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Demographic inference from whole genomes
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README.md

A Particle Filter for Demographic Inference

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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.

Recommended Installation via conda

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:

  1. 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

  2. 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

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