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 online or in docs/.

Installation

This repository contains two components, and both must be installed to properly use smcsmc.

Recommended Installation via conda

NOTE: We currently only support conda installation on 64 bit Linux. If you are using a different operating system you must install manually -- see below.

We have automated this process in a conda package, and we highly recommend installing it this way.

conda install -c conda-forge -c luntergroup smcsmc

We must add conda-forge as a channel (with the -c flag) because the Boost version there is more current than default channels.

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 [email protected]: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
cd ..

Install the frontend

pip install -r requirements.txt
pip install .

Docker

You can use smcsmc through Docker, either by creating your own image or by using a pre-made one and mounting in your data. The docker image comes with both the python and C++ code pre-installed and ready to use.

docker run chris1221/smcsmc:latest

Citation

If you use smcsmc in your work, please cite the following articles:

1. Henderson D, Zhu S, Cole CB, Lunter G (2021) Demographic inference from multiple whole genomes using a particle filter for continuous Markov jump processes. PLOS ONE 16(3): e0247647. https://doi.org/10.1371/journal.pone.0247647

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