This is a workflow called Pangenome-Informed Genome Assembly (PIGA) for population-scale diploid genome assembly, which employs a pangenome graph as a unified framework to integrate sequence information across individuals and perform joint diploid genome assembly.

Compared to the current assembly methods, the PIGA workflow fully utilizes multiple sources of information (long read, short read, internal haplotype, external assembly panel) and is well adapted to low-coverage and modest-coverage situations.

First, clone the PIGA repository from GitHub:

git clone https://github.com/JianYang-Lab/PIGA.git
cd PIGA

PIGA uses Snakemake and requires several software dependencies. The easiest way to install them is by creating a dedicated conda environment from the provided YAML file.

# Create the conda environment named 'piga'
conda env create -f environment.yaml -n piga

# Activate the environment to use PIGA
conda activate piga

Some tools are not available via conda and need to be built from source or downloaded separately.

# Build and download additional tools
bash build.sh

# Download the test dataset
cd test_data
bash download.sh

The configuration file should contain:

• samples: Path to a text file listing all samples. The file should be tab-delimited, with:

  • Column 1: Sample name
  • Column 2: Sample sex

• sr_fastqs: Paths to the paired-end short-read FASTQ files. Use {sample} as a wildcard.

• lr_hifi_fastqs: Paths to PacBio HiFi long-read FASTQ files. Use {sample} as a wildcard.

• lr_zmw_fastqs: Paths to PacBio ZMW FASTQ files (selected representative read for each ZMW). Use {sample} as a wildcard.

• lr_subreads_bam: Paths to PacBio subreads BAM files. Use {sample} as a wildcard.

• reference:

  • CHM13: Path to the CHM13 reference genome in FASTA format.
  • GRCh38: Path to the GRCh38 reference genome in FASTA format.

• GATK_Resource: Paths to GATK reference resource datasets for quality control.

• external_pangenome: Path to the external pangenome graph in GBZ format.

• par_region: Path to the pseudoautosomal region (PAR) BED file for CHM13.

• external_assembly_list: Path to a file listing external genome assemblies used for constructing the PIGA pangenome.

• train_sample_list: Path to a file listing names of matched assemblies from training samples used for model training. The file should be tab-delimited, with:

  • Column 1: Names of PIGA draft assemblies from training samples
  • Column 2: Names of matched high-quality assemblies from training samples used as the truth

• prefix: Prefix used for naming output files.

After setting up the configuration file (config/config.yaml) with your sample data, you can run the entire PIGA workflow. There are two main ways to execute it:

This method is suitable for running PIGA on a single, powerful machine. It will use the specified number of cores on the local machine.

# Dry run test
snakemake -s Snakefile --cores 64 --configfile config/config.yaml --workflow-profile ./profile/config_local/ --dry-run
# Run PIGA locally using up to 64 cores
snakemake -s Snakefile --cores 64 --configfile config/config.yaml --workflow-profile ./profile/config_local/

This method is designed for high-performance computing (HPC) environments and uses a job submission system (e.g., SLURM) to distribute the workload across a cluster.

# Run PIGA using a workflow profile to submit jobs to a cluster
snakemake -s Snakefile --cores 64 --jobs 64 --configfile config/config.yaml --workflow-profile ./profile/config_slurm/

Note: By default, we provide a profile which is configured to use the SLURM job scheduler. You can customize the cluster settings (e.g., switch to a different scheduler or change resource allocation) by editing the configuration file.

PIGA consists of six modules, each containing several commands that can be executed independently. A detailed tutorial is provided for each module.

• call_sr_snv: detect SNVs using short reads.
• call_lr_snv: detect SNVs using long reads.
• merge_snv: merge the short-read SNV callset and long-read SNV callset.

• phase_snv: perform SNV haplotype phasing leveraging long-read and population information.

• generate_personal_reference: generate personalized reference by modifying the reference genome with homozygous variants genotyped from the external pangenome.

• draft_assembly: partition long reads into haplotypes and produce draft diploid assemblies.

• construct_pangenome: construct and refine the base-level pangenome.
• simplify_pangenome: simplify the pangenome.
• merge_pangenome: merge pangenome subgraphs into the final pangenome.

• infer_diploid_path: reconstruct the final diploid assembly by inferring the diploid paths.

License: MIT License