Skip to content

Latest commit

 

History

History
86 lines (56 loc) · 6.11 KB

README.md

File metadata and controls

86 lines (56 loc) · 6.11 KB
Raw

Data challenge SSMPG 2022

Repository for Software and Statistical Methods for Population Genetics (SSMPG 2022) (Aussois, September 19-23 2022)

Attendees will have the opportunity to explore methods by studying simulated data, discussing best practices and methodological weaknesses of the studied techniques. Practical sessions will favor interactions among participants in a collaborative spirit.

1. Two problems to solve

The first challenge is the Woolly marmot challenge. The woolly marmot is an emblematic rodent species that lives in Thibaut's computer that recently disappeared from three directories due to poor coding decisions. Thibaut would like to repopulate the three sites but want to be sure that the reintroduced individuals would be optimally adapted to local environemental conditions. The goal of the practical session is to select source populations that minimize maladaptation at the three reintroduction sites using genetic offset measures. The data consist of a matrix of genotype for $n = 610$ individuals (61 populations) genotyped at $L = 1000$ loci (0,1,2 genotype format). Ten environmental variables have been measured for each source and reintroduction directory. The associated datasets can be found in the Woolly marmot folder.

The second challenge is to measure the loss of fitness of Osuah tree samples after a brutal change of climate. Osuah (pronounced Aussuah) in a small village in the French alps with virtual tree species that grow in computers only. The Osuah tree is an emblematic species of Clement's computer with SLIM DNA code that recently survived an abrupt environmental change. Clement recorded the survival probability for each population before and after change. The objective of the practical session is to come as close as possible to Clement's ground-thruth measure of fitness loss with genetic offset measures. The data consist of a matrix of genotypes for $n = 300$ sampled individuals genotyped at $L = 2333$ loci. Four environmental variables were measured before and after the abrupt change in conditions.

2. Create research groups and submit your paper

During data analysis sessions, participants are encouraged to create teams. Ideally a team should be composed of 4/5 participants. Teams will collectively report a synthesis of their analysis in public (last day), and send three files to the organizers for each data analysis. The three files should contain

  • a list of candidate loci detected by their preferred GEA method or by their preferred combination of methods for both challenges
  • a list of offset values obtained from their preferred offset method or by their preferred combination of methods for each Osuah tree sample site
  • a list of optimal source populations for the three Woolly marmot reintroduction sites
  • a short README ("Materials and Methods") file that explains all the choices made during the analysis.

Eeach team will be asked to present 2-3 slides for each data set.

3. Evaluation

Thibaut and Clement will reveal the truth about their simulation and what could be inferred from the genotypes and environmental data of whooly marmots and Osuah trees. Don't be worried. Everyone wins!

4. Install software

Install R and Rstudio

To participate in the practical sessions, bring your own laptop and install R and RStudio, an integrated development environment (IDE) for R.

Install R packages (LEA, gradientForest, vegan, qvalue)

To install R packages for the data analyses, copy and paste the following pieces of code in the R session

#Install R packages for SSMPG 2022

#Install packages from github
install.packages("devtools")

#Package LEA (development/latest version) 
devtools::install_github("bcm-uga/LEA")

#Package gradientForest
install.packages("gradientForest", repos="http://R-Forge.R-project.org")

#Package vegan for RDA
install.packages("vegan")

#Package qvalue for controlling FDRs
if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("qvalue")

Install BAYPASS

Download the archive for the latest stable version (2.3) from http://www1.montpellier.inra.fr/CBGP/software/baypass/ or directly via the following command run on a terminal:

wget http://www1.montpellier.inra.fr/CBGP/software/baypass/files/baypass_2.3.tar.gz

Extract the archive, e.g., from a terminal:

tar -zxvf baypass_2.3.tar.gz

The source files are to be found in the src subdirectory. BayPass is coded in Fortran90 and can therefore be compiled for any system supporting a Fortran90 compiler using the provided Makefile. This Makefile is designed to work with either the free compiler gfortran (if not already installed in your system, binaries are available at https://gcc.gnu.org/wiki/GFortranBinaries and are easy to install for most Windows, Mac and Linux OS versions) or the commercial ifort intel Fortran compiler that is now freely available (for non commercial use) as part of the Intel oneAPI Toolkit (see installation instruction for Windows, MacOS and Linux system). BayPass also uses OpenMP (http://openmp.org/wp/) to implement multithreading, which allows parallel calculation on computer systems that have multiple CPUs or CPUs with multiple cores. Users thus have to make sure that the corresponding libraries are installed (which is usually the case, on Linux OS or following compiler installation previously described). The following instructions run within the src subdirectory allows to compile the code and to produce a binary:

  • using the gfortran free compiler (the command should automatically produce an executable called g_baypass):
make clean all FC=gfortran
  • using the ifort intel Fortran compiler (the command should automatically produce an executable called i_baypass):
make clean all FC=ifort

Note: Under Linux (or MacOS), before the first use, make sure to give appropriate execution rights to the program. For instance you may run: chmod +x baypass