Sampling and manipulating genome-wide ancestral recombination graphs (ARGs).
The ARGweaver software package contains programs and libraries for sampling and manipulating ancestral recombination graphs (ARGs). An ARG is a rich data structure for representing the ancestry of DNA sequences undergoing coalescence and recombination.
See the manual for documentation on the programs and file formats associated with ARGweaver.
The following dependencies must be installed to compile and run ARGweaver:
pip install argweaver
Alternatively, ARGweaver can be install using the
python setup.py install
Lastly, ARGweaver can be installed using the
Once compiled, install the ARGweaver programs (default install in
By default this will install all files into
/usr, which may require
super user permissions. To specify your own installation path use:
make install prefix=$HOME/local
If you use this option, make sure
$HOME/local/bin is in your
$HOME/local/lib/python2.X/site-packages is in your
ARGweaver can also run directly from the source directory. Simply add the
bin/ directory to your
PATH environment variable or create symlinks to the
bin/ to any directory on your
PATH. Also add the
argweaver source directory to your
examples/ for details.
Here is a brief example of an ARG simulation and analysis. To generate simulated data containing a set of DNA sequences and an ARG describing their ancestry the following command can be used:
arg-sim \ -k 8 -L 100000 \ -N 10000 -r 1.6e-8 -m 1.8e-8 \ -o test1/test1
This will create an ARG with 8 sequences each 100kb in length evolving in a population of effective size 10,000 (diploid), with recombination rate 1.6e-8 recombinations/site/generation and mutation rate 1.8e-8 mutations/generation/site. The output will be stored in the following files:
test1/test1.arg -- an ARG stored in *.arg format test1/test1.sites -- sequences stored in *.sites format
To infer an ARG from the simulated sequences, the following command can be used:
arg-sample \ -s test1/test1.sites \ -N 10000 -r 1.6e-8 -m 1.8e-8 \ --ntimes 20 --maxtime 200e3 -c 10 -n 100 \ -o test1/test1.sample/out
This will use the sequences in
test1/test1.sites and it assumes the
same population parameters as the simulation (i.e.
-N 10000 -r 1.6e-8
-m 1.8e-8). Also several sampling specific options are given
(i.e. 20 discretized time steps, a maximum time of 200,000 generations,
a compression of 10bp for the sequences, and 100 sampling iterations.
After sampling the following files will be generated:
test1/test1.sample/out.log test1/test1.sample/out.stats test1/test1.sample/out.0.smc.gz test1/test1.sample/out.10.smc.gz test1/test1.sample/out.20.smc.gz ... test1/test1.sample/out.100.smc.gz
out.log contains a log of the sampling procedure,
out.stats contains various ARG statistics (e.g. number of
recombinations, ARG posterior probability, etc), and
out.100.smc.gz contain 11 samples of an ARG in *.smc file
To estimate the time to most recent common ancestor (TMRCA) across these samples, the following command can be used:
arg-extract-tmrca test1/test1.sample/out.%d.smc.gz \ > test1/test1.tmrca.txt
This will create a tab-delimited text file containing six columns: chromosome, start, end, posterior mean TMRCA (generations), lower 2.5 percentile TMRCA, and upper 97.5 percentile TMRCA. The first four columns define a track of TMRCA across the genomic region in BED file format.
Many other statistics can be extracted from sampled ARGs. For more details
The following Python libraries are needed for developing ARGweaver:
nose pyflakes pep8
These dependencies can be installed using
pip install -r requirements-dev.txt
The python tests can be run either with nose or make:
# Run tests with nose nosetests test # Run tests with make make test
There are also C++ tests written using googletest, Google's unit-testing framework. Googletest can either be installed system-wide or within the ARGweaver source tree. For convenience, googletest can be installed in the source tree using
Once installed, c++ unit tests can be run using