SimPrily runs genome simulations with user defined parameters or priors and computes genomic stats.
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Created by Ariella Gladstein, based on code from Consuelo Quinto Cortes and Krishna Veeramah.
Also worked on by David Christy, Logan Gantner, and Mack Skodiak.
[email protected]
SimPrily runs genome simulations with user defined parameters or parameters randomly generated by priors and computes genomic statistics on the simulation output.
Version 1
This is ideal for use with Approximate Bayesian Computation on whole genome or SNP array data.
Uses c++ programs macs and GERMLINE. For more information on these programs, see:
https://github.com/gchen98/macs
https://github.com/sgusev/GERMLINE
cd to the directory you want to work in,
git clone https://github.com/agladstein/SimPrily.git
If using Vagrant (this is recommended if running on non-Linux OS):
Start Vagrant, ssh into Vagrant, cd to SimPrily directory:
vagrant up
vagrant ssh
cd /vagrant
Install the virtual environment and install the requirements.
./setup/setup_env_vbox_2.7.sh
If not using Vagrant, just install the virtual environment and install the requirements:
./setup/setup_env_2.7.sh
e.g. One Test simulation:
python simprily.py -p examples/eg1/param_file_eg1_asc.txt -m examples/eg1/model_file_eg1_asc.csv -g genetic_map_b37/genetic_map_GRCh37_chr1.txt.macshs -a array_template/ill_650_test.bed -i 1 -o output_dir -v
For quick help:
python simprily.py --help
simprily.py takes 4 required arguments and 2 optional arguments, and help, verbose, and profile options.
Run as
python simprily.py [-h] -p PARAM -m MODEL -i ID -o OUT [-g MAP] [-a ARRAY] [-v] [--profile]
-p PARAM or --param PARAM = The location of the parameter file
-m MODEL or --model MODEL = The location of the model file
-i ID or --id ID = The unique identifier of the job
-o OUT or --out OUT = The location of the output directory
-h or --help = shows a help message and exists
-v = increase output verbosity. This includes 3 levels, -v, -vv, and -vvv
--profile = Print a log file containing the time in seconds and memory use in Mb for main functions
-g MAP or --map MAP = The location of the genetic map file
-a ARRAY or --array ARRAY = The location of the array template file, in bed form
Three subdirectories are created in the directory specified in the output_dir argument.
output_dir/results
output_dir/sim_data
output_dir/germline_out
Intermediate files go to output_dir/sim_data and output_dir/germline_out.
output_dir/sim_data contains PLINK formated .ped and .map files created from the pseudo array, which are necessary to run GERMLINE.
output_dir/germline_out contains the GERMLINE .match output and .log. The .match contains all of the identified IBD segments.
These files are NOT automatically removed in python script, but are unnecessary once the job is complete.
Output files go to output_dir/results.
output_dir/results contains the parameter values used in the simulation and the summary statistics calculated from the simulation.
The first line is a header with the parameter names and summary statistics names.
The second line is the parameter values and summary statistics values.
Must have an Open Science Grid Connect account.
Create an account at https://osgconnect.net/signup
Log onto Open Science Grid Connect
Working directory must be pegasus_workflow.
Submit a Pegasus workflow (must be in pegasus_workflow)
./submit -p PARAM -m MODEL -j NUM [-g MAP] [-a ARRAY]
e.g.
./submit -p ../examples/eg2/param_file_eg2_asc.txt -m ../examples/eg2/model_file_eg2_asc.csv -j 10 -a ../array_template/ill_650_test.bed -g ../genetic_map_b37/genetic_map_GRCh37_chr1.txt.macshs
The results will appear in
/local-scratch/user-name/workflows/simprily_id
where user-name is specific to the user, and id is the workflow id.
Content type
Image
Digest
Size
457.8 MB
Last updated
over 6 years ago
docker pull agladstein/simprily