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DOI

Download phased haplotype panel at this location

Download CHM13v2.0 recombination maps at this location

Computationally phased T2T 1KGP panel

This repository contains CHM13v2-aligned 1000 Genomes Project (1KGP) variant data (Rhie et al 2023) that have been computationally phased using SHAPEIT5 (Hofmeister et al 2023). Phased panels (both unrelated 2504 member panels and full 3202 member panels) are available at the T2T/HPRC aws bucket.

All intermediate and summary dataframes generated by scripts/create_summary_dataframes.py, along with all resources necessary to replicate publication figures has been uploaded to zenodo at this link. Please cite our preprint describing this work, which is available at Lalli et al. 2025.

Repository structure

├── bin/
│   └── [ GLIMPSE2 / impute5 / SHAPEIT5 static binaries ]
├── figures/ (Generated publication figures)
│   ├── figure6/ (Figure 6 karyotype plots and assembled SVGs)
│   └── supplemental/
├── notebooks/
│   ├── notebooks_whole_genome/
│   │   └── [ Analysis notebooks for whole-genome panel data ]
│   ├── calc_figures_for_paper.ipynb (Summary statistics)
│   ├── calc_per_variant_figures_for_paper.ipynb (Per-variant analysis)
│   └── make_plots.ipynb (Figure generation)
├── resources/
│   ├── GIABv3.6_bedfiles/
│   │   └── [ GIAB v3.6 T2T/GRCh38 stratifications ]
│   ├── pedigrees/
│   │   └── 1kGP Pedigrees
│   ├── recombination_maps/
│   │   ├── grch38/ (HAPMAP GRCh38 recombination maps)
│   │   └── t2t_native_scaled_maps/ (T2T native recombination maps)
│   ├── sample_subsets/
│   │   └── [ Sample IDs used to, for example, remove pangenome relatives ]
│   ├── SGDP_variation/
│   │   └── [ Reference GRCh38/CHM13 Simons Genome Diversity Project variation ]
│   └── [ Additional reference files ]
├── scripts/
│   └── [Utility and analysis scripts]
├── tables/
│   └── [Generated summary tables]
├── Dockerfile (Containerized environment)
└── README.md (This file)

Key Directory Functions

  • bin/: Static executables for SHAPEIT5 phasing and validation tools
  • resources/: Reference data including genomes, genetic maps, and validation datasets
    • pedigrees/: Family relationship files for trio-informed phasing
    • sample_subsets/: Population and family groupings for analysis
    • recombination_maps/: Genetic distance maps for both T2T and GRCh38 references
  • scripts/: Analysis pipeline and utility scripts for phasing and evaluation
  • notebooks/: Jupyter notebooks for figure generation and statistical analysis
  • figures/ & tables/: Generated publication outputs

How to create this phased haplotype panel

This section provides step-by-step instructions for how to recreate the phased CHM13v2.0 haplotype panel in your working environment.

Prerequisites

Before downloading data files, ensure you have the following tools installed:

# Required system tools
sudo apt-get update && sudo apt-get install -y \
    wget curl bcftools tabix parallel docker.io

# Python packages (if running analysis scripts)
pip install polars pandas numpy jupyter

Docker Environment (Recommended)

To avoid dependency conflicts and ensure reproducible results, users can run the entire pipeline within the pre-configured Docker container. This container includes all necessary tools and dependencies.

Run in Docker container:

# Pull the container
docker pull jlalli/phasing_t2t_dep_container:v2.0

# Start an interactive session with the project mounted
docker run -it -v /path/to/your/phasing_T2T_project:/workspace/phasing_T2T_project \
    -w /workspace/phasing_T2T_project \
    jlalli/phasing_t2t_dep_container:v2.0

# Once inside the container, you can run any pipeline commands
cd scripts
./create_and_assess_haplotype_panels.sh chr22_test 12 docker_test CHM13v2.0

Replace /path/to/your/phasing_T2T_project with the absolute path to your local copy of this repository.

1) Clone this repository

Begin by cloning this github repository to your local hard drive and going into the phasing_T2T directory:

# Create main project directory
git clone https://github.com/JosephLalli/phasing_T2T
cd phasing_T2T

2) Obtain primary data sources

If your goal is to run the Docker smoke test or the chr15/chr22 test regions, do not skip this section. The repository includes the scripts and region definitions for those test cases, but the biological inputs are still external. The fastest supported setup path is:

bash scripts/utility/download_resources.sh --test

That helper downloads the canonical test inputs into the repo, converts and indexes the FASTA files (including the required .gzi files), and populates the SGDP truth data needed by the smoke test.

Add --parallel if you want the independent download workloads to run together across sections:

bash scripts/utility/download_resources.sh --test --parallel

If you are interested in replicating our work completely, the necessary data that is too large to store on github can be obtained by following the instructions below.

Primary 1kGP Variant Calls

Unphased 1000 Genomes Project Variant Calls (T2T-CHM13v2.0) (~30-130GB per chromosome):

# Download T2T-CHM13 variant calls for all chromosomes
for chr in {1..22} X; do
  wget -P unphased_variant_calls/t2t https://s3-us-west-2.amazonaws.com/human-pangenomics/T2T/CHM13/assemblies/variants/1000_Genomes_Project/chm13v2.0/all_samples_3202/1KGP.CHM13v2.0.chr${chr}.recalibrated.snp_indel.pass.vcf.gz
  wget -P unphased_variant_calls/t2t https://s3-us-west-2.amazonaws.com/human-pangenomics/T2T/CHM13/assemblies/variants/1000_Genomes_Project/chm13v2.0/all_samples_3202/1KGP.CHM13v2.0.chr${chr}.recalibrated.snp_indel.pass.vcf.gz.tbi
done

Unphased 1000 Genomes Project Variant Calls (GRCh38) (~30-130GB per chromosome):

(Note that these files are only necessary for comparing how many variants are filtered by different pre-phasing filter cutoffs.)

# Download unphased, annotated GRCh38 1kGP variant calls.
for chr in {1..22} X; do
  wget -P unphased_variant_calls/grch38 https://42basepairs.com/download/s3/1000genomes/1000G_2504_high_coverage/working/20201028_3202_raw_GT_with_annot/20201028_CCDG_14151_B01_GRM_WGS_2020-08-05_chr${chr}.recalibrated_variants.vcf.gz
  wget -P unphased_variant_calls/grch38 https://42basepairs.com/download/s3/1000genomes/1000G_2504_high_coverage/working/20201028_3202_raw_GT_with_annot/20201028_CCDG_14151_B01_GRM_WGS_2020-08-05_chr${chr}.recalibrated_variants.vcf.gz.tbi
done

Phased GRCh38 1000 Genomes Variant Calls (~30-130GB per chromosome):

# Download Byrska-Bishop (2022) phased GRCh38 1kGP variant calls.
for chr in {1..22} X; do
  wget -P phased_panels/grch38 https://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/1000G_2504_high_coverage/working/20220422_3202_phased_SNV_INDEL_SV/1kGP_high_coverage_Illumina.chr${chr}.filtered.SNV_INDEL_SV_phased_panel.vcf.gz
  wget -P phased_panels/grch38 https://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/1000G_2504_high_coverage/working/20220422_3202_phased_SNV_INDEL_SV/1kGP_high_coverage_Illumina.chr${chr}.filtered.SNV_INDEL_SV_phased_panel.vcf.gz.tbi
done

Reference Genomes

T2T-CHM13 Reference Genome (914MB):

wget -P resources/ https://s3-us-west-2.amazonaws.com/human-pangenomics/T2T/CHM13/assemblies/GCA_009914755.4/chm13v2.0.fa.gz
wget -P resources/ https://s3-us-west-2.amazonaws.com/human-pangenomics/T2T/CHM13/assemblies/GCA_009914755.4/chm13v2.0.fa.gz.fai

GRCh38 Reference Genome (783MB):

curl https://42basepairs.com/download/s3/1000genomes/technical/reference/GRCh38_reference_genome/GRCh38_full_analysis_set_plus_decoy_hla.fa | bgzip > resources/GRCh38_full_analysis_set_plus_decoy_hla.fa.gz
wget -P resources/ https://42basepairs.com/download/s3/1000genomes/technical/reference/GRCh38_reference_genome/GRCh38_full_analysis_set_plus_decoy_hla.fa.fai

Reference Pangenome Variation:

wget -P resources/ https://s3-us-west-2.amazonaws.com/human-pangenomics/pangenomes/freeze/freeze1/minigraph-cactus/hprc-v1.1-mc-chm13/hprc-v1.1-mc-chm13.vcfbub.a100k.wave.vcf.gz
wget -P resources/ https://s3-us-west-2.amazonaws.com/human-pangenomics/pangenomes/freeze/freeze1/minigraph-cactus/hprc-v1.1-mc-chm13/hprc-v1.1-mc-chm13.vcfbub.a100k.wave.vcf.gz.tbi

wget  -P resources/ https://s3-us-west-2.amazonaws.com/human-pangenomics/pangenomes/freeze/freeze1/minigraph-cactus/hprc-v1.1-mc-grch38/hprc-v1.1-mc-grch38.vcfbub.a100k.wave.vcf.gz
wget  -P resources/  https://s3-us-west-2.amazonaws.com/human-pangenomics/pangenomes/freeze/freeze1/minigraph-cactus/hprc-v1.1-mc-grch38/hprc-v1.1-mc-grch38.vcfbub.a100k.wave.vcf.gz.tbi

wget -P resources/ https://s3-us-west-2.amazonaws.com/human-pangenomics/pangenomes/scratch/2024_02_23_minigraph_cactus_hgsvc3_hprc/hgsvc3-hprc-2024-02-23-mc-chm13-vcfbub.a100k.wave.norm.vcf.gz
wget -P resources/ https://s3-us-west-2.amazonaws.com/human-pangenomics/pangenomes/scratch/2024_02_23_minigraph_cactus_hgsvc3_hprc/hgsvc3-hprc-2024-02-23-mc-chm13-vcfbub.a100k.wave.norm.vcf.gz.tbi

wget -P resources/ https://s3-us-west-2.amazonaws.com/human-pangenomics/pangenomes/scratch/2024_02_23_minigraph_cactus_hgsvc3_hprc/hgsvc3-hprc-2024-02-23-mc-chm13.GRCh38-vcfbub.a100k.wave.norm.vcf.gz
wget -P resources/ https://s3-us-west-2.amazonaws.com/human-pangenomics/pangenomes/scratch/2024_02_23_minigraph_cactus_hgsvc3_hprc/hgsvc3-hprc-2024-02-23-mc-chm13.GRCh38-vcfbub.a100k.wave.norm.vcf.gz.tbi

wget -P resources/ https://s3-us-west-2.amazonaws.com/human-pangenomics/pangenomes/scratch/2024_02_26_minigraph_cactus_hgsvc3/hgsvc3-2024-02-23-mc-chm13-vcfbub.a100k.wave.norm.vcf.gz
wget -P resources/ https://s3-us-west-2.amazonaws.com/human-pangenomics/pangenomes/scratch/2024_02_26_minigraph_cactus_hgsvc3/hgsvc3-2024-02-23-mc-chm13-vcfbub.a100k.wave.norm.vcf.gz.tbi

wget -P resources/ https://s3-us-west-2.amazonaws.com/human-pangenomics/pangenomes/scratch/2024_02_26_minigraph_cactus_hgsvc3/hgsvc3-2024-02-23-mc-chm13.GRCh38-vcfbub.a100k.wave.norm.vcf.gz
wget -P resources/ https://s3-us-west-2.amazonaws.com/human-pangenomics/pangenomes/scratch/2024_02_26_minigraph_cactus_hgsvc3/hgsvc3-2024-02-23-mc-chm13.GRCh38-vcfbub.a100k.wave.norm.vcf.gz.tbi

Alternatively, run scripts/utility/download_resources.sh from the repository root to fetch the canonical runtime inputs, including reference genomes, pangenome VCFs, SGDP truth data, and the required FASTA indexes. Use --test to fetch only the chr15/chr22 smoke-test inputs, and add --parallel to launch the independent download sections together.

Make binaries executable

After downloading, ensure binaries are executable:

# Check executable permissions
chmod +x SHAPEIT5_* GLIMPSE2_* impute5_*
chmod +x scripts/*.sh

3) Run phasing scripts

As an example, to compare the performance of phasing the test region of chromosome 22, run the following commands

# Phase chr22_test (chr22_test and chr15_test are recognized, along with chr1-22, X, PAR1, PAR2)
cd scripts
CHM13_suffix=test_phasing_CHM13
GRCh38_suffix=test_phasing_GRCh38
num_threads=12

./create_and_assess_haplotype_panels.sh chr22_test $num_threads $CHM13_suffix CHM13v2.0

#...after phasing the region, assessing the phasing quality, imputing SGDP data with the phased region, and assessing the quality of the imputation...#

# Evaluate the GRCh38 panel
./create_and_assess_haplotype_panels.sh chr22_test $num_threads $GRCh38_suffix GRCh38

The resulting phased haplotype panels will be found in phased_panels/phased_CHM13v2.0_panel_$CHM13_suffix folder, while the equivelent subsets of the Byrska-Bishop phased GRCh38 panel can be found in phased_panels/phased_GRCh38_panel_$GRCh38_suffix.

Raw output from SHAPEIT5_switch phasing accuracy assessments can be found in SHAPEIT5_switch_output.

To replicate analysis in paper

Information on how to generate recombination maps can be found at https://github.com/mccoy-lab/1kgp_chm13_maps. This repository contains the portion of the analysis devoted to producing and evaluating a phased T2T-CHM13 reference panel.

Gather summary statistics

Option A: Download summary statistics

Researchers who wish to replicate this work on their personal computers should download the summary data parquet files from zenodo (https://zenodo.org/record/7612953).

Option B: Generate summary statistics from output of create_and_assess_haplotype_panels.sh

If you are interesting in replicating our work from scratch, please continue from step 3 above. These scripts gather the results of SHAPEIT5_switch and GLIMPSE2_concordance into experiment-wide dataframes.

Please note: These scripts can easily take up over 350GB of ram as written.

# Build the syntenic/nonsyntenic label files used by the concordance aggregation.
./scripts/create_syn_nonsyn_bins.sh CHM13v2.0 $CHM13_suffix imputation_statistics/imputation_results_${CHM13_suffix} true
./scripts/create_syn_nonsyn_bins.sh GRCh38 $GRCh38_suffix imputation_statistics/imputation_results_${CHM13_suffix} true

# Collect imputation statistics in one place:
./scripts/calc_genomewide_imputation_statistics_full.sh $GRCh38_suffix $CHM13_suffix $num_threads true

# Collect all data into a series of summary parquet files
python3 ./scripts/analysis/create_summary_phasing_dataframes_polars_regional.py \
    --CHM13_run_suffix $CHM13_suffix \
    --GRCh38_run_suffix $GRCh38_suffix \
    --test

GLIMPSE2_concordance output (Both GRCh38 and CHM13v2.0) will be found in imputation_statistics/imputation_results_${CHM13_suffix}

Summary parquet files will be found in intermediate data

Run data notebooks

Three jupyter notebooks were used to produce the final data reported in the paper. All can be found in the notebooks folder.

  • calc_figures_for_paper.ipynb
    • Calculates all values reported in the text of the paper. Only uses smaller summary parquet files; designed to be run on a laptop
  • calc_per_variant_figures_for_paper.ipynb
    • Calculates all values reported in text of the paper that require loading the full variant dataframes into memory. Run on a server.
  • make_plots.ipynb
    • Makes all main, extended, and supplemental figures in paper with the exception of Figure 6.
    • Figures will be saved to the figures folder
    • Tables will be saved to the tables folder

Run the Figure 6 script

Run scripts/figure6/Figure_6_script.R, which produces the karyotype plots used in Figure 6.

Rscript scripts/figure6/Figure_6_script.R
python3 scripts/figure6/stitch_svgs.py --batch figures/figure6
python3 scripts/figure6/stitch_svgs.py --grid figures/figure6

Switch Error Rates

Performance was measured by comparing the phased haplotypes of 39 samples shared between the Human Pangenome Reference Consortium (HPRC)'s draft human pangenome and the 1000 Genotypes dataset, or the 61 samples shared between HGSVC3 released assemblies and the 1000 Genomes Project.

Panel Source of ground truth assembly Trio Membership Switch Error Rate (%) Flip Error Rate (%) True Switch Error Rate (%) Genotype Discordance Rate (%)
GRCh38 HPRC Proband 0.408 0.186 0.029 1.114
GRCh38 HGSVC3 Proband 0.504 0.232 0.030 1.333
GRCh38 HGSVC3 Parent 0.705 0.303 0.080 1.457
GRCh38 HGSVC3 Non-trio 1.285 0.487 0.257 1.443
T2T-CHM13 HPRC Proband 0.355 0.166 0.019 0.918
T2T-CHM13 HGSVC3 Proband 0.428 0.202 0.019 1.153
T2T-CHM13 HGSVC3 Parent 0.495 0.235 0.020 1.277
T2T-CHM13 HGSVC3 Non-Trio 1.110 0.474 0.130 1.253

Abbreviated Methods

Variant filters used in this analysis (And string used to implement filter with bcftools)


  • exclude FILTER (column in the VCF) = PASS:

    FILTER!="PASS"

  • exclude variants with an alt allele of '*' after multiallelic splitting:

    ALT=='*'

  • exclude GT missingness rate < 5%

    F_MISSING>0.05

  • exclude Hardy-Weinberg p-value < 1e−10 in any 1000G subpopulation (as calculated in the 2504 unrelated 1KGP samples):

    INFO/HWE_EUR<1e-10 && INFO/HWE_AFR<1e-10 && INFO/HWE_EAS<1e-10 && INFO/HWE_AMR<1e-10 && INFO/HWE_SAS<1e-10

  • exclude sites where Mendelian Error Rate (Mendelian errors/num alleles) >= 0.05 (Note: 0.05*602 trios = 30 mendelian errors)

    INFO/MERR>=30

  • exclude homoalellelic sites

    MAC==0

  • exclude variants with a high chance of being errors as predicted by computational modeling*

    INFO/VQSLOD<0

Note: the SHAPEIT5 UK Biobank phasing paper excludes alternative alleles with AAscore < 0.5. This is a statistic produced by GraphTyper, which was not used to produce this dataset. The closest equivelent is the VQSLOD produced by Haplotype Caller. GraphTyper's AA score is simply the likelihood of an alternative allele truly being present in the dataset, so a cutoff of 0.5 is equivelent to 50% odds. Log odds of 1:1 is 0, so the VQSLOD log odds equivelent would be to exclude sites with a VQSLOD score of less than a cutoff of 0.


Phasing

Phasing was performed with SHAPEIT v5.1.1, largely in accordance with the recommendations outlined in SHAPEIT5's online tutorial. For each chromosome, common variants were phased in one chunk. Rare variants were phased in chunks of 40 megabases.

Chromosome X PAR regions were phased separately from the rest of chromosome X. To phase the body of chromsome X, male samples were provided to SHAPEIT5 via the --halpoid option. The provided Ne value was reduced to 75% of the overall Ne, to account for the reduced population of haplotypes in this region of chrX. Phasing statistics were only calculated for female samples.

Evaluation of phasing accuracy

We rely on two different methods of phasing quality evaluation:

To perform these evaluations, we phase the data set four times:

  • all 3202 samples, providing a pedigree
  • all samples excluding those identified as parents in the 1KGP pedigree (2002 samples)
  • all samples excluding those identified as parents of samples included in the draft pangenome (note: all shared 1KGP-pangenome samples are in the 1KGP dataset as children of trios).
  • Only pangenome samples, using the first data set (with pangenome samples and their parents excluded) as a reference panel.

We then calculate different sets of performance statistics using SHAPEIT5's switch tool.

Using family data as 'ground truth', we can:

  • Evaluate switch error rate of pedigree-informed 3202 sample panel, using family data
    • Measures best-case performance of phasing when using pedigree data
  • Evaluate switch error rate of 2002 sample no-parent phased panel using family data as outlined in SHAPEIT5's documentation.
    • Most valid measure of phasing accuracy when not using pedigree data
    • Sets upper bound on error rate, as phasing is performed with ~66% of our dataset's haplotypes

Using the 39 1KGP samples present in the draft pangenome, we can use the HPRC's empirically phased haplotypes to:

  • Evaluate switch error rate of the of pedigree-informed 3202 sample panel
    • Measures phasing accuracy when using a pedigree
  • Evaluate switch error rate of a panel generated using samples excluding those identified as parents in the 1KGP pedigree (2002 samples)
    • Measures phasing accuracy without a pedigree
  • Evaluate switch error rate when phasing a small number of samples (aka the 39 HPRC samples) using 2502 unrelated samples from the pedigree-informed 1KGP phased panel dataset as a reference (with the HPRC samples and parents excluded, of course)
    • Measures accuracy in most realistic use case

DOI

Computationally phased T2T 1KGP panel

This repository contains the code used to build and evaluate the CHM13v2.0- aligned 1000 Genomes Project phased panel described in Lalli et al. 2025. The repository is intended to be:

  • cloneable
  • container-runnable
  • reproducible at the software level

The biological inputs are not fully self-contained in git. Large external data files are required for both the smoke test and the full reproduction workflow.

Published outputs

What is in git

  • bin/: pinned static binaries used by the shell pipeline
  • scripts/: phasing, imputation, aggregation, and figure utilities
  • resources/: small tracked annotations, recombination maps, pedigrees, and sample lists
  • notebooks/: figure and paper-summary notebooks
  • figures/ and tables/: generated publication artifacts kept for reference
  • Dockerfile: reproducible runtime environment

What is not in git

The following are required at runtime but intentionally not tracked:

  • CHM13 and GRCh38 reference FASTA files plus indexes
  • unphased 1KGP callsets
  • phased GRCh38 reference panels
  • SGDP truth data
  • HPRC and HGSVC3 pangenome VCFs plus indexes

See resources/README.md for the canonical runtime paths and source URLs.

Supported workflows

1. Docker test

This is the recommended first run. It validates the containerized environment and the repo entrypoints by running the chr22_test and chr15_test regions for both CHM13v2.0 and GRCh38, then performing the downstream aggregation, notebook execution, and Figure 6 generation steps inside the container.

  1. Clone the repository.
  2. Download the canonical test inputs.
  3. Pull the smoke-test image.
  4. Copy docker.env.example to docker.env. Verify that docker.env descriibes the correct paths to the correct files. If you used the helper download script and kept the default repo layout, the paths should already match.
  5. Run the test.
git clone https://github.com/JosephLalli/phasing_T2T.git
cd phasing_T2T

bash scripts/utility/download_resources.sh --test
docker pull jlalli/phasing_t2t_dep_container:v2.0
cp docker.env.example docker.env

./scripts/run_docker_smoke_test.sh docker.env

Notes:

  • download_resources.sh --test is the supported way to fetch those inputs into the canonical repo-local paths used by docker.env.example.
  • Notebook execution and Figure 6 generation are on by default. Set RUN_NOTEBOOKS=0 in docker.env if you want to skip them.
  • Outputs are written to OUTPUT_DIR, which defaults to the project root and therefore uses the plain test folders.
  • Generated figures, tables, and executed notebooks are written to OUTPUT_DIR/figures, OUTPUT_DIR/tables, and OUTPUT_DIR/notebook_runs.

2. Full reproduction

For the full publication workflow, run the shell pipeline on the desired chromosomes or regions, then aggregate the outputs and execute the notebooks.

# Example test-region runs
./scripts/create_and_assess_haplotype_panels.sh chr22_test 12 test_CHM13 CHM13v2.0
./scripts/create_and_assess_haplotype_panels.sh chr15_test 12 test_CHM13 CHM13v2.0
./scripts/create_and_assess_haplotype_panels.sh chr22_test 12 test_GRCh38 GRCh38
./scripts/create_and_assess_haplotype_panels.sh chr15_test 12 test_GRCh38 GRCh38

# Build the syntenic/nonsyntenic label files used by the concordance aggregation
./scripts/create_syn_nonsyn_bins.sh CHM13v2.0 test_CHM13 imputation_statistics/imputation_results_test_CHM13 true
./scripts/create_syn_nonsyn_bins.sh GRCh38 test_GRCh38 imputation_statistics/imputation_results_test_CHM13 true

# Aggregate genome-wide imputation outputs
./scripts/calc_genomewide_imputation_statistics_full.sh test_GRCh38 test_CHM13 12 true

# Build summary parquet files
python3 ./scripts/analysis/create_summary_phasing_dataframes_polars_regional.py \
    --CHM13_run_suffix test_CHM13 \
    --GRCh38_run_suffix test_GRCh38 \
    --test

# Run notebooks and Figure 6 generation
cd notebooks
jupyter nbconvert --to notebook --execute --ExecutePreprocessor.timeout=600 calc_figures_for_paper.ipynb
jupyter nbconvert --to notebook --execute --ExecutePreprocessor.timeout=600 calc_per_variant_figures_for_paper.ipynb
jupyter nbconvert --to notebook --execute --ExecutePreprocessor.timeout=600 make_plots.ipynb
cd ..

Rscript ./scripts/figure6/Figure_6_script.R
python3 ./scripts/figure6/stitch_svgs.py --batch ./figures/figure6
python3 ./scripts/figure6/stitch_svgs.py --grid ./figures/figure6

The main output locations are:

  • phased_panels/
  • SHAPEIT5_switch_output/
  • imputation_statistics/
  • intermediate_data/

Notebook and figure generation

  • notebooks/calc_per_variant_figures_for_paper.ipynb Performs heavier per-variant analyses.
  • notebooks/calc_figures_for_paper.ipynb Computes paper-level summary values from the aggregated parquet files.
  • notebooks/make_plots.ipynb Produces the main and supplemental figures, except Figure 6.
  • scripts/figure6/Figure_6_script.R Generates the Figure 6 karyotype visualizations.

Publication notes

  • The publication branch no longer tracks machine-specific symlinks under resources/.
  • The Docker environment is the source of truth for software reproducibility.
  • If long-term bitwise reproducibility matters, archive the exact external input files and checksums alongside the repository release.

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Repository for code to produce phased 1000 Genomes Project haplotypes called against the CHM13v2 T2T reference genome.

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Apr 30, 2026
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