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Code for "A unified genealogy of modern and ancient genomes"

This repo contains code used in "A unified genealogy of modern and ancient genomes". This includes:

  • pipelines to generate a unified, dated tree sequence from multiple datasets
  • simulation based validation of tsinfer and tsdate
  • empirical validation of inferred tree sequences using ancient genomes
  • code to produce all non-schematic figures in the paper, as well as the interactive figure and supplementary video

These analyses are placed in subdirectories as follows:

  • all-data contains all code for downloading and preparing real data, as well as the pipeline for creating tree sequences
  • src contains scripts to run analyses and validation on both real and simulated data, as well as code for plotting figures
  • data contains reference data needed for some analyses. It is also where the results of analyses performed on tree sequences inferred from real data are stored. Finally, it is also where simulations evaluating mismatch parameters in tsinfer are performed
  • simulated-data is where the results of all simulation-based analyses are saved
  • tools contains methods used to compare the accuracy of tsinfer and tsdate as well as tools to process variant data files

Getting Started

You must first clone this repo, including submodules as follows:

git clone --recurse-submodules https://github.com/awohns/unified_genealogy_paper.git
cd unified_genealogy_paper

Required Software

Installing required python modules

Please use Python >= 3.8 and <= 3.10 (`numba' currently fails to install under Python 3.11).

First, cartopy (required for generating figures with maps) and ffmpeg (required to create the movie) must be installed with conda as follows:

conda install -c conda-forge cartopy
conda install -c conda-forge ffmpeg

All other required Python packages are listed in the requirements.txt file. These can be installed with

python -m pip install -r requirements.txt

Required software for preparing real data

We require BCFtools, SAMtools, and HTSlib, as well as convertf, Picard, eigensoft and plink to prepare the variant data files from the 1000 Genomes Project, Human Genome Diversity Project, Simons Genome Diversity Project, and ancient DNA datasets for use with tsinfer and tsdate. We compare our methods with Genealogical Estimation of Variant Age (GEVA) and Relate. All of these tools are kept in the tools directory and can be downloaded and built using

cd tools
make all

Inferring Tree Sequences from Real Data

Please see the README in the all-data directory for details on inferring tree sequences from real data.

Running Simulations

To generate data required for simulation-based figures and to plot the figures themselves, follow this general process: generate data, run analyses, and plot the results. For example, the following will generate Figure 1c:

python src/run_evaluation.py tsdate_neutral_sims --setup
python src/run_evaluation.py tsdate_neutral_sims --inference
python src/plot.py tsdate_neutral_sims

The first command runs the simulations for each evaluation. The simulations are stored in the simulated-data directory The second command performs inference on the results of the simulations. This will take multiple days to run for all simulations in the paper. The results are stored as csv files in the simulated-data directory. The third command plots the csv files and saves the resulting figures to the figures directory.

Running all evaluations

To produce all the simulation data in our paper, run the following, in order:

python src/run_evaluation.py --setup all 
python src/run_evaluation.py --infer all # will take multiple days

You can speed up the inference step by using multiple processors, specified using the -p flag. For instance, on a 64 core machine, using all cores:

python src/run_evaluation.py --infer all -p 64

The mismatch simulations are produced separately, using the Makefile in the data directory:

cd data
make mismatch

Analyzing inferred tree sequences

Once you have inferred tree sequences and the results are in the all-data directory, run functions in src/analyze_data.py to generate data for non-simulation based figures. The figures themselves are plotted using src/plot.py

Before running these analyses, for certain plots you will need to download other required files (allele age estimates from Relate and GEVA for fig S18="tgp_muts_frequency"; the 1000 genomes chr20 mask file for for fig S6="ld_quality_by_mutations"). You can do this using the following:

cd data
make relate_ages
make geva_ages.csv.gz
make chr20_mask

You can then run all analyses using real data with:

python src/analyze_data.py all

Plotting figures

When the above steps have been completed, figures (including the movie) can be plotted using:

python src/plot.py all

The interactive figure is created separately using the following command:

python src/interactive_plot.py

Reproducing plots from downloaded data

Note that if you simply want to produce figures similar to those in our published paper, you can download the tree sequences from Zenodo (https://zenodo.org/record/5512994), and perform plotting steps separately, e.g. within a terminal

cd all-data
# can do this for different chromosome arms: here we use chr20_q as an example
wget https://zenodo.org/record/5512994/files/hgdp_tgp_sgdp_high_cov_ancients_chr20_q.dated.trees.tsz
python -m tszip -d hgdp_tgp_sgdp_high_cov_ancients_chr20_q.dated.trees.tsz  # uncompress the download

# Make a world density map (see python src/plot.py for plots available)
# Make any csv files needed for a specific plot (see python src/analyze_data.py --help for options)
python src/analyze_data.py hgdp_sgdp_ancients_ancestral_geography --chrom 20_q
python src/plot.py world_density --chrom 20_q  # creates files in figures/

# Make a world locations map
python src/analyze_data.py average_pop_ancestors_geography --chrom 20_q
python src/plot.py population_ancestors --chrom 20_q  # creates files in figures/