gpmap-tools 0.4.1

Tools for inference and visualization of complex genotype-phenotype maps

gpmap-tools: tools for inference and visualization of complex genotype-phenotype maps

gpmap-tools is a python library containing a suite of tools for inference and visualization of large and complex genotype-phenotype maps for genotypic spaces containing up to several million genotypes.

gpmap-tools is written for Python 3 and is provided under an MIT open source license. The documentation provided here is meant guide users through the basic principles underlying the method as well as explain how to use it for calculating the embedding coordinates and use the functionalities provided for advanced plotting of their own fitness landscapes.

Please do not hesitate to contact us with any questions or suggestions for improvements.

• For technical assistance or to report bugs, please contact Carlos Martí-Gómez ([e-mail])
• For more general correspondence, please contact David M. McCandlish ([e-mail])

Installation

Create a new environment and activate it

The library has only been tested with specific versions of the dependent libraries, so we recommend the same to be used to avoid potential incompatibilities.

conda create -n gpmap python=3.8
conda activate gpmap

Users: install from PyPI

pip install gpmap-tools

Developers: from from repository

Download the repository using git and cd into it

git clone https://github.com/cmarti/gpmap-tools.git
cd gpmap_tools
pip install .

Run tests using pytest

pytest test

References

• Martí-Gómez, C.; Zhou, J.; Chen W.; Kinney J. B.; Mccandlish, D. M. Inference and visualization of complex genotype-phenotype maps. Molecular Biology and Evolution (2026). doi
• Martí-Gómez, C and McCandlish, DM. Inference of fitness landscapes with heterogenous patterns of epistasis across sites (2026). In preparation.
• Zhou, J.; McCandlish D. M.; Minimum epistasis interpolation for sequence-function relationships. Nat. Comm. (2020) doi
• Zhou, J.; Wong, M. S.; Chen, W.; Krainer, A. R.; Kinney J. B.; Mccandlish, D. M. Higher-Order Epistasis and Phenotypic Prediction. PNAS (2022). doi.
• Chen WC, Zhou J, Sheltzer JM, Kinney JB, McCandlish DM. Field theoretic density estimation for biological sequence space with applications to 5' splice site diversity and aneuploidy in cancer. PNAS (2021). doi
• Mccandlish, D. M. Visualizing fitness landscapes. Evolution (2011). doi