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Revisiting Adaptive Introgression at the HLA Genes in Lithuanian Genomes with Machine Learning

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Lithuanian-archaic-introgression

Introduction

This repository contains a Snakemake workflow designed to reproduce the results of a scan for candidates of adaptive introgression and balancing selection on chromosome 6 in Lithuanian genomes. The workflow has been tested on Oracle Linux 9 using the Life Science Compute Cluster at the University of Vienna.

Usage

  1. Install Mambaforge (version: 23.3.1).

  2. Clone this repository:

git clone https://github.com/xin-huang/Lithuanian-archaic-introgression
cd Lithuanian-archaic-introgression
  1. Create the environment:
mamba env create -f workflow/envs/env.yaml
  1. Activate the environment:
mamba activate lai
  1. Run the analysis locally:
snakemake -c 1 --use-conda
  1. Run the analysis on HPC:
snakemake -c 1 --use-conda --profile config/slurm

Users should adjust the resource parameters in each Snakemake file to match their cluster settings and modify the config.yaml file in config/slurm to suit their job scheduler.

Results

Distribution of adaptive introgression probabilities on chromosome 6 in Lithuanian genomes using MaLAdapt (Zhang et al. 2023).

MaLAdapt

Manhattan plot of B1 scores on chromosome 6 in Lithuanian genomes using BetaScan (Siewert and Voight 2017).

BetaScan

References

  • Siewert and Voight. 2017. Mol Biol Evol 34: 2996–3005.

  • Zhang et al. 2023. Mol Biol Evol 40: msad001.

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Revisiting Adaptive Introgression at the HLA Genes in Lithuanian Genomes with Machine Learning

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