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PyPop: Python for Population Genomics

Authors: Lancaster, Alexander K.; Nelson, Mark P.; Single, Richard; Solberg, Owen; Tsai, Yingsu; Meyer, Diogo; Webster, Gordon; +6 Authors

PyPop: Python for Population Genomics

Abstract

PyPop for Population Genomics (PyPop) is a Python program for processing genotype and allele data and running population genetic analyses, including conformity to Hardy-Weinberg expectations; tests for balancing or directional selection; estimates of haplotype frequencies and measures and tests of significance for linkage disequilibrium (LD). Output of analyses are stored in XML format for maximum downstream flexibility. PyPop also has an internal facility for additionally aggregating the output XML and generating output tab-separated values (TSV) files, as well as default plain text files for each population. Although it can be run on any kind of genotype data, it has additional support for analyzing population genotype with allelic nomenclature from the human leukocyte antigen (HLA) region.

If you use this software, please cite both the software itself, as well as the article Lancaster AK et al. (2024) PyPop: A mature open-source software pipeline for population genomics. Front. Immunol. 15:1378512. doi: 10.3389/fimmu.2024.1378512

Main website: http://pypop.org/. Development is on GitHub: https://github.com/alexlancaster/pypop Binary packages are available on PyPI: https://pypi.org/project/pypop-genomics/. 

Keywords

HLA, computational biology, Human leukocyte antigen, population genomics, Major histocompatibility complex, evolutionary biology, population genetics, bioinformatics, MHC

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average