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Python scripts and Jupyter notebooks for paper: Simon and Huttley 2021 A New Likelihood-based Test for Natural Selection bioRxiv doi = 10.1101/2021.07.04.451068.

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NeutralityTest

This repository contains Python code supporting the analyses in the paper Simon and Huttley 2021 A New Likelihood-based Test for Natural Selection bioRxiv doi = 10.1101/2021.07.04.451068.

The central topic is the calculation of a statistic for selective neutrality, ρ, which is a relative likelihood of two evolutionary models.

Code in this repository uses the library selectiontest, which is intended to make the core functions available to end users for analysis of their own data. Documentation of selectiontest can be found at https://readthedocs.org/projects/selectiontest/.

The following is a brief summary of scripts and notebooks in this repository.

roc_simulation.py creates synthetic data for a range of scenarios using the MSMS program (Ewing, G. and Hermisson, J. (2010). MSMS: a coalescent simulation program including recombination, demographic structure and selection at a single locus. Bioinformatics, 26(16):2064–2065). It calculates ρ and Tajima's D for these data sets. This data can be used to generate receiver operating characteristic (ROC) curves to compare the performance of these two statistics. MSMS is Java code, which we call from within a Python script.

Plot_roc_curves.ipynb plots output from roc_simulation.py.

generate_calibration_table.ipynb generates a table of calibration values used in the paper.

analyse_region_by_population.py supports the analysis of the 2q11.1 region of the human chromosome contained in the paper 'A New Statistical Test Provides Evidence of Selection Against Deleterious Mutations in Genes Promoting Disease Resistance'. The analysis uses data in VCF format from the 1000 Genomes Project. See Auton, A. et al. (2015). A global reference for human genetic variation. Nature, 526:68–74 ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/2013050

vcf_1KG.p contains functions supporting the analysis of the 1000 Genomes Project data.

plot_heatmap.ipynb is used to plot output from analyse_region_by_population.py.

Chromosome 2q11.1-analyse 20k segment.ipynb supports further analysis of the 2q11.1 region of the human chromosome.

ACKR1 Gene (FY, DARC, Duffy).ipynb supports the analysis of the ACKR1 gene contained in the paper 'A New Statistical Test Provides Evidence of Selection Against Deleterious Mutations in Genes Promoting Disease Resistance'. This uses the same 1000 Genomes Project data set as above.

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Python scripts and Jupyter notebooks for paper: Simon and Huttley 2021 A New Likelihood-based Test for Natural Selection bioRxiv doi = 10.1101/2021.07.04.451068.

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