moments: population genetic analyses and inference using diversity statistics

Please see the documentation for more details, examples, tutorials and API usage.

moments provides a suite of methods for demographic history and selection inference from genetic data, based on diffusion approximations to the one- and two-locus allele frequency spectrum. moments is modeled after the ∂a∂i open source package developed by Ryan Gutenkunst. For SFS-based methdos, we largely reuse ∂a∂i's API, but introduce a new simulation engine. This new method is based on the direct computation of the frequency spectrum without solving the diffusion system, removing the need for frequency grids as used in ∂a∂i. moments.LD, packaged within moments, implements methods for computing linkage disequilibrium statistics and running multi-population demographic inference using patterns of LD.

Getting started

moments now supports Python 3, and we no longer guarantee compatibility with Python 2.

The simplest way to install moments is using pip:

pip install moments-popgen

moments can then be imported using import moments. Important note: pip install moments installs a different package named moments, and our pypi package is named moments-popgen.

We can install the development branch directly from Github by running

pip install git+https://github.com/MomentsLD/moments.git@devel

Alternatively, you can clone the git repository to make an editable or development build.

git clone https://github.com/MomentsLD/moments.git

and then from within the moments directory (cd moments), run

pip install -r requirements.txt
pip install .

If you use conda, moments is available via bioconda:

conda config --add channels bioconda
conda install moments

Citing moments

If you use moments in your research, please cite:

  • Jouganous, J., Long, W., Ragsdale, A. P., & Gravel, S. (2017). Inferring the joint demographic history of multiple populations: beyond the diffusion approximation. Genetics, 206(3), 1549-1567.

If you use moments.LD in your research, please cite:

  • Ragsdale, A. P. & Gravel, S. (2019). Models of archaic admixture and recent history from two-locus statistics. PLoS Genetics, 15(6), e1008204.

  • Ragsdale, A. P. & Gravel, S. (2020). Unbiased estimation of linkage disequilibrium from unphased data. Mol Biol Evol, 37(3), 923-932.

If you use moments.TwoLocus in your research, please cite:

moments is developed in the Simon Gravel and Aaron Ragsdale research groups, at McGill University and UW-Madison, respectively. For any issues, questions or bug reports, please open an issue on Github.

Dependencies

If you install moments from source (e.g., after cloning the repository), you will need to install the dependencies. These are all listed in requirements.txt, and can be installed via pip after navigating to the moments directory:

pip install -r requirements.txt

A few more details: moments and moments.LD requires a handful of dependencies. At a minimum, these include

  • numpy

  • scipy

  • cython

  • mpmath

  • demes

We also strongly recommend installing ipython.

If you are using conda, all dependencies can be installed by navigating to the moments directory and then running

conda install --file requirements.txt

Once dependencies are installed, to install moments, run the following command in the moments directory:

python -m pip install -e .

You should then be able to import moments in your python scripts. Entering an ipython or python session, try to import moments. More details on installation can be found in the documentation. If, for any reason, you have trouble installing moments after following these steps, please submit an issue.

If you use Parsing from moments.LD, which reads VCF files and computes LD statistics to compare to predictions from moments.LD, you will need to additionally install

  • hdf5

  • scikit-allel

  • pandas

Changelog

All changes are detailed in the documentation.

Project links

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Credits

Author: Aaron Ragsdale

GitHub Statistics

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License

MIT

Requires

Python <3.15, >=3.11

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