There are 10 repositories under population-genomics topic.
rhierbaps: R implementation of hierBAPS
Estimation of population genetic parameters using deep learning
strataG is a toolkit for haploid sequence and multilocus genetic data summaries, and analyses of population structure.
PyPop: Python for Population Genomics
群体遗传学分析中用到的script和skill等
Python package for detecting positive selective sweeps using time-series genomics sampling data.
Snakemake workflow for Illumina RNA-sequencing experiments - extract population genomic signals from RNA-Seq data
Population Assignment using Genetic, Non-genetic or Integrated Data in a Machine-learning Framework. Methods in Ecology and Evolution. 2018;9:439–446.
Automated and distributed population genetic model inference from allele frequency spectra
For QMUL's Genome Bioinformatics MSc module BIO721P & SIB's Spring school in bioinfo & population genomics
Code used to analyse WGS data of giraffe in Coimbra et al. 2021
An python omics data toolkit for the analysis across biological scales
Scripts in R for Landscape Genomics Analyses v.2
Method to estimate the age and intensity of recent bottlenecks/founder events, using genotype data and a recombination map.
R is nowadays probably the most powerful tool for calculations of all kinds. There are plenty of modules available for work with molecular data. Those will be introduced during the course.
Bioinformatics pipeline to process whole genome resequencing data and perform genotype likelihood based population genomic analyses using ANGSD and related softwares. Flexible to datasets that combine high/low coverage and historical/fresh samples.
Bayesian bi-clustering of categorical data
Small scripts for population genetics analysis
PRAWNS: A fast and scalable bioinformatics tool that generates an efficient pan-genome representation of closely related whole genomes to provide a concise list of genomic features
Presented here are the tools and strategies implemented for short-read SV discovery (Chapter 3), a population genomics study (Chapter 4) and genome graph construction (Chapter 5).