Tools
- Structure version 2.3.4 (Pritchard, Stephens & Donnelly, 2000; Falush, Stephens & Pritchard, 2003; Hubisz et al., 2009; Pritchard, Falush, Hubisz, 2012)
- Structure Harvester version 0.6.94 (Earl & vonHoldt, 2012; Earl, 2014)
CLUMPP version 1.1.2 (Jakobsson & Rosenberg, 2007; Jakobsson & Rosenberg, 2009) - StrAuto version 1.0 (Chhatre & Emerson, 2017; Chhatre & Emerson, 2018)
- Distruct version 2.2 (Raj et al., 2014; Chhatre, 2016)
- Obtained and unpacked Structure
wget https://web.stanford.edu/group/pritchardlab/structure_software/release_versions/v2.3.4/release/structure_linux_console.tar.gz
tar xvfz structure_linux_console.tar.gz
- Obtained Structure Harvester
git clone https://github.com/dentearl/structureHarvester.git
- Obtained and unpacked StrAuto
wget http://www.crypticlineage.net/download/strauto/strauto_1.tar.gz
tar xvfz strauto_1.tar.gz
- Obtained and unpacked CLUMPP
wget https://rosenberglab.stanford.edu/software/CLUMPP_Linux64.1.1.2.tar.gz
tar xvfz CLUMPP_Linux64.1.1.2.tar.gz
- Obtained and unpacked Distruct
wget http://www.crypticlineage.net/download/distruct/distruct22.tar.gz
tar xvfz distruct22.tar.gz
We made a few slight modifications to the distruct2.2.py script for our plots. We have provided our modified version of the script in this repository as "distruct2.2_ocwa.py".
We first ran analyzed our dataset with eight designated populations.
We ran StrAuto using the LOCPRIOR with eight designated populations (see input files in the 8pop_StrAuto directory) for K=1-20.
python strauto.1.py
We changed the extraparams file created by the above command to enable the LOCPRIOR ("#define LOCPRIOR 1").
./runstructure
The Structure Harvester output suggested K=2 as optimal, so we ran CLUMPP on the K=2 output (see input files in the 8pop_clumpp directory).
CLUMPP paramfile_K2individ
$ python distruct2.2_ocwa.py --input=StrAuto8pop -K 2 --output=StrAuto8pop_distruct2.2.png --popfile=OCWA_8pop_justPopFlags_names --poporder=OCWA_8pop_order_names
Based upon the 8pop analyses, we removed the Channel Islands population and separately analyzed the remaining seven populations in our dataset.
We ran StrAuto using the LOCPRIOR with seven designated populations (see input files in the 7pop_StrAuto directory) for K=1-20.
python strauto.1.py
We changed the extraparams file created by the above command to enable the LOCPRIOR ("#define LOCPRIOR 1").
./runstructure
The Structure Harvester output suggested K=2 as potentially optimal, so we ran CLUMPP on the K=2 output (see input files in the 7pop_clumpp directory).
CLUMPP paramfile_K2individ
$ python distruct2.2_ocwa.py --input=StrAuto7pop -K 2 --output=StrAuto7pop_distruct2.2.png --popfile=OCWA_7pop_justPopFlags_names --poporder=OCWA_7pop_order_names
Based upon the 8pop analyses, we separately analyzed the Channel Islands population for substructure. We designated the individuals from the northern and southern Channel Islands as separate populations.
We ran StrAuto using the LOCPRIOR with two designated populations (see input files in the 2pop_StrAuto directory) for K=1-10.
python strauto.1.py
We changed the extraparams file created by the above command to enable the LOCPRIOR ("#define LOCPRIOR 1").
./runstructure
The Structure Harvester output suggested K=4 as potentially optimal, so we ran CLUMPP on the K=4 output (see input files in the 7pop_clumpp directory).
CLUMPP paramfile_K4individ
$ python distruct2.2_ocwa.py --input=StrAuto2pop -K 2 --output=StrAuto2pop_distruct2.2.png --popfile=OCWA_2pop_justPopFlags_names --poporder=OCWA_2pop_order_names
We ran IMa2p version 58a02604e58b6a2bc3c1ccbb75767dafbb6fa781 (Sethuraman & Hey 2015; Sethuraman, 2017) three separate times with these settings.
mpirun -np 10 IMa2p -hn 15 -q1183.5505060899999 -m6.7593228669463 -t23.6710101218 -
b1000000 -r2 -r5 -l20000 -p6 -u1 -hfg -ha0.999 -hb0.3 -i IMa2_input -o run_out
Please cite this repository as follows (you should also add which version you used):
Chhatre VE. 2016. Distruct. Version 2.2. [Accessed 2018 Jun 21]. Available from: http://www.crypticlineage.net/pages/distruct.html
Chhatre VE., Emerson KJ. 2017. StrAuto: automation and parallelization of STRUCTURE analysis. BMC Bioinformatics 18:192. DOI: 10.1186/s12859-017-1593-0.
Chhatre VE., Emerson KJ. 2018. StrAuto. Version 1.0. [Accessed 2018 Jun 21]. Available from: http://www.crypticlineage.net/pages/software.html
Earl DA., vonHoldt BM. 2012. STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conservation Genetics Resources 4:359–361. DOI: 10.1007/s12686-011-9548-7.
Earl D. 2014. Structure Harvester. 2014. Structure Harvester. Version 0.6.94. [Accessed 2018 Jun 21]. Available from: https://github.com/dentearl/structureHarvester
Falush D., Stephens M., Pritchard JK. 2003. Inference of Population Structure Using Multilocus Genotype Data: Linked Loci and Correlated Allele Frequencies. Genetics 164:1567–1587.
Hubisz MJ., Falush D., Stephens M., Pritchard JK. 2009. Inferring weak population structure with the assistance of sample group information. Molecular Ecology Resources 9:1322–1332. DOI: 10.1111/j.1755-0998.2009.02591.x.
Jakobsson M., Rosenberg NA. 2007. CLUMPP: a cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics 23:1801–1806. DOI: 10.1093/bioinformatics/btm233.
Jakobsson M., Rosenberg NA. 2009. CLUMPP: CLUster Matching and Permutation Program. Version 1.1.2. [Accessed 2018 Jun 21]. Available from: https://rosenberglab.stanford.edu/clumpp.html
Pritchard JK., Stephens M., Donnelly P. 2000. Inference of Population Structure Using Multilocus Genotype Data. Genetics 155:945–959.
Pritchard JK., Falush D., Hubisz MJ. 2012. Structure. Version 2.3.4. [Accessed 2018 Jun 21]. Available from: https://web.stanford.edu/group/pritchardlab/structure.html
Raj A., Stephens M., Pritchard JK. 2014. fastSTRUCTURE: Variational Inference of Population Structure in Large SNP Data Sets. Genetics 197:573–589. DOI: 10.1534/genetics.114.164350.
Sethuraman A, Hey J. 2015. IMa2p – parallel MCMC and inference of ancient demography under the Isolation with migration (IM) model. Molecular Ecology Resources 16:206–215. DOI: 10.1111/1755-0998.12437.
Sethuraman A. 2017. IMa2p. Version 58a02604e58b6a2b. [Accessed 2018 Jun 21]. Available from: https://github.com/arunsethuraman/ima2p