X-chromosomal and autosomal data from the Human Genome Diversity Panel, analyzed in S Ramachandran, NA Rosenberg, MW Feldman, and J Wakeley (2008), "Population differentiation and migration: coalescence times in a two-sex island model for autosomal and X-linked loci". Theor Pop Biol. Vol. 74:291-301
Outbreak data, analyzed in KF Smith et al. (2014), "Global rise in human infectious disease outbreaks". J Roy Soc Interface Vol. 11: 20140950
A new approach for inferring population size changes over time, developed and implemented in JA Palacios, J Wakeley, and S Ramachandran (2015) "Bayesian nonparametric inference of population size changes from sequential genealogies". Genetics Vol. 201: 281-304
Phoneme data for 2082 languages, analyzed in N Creanza et al. (2015) "A comparison of worldwide phonemic and genetic variation in human populations" Proc Natl Acad Sci USA Vol. 112: 1265-1272
pong is a freely available software package, released by Behr et al. (2016, Bioinformatics), for post-processing output from clustering inference using population genetic data. It combines a a network-graphical approach for analyzing and visualizing membership in latent clusters with an interactive D3.js-based visualization. pong outpaces current solutions by more than an order of magnitude in runtime while providing a user-friendly, interactive visualization of population structure that is more accurate than those produced by current tools. Thus, pong enables unprecedented levels of scale and accuracy in the analysis of population structure from multilocus genotype data.
pong requires Python 2.7 and a modern web browser (e.g. Chrome, Firefox, Safari). pong is not compatible with Internet Explorer. pong is hosted on PyPI and can thus be easily installed with
pip by running:
pip install pong
PEGASUS is a freely available software package, released by Nakka et al. (2016, Genetics), for combining SNP-level p-values into gene scores and conducting gene-level association tests with a phenotype of interest. PEGASUS computes gene scores of association analytically and produces gene scores with as much as 10 orders of magnitude higher numerical precision than competing methods.
SWIF(r) is freely available software, released by Sugden et al. (2018, Nature Communications), for calculating SNP-based probabilities of adaptation based on training simulations from a demographic model. Code for training and running SWIF(r), as well as for calibrating the probabilistic output and visualizing learned distributions can be found at the SWIF(r) git repository.
SWIF(r) requires Python v2.7, Matplotlib v1.7, SciPy v0.16, and Scikit-learn v0.17.