Documentation for the 2019 Workshop on Computational Population Genetics

Attention

Please note that the content of this Workshop is quite outdated. Please check My Homepage for newer material.

This is the documentation for the Workshop held during January 22-24, 2019 at the Max-Planck-Institute for the Science of Human History (MPI-SHH) in Jena, organised by Stephan Schiffels (MPI-SHH), Choongwon Jeong (MPI-SHH) and Benjamin Peter (MPI-EVA). Important contributions came from Kay Prüfer and Cosimo Posth!

Contents:

• Setting up
  • The Computational Infrastructure for this Workshop
  • Basic Usage
  • Notebooks
  • Working with Bash
  • Back to Notebooks
  • Some Plotting with Python
• Bam Files
  • samtools: Converting, Filtering SAM and BAM Files
  • Bash programming
  • Basic programming in AWK
  • Calculating coverage with samtools
  • EXTRA: Genotype calling from bam files
• Principal Components Analysis (PCA)
  • Genotype Data
  • How PCA works
  • Preparing the parameter file
  • Population lists vs. Projection
  • Running smartPCA
  • Plotting modern populations
  • Adding ancient individuals
• F Statistics and Treemix
• Admixture modelling with qpWave and qpAdm
  • Overview: summarizing a matrix of F4 statistics
  • Data
  • Preparing “left” and “right” populations
  • Preparing the parameter file
  • Running the programs
  • Reading the qpWave log file
  • Reading the qpAdm log file
• Estimating Admixture times with Alder
  • Overview
  • Data
  • Preparing the parameter file
  • Running the programs
  • Reading the main output file (weighted LD table)
  • Reading the log file
  • Plotting weighted LD decay curve in R
• Estimating Admixture Graphs with qpGraph
  • Overview method
  • Data
  • Preparing the parameter file
  • Preparing the graph topology
  • Running the program
  • Reading the output files
  • Adding new groups to the scaffold graph
  • Continuing to fit new groups
  • Test the robustness of the graph topology
  • Adding admixture edges
• MSMC
  • Data
  • Generating consensus sequences for each sample
  • Combining samples
  • Running MSMC2 for estimating the effective population size
  • Estimating population separation history
  • Plotting in Python