A Particle Filter for Demographic Inference
SMCSMC (Sequential Monte Carlo for the Sequential Markovian Coalescent) or SMC2 is a program for inferring population history from multiple genome sequences. It includes both a python package smcsmc
and a command line interface smc2
along with two backend binaries smcsmc
/scrm
.
For examples and explaination, please see the documentation in docs/
or online.
Installation
This repository contains two components, and both must be installed to properly use smcsmc
.
conda
Recommended Installation via We have automated this process in a conda
package, and we highly recommend installing it this way.
NOTE: We currently only support
conda
installation on 64 bit Linux and if you are using a different operating system you must install manually.
conda config --add channels conda-forge
conda config --add channels terhorst
conda install -c luntergroup smcsmc
Installation from Source
Alternatively, a combination of cmake
and pip
can be used to install the python and core components.
Obtain the code
git clone git@github.com:luntergroup/smcsmc.git git-smcsmc
cd git-smcsmc
git submodule init
git submodule update
Install dependencies
Download and install the following packages (or use a package manager):
- boost
- cmake
- tcmalloc
Install the c++ backend
mkdir build; cd build
cmake ..
make
Install the frontend
pip install -r dependencies
pip install .
Citation
If you use smcsmc
in your work, please cite the following articles:
-
Henderson, D., Zhu, S. (Joe), & Lunter, G. (2018). Demographic inference using particle filters for continuous Markov jump processes. BioRxiv, 382218. https://doi.org/10.1101/382218
-
Staab, P. R., Zhu, S., Metzler, D., & Lunter, G. (2015). scrm: efficiently simulating long sequences using the approximated coalescent with recombination. Bioinformatics, 31(10), 1680–1682. https://doi.org/10.1093/bioinformatics/btu861