Analysis 2

Analysis of inference methods on standard population models including selection. Here's how to get going, if you'd like to run this analysis

Get the Analysis repo

Now clone the analysis2 repo, and install its dependencies

git clone git@github.com:popsim-consortium/analysis2.git
cd analysis2/

Set up your python environment to run the analysis

We recommend you start by creating a new conda environment for the analysis. This can be done using the command below, which will create a new conda env called analysis2. Currently the workflow is targeted to run on python 3.8. We believe there are issues when running python 3.9 or higher.

conda env create -f environment.yml
conda activate analysis2

For using msmc we need to download and compile it to play nice with the conda environment that we have set up.

cd ext
git clone https://github.com/stschiff/msmc.git
cat msmc_makefile_stdpopsim_patch > msmc/Makefile && cd msmc && make
cd ../../

For using DFE-alpha, we need to download the program and extra data (5GB) from http://www.homepages.ed.ac.uk/pkeightl/dfe_alpha/download-dfe-alpha.html

cd ext
wget -c http://homepages.ed.ac.uk/eang33/dfe_alpha/dfe-alpha-release-2.16.tar.gz
tar -xvf dfe-alpha-release-2.16.tar.gz
cat dfe_alpha_makefile_stdpopsim_patch > dfe-alpha-release-2.16/Makefile && cd dfe-alpha-release-2.16 && make
wget -c https://datashare.ed.ac.uk/bitstream/handle/10283/2730/data.tar.gz?sequence=1&isAllowed=y
tar -xvf data.tar.gz\?sequence\=1
cd ../../

For using polyDFE and grapes, we can download them from their GitHub repositories.

cd ext
git clone https://github.com/paula-tataru/polyDFE.git
cd polyDFE
chmod a+x polyDFE-2.0-linux-64-bit
wget -c https://github.com/BioPP/grapes/releases/download/v1.1.0/grapes-x86_64-bin-static-1.1.0-1.tar.gz
tar -xvf grapes-x86_64-bin-static-1.1.0-1.tar.gz
cd ..

Further instructions can be currently found in each task directory. A small example to simulate genomes with selection is described below.

Simulating selection with stdpopsim

For each HomSap chromosome one can simulate selection in specific regions (e.g. coding region) by using the function in stdpopsim and passed to stdpopsim.species.get_annotations (see the catalog for details). Below is a complete example that uses a given annotation to simulate selection on chr20.

import numpy as np
import stdpopsim

species = stdpopsim.get_species("HomSap")

# Adding a specific Distribution of fitness effects
dfe = species.get_dfe("Gamma_K17")

contig = species.get_contig("chr20")
model = species.get_demographic_model("OutOfAfrica_3G09")
samples = model.get_samples(100, 100, 100)  # YRI, CEU, CHB

# Extracting gene annottation
exons = species.get_annotations("ensembl_havana_104_exons")
exon_intervals = exons.get_chromosome_annotations("chr20").astype("int")
contig.add_dfe(intervals=exon_intervals, DFE=dfe)

# Engine used for simulations
engine = stdpopsim.get_engine("slim")

# Simulate
ts = engine.simulate(
    model,
    contig,
    samples,
    seed=236,
    slim_scaling_factor=100,
    slim_burn_in=10,
)

izabelcavassim/analysis2

Analysis 2

Get the Analysis repo

Set up your python environment to run the analysis

Simulating selection with stdpopsim