most recent version includes automated routines for selecting tagging SNPs for large candidate gene lists using HapMap data. The methods implemented in TagIT are described in Weale et al. (2003), AJHG 73: 551-565, in Goldstein et al. (2003), Trends in Genetics 19: 615-622 and in Ahmadi et al (2005), Nature Genetics 37: 84-89.

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This is a set WinBUGS routines for Bayesian estimation of the boundaries of an associated interval around a SNP that has been nominated as having an association with a phenotype of interest. The associated interval is defined as the region within which other SNPs are in high enough LD with the nominated SNP to be also considered as putative functionals. The methods implemented in AssocInt are described in Supplementary Material of Soranzo et al. (2004), Genome Research 14: 1333-44.

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This is a WinZip file that contains PC-only stand alone software that takes aligned HVS-1 sequence data in .GDE flat format and produces a list of Variable Sites Only (VSO) for each sample and tentatively groups them into mtDNA haplogroups which can be used to type specific coding region markers as described by Holmquist et al 2006 (in preparation). This software also joins together samples that have been sequenced multiple times. For full description and instructions see readme file within Zip file. It must be extracted using a recent version of WinZip.

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This is a WinZip file that contains PC-only stand alone software that takes Variable Sites Only (VSO) lists from HVS-1 sequence data and tentatively groups samples into mtDNA haplogroups which can be used to type specific coding region markers as described by Holmquist et al 2006 (in preparation). For full description and instructions see readme file within Zip file. It must be extracted using a recent version of WinZip.

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This Excel spreadsheet with embedded formulae acts as a template for inserting data generated using either WinMTSEQreader or WinVSOreader software in order to type appropriate mtDNA coding region markers according to the scheme of Holmquist et al 2006 (in preparation)

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