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Alexis Mergez authored
Using config.yaml, make some steps optional as PAV matrices creation
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Pan1c workflow

Pan1c (Pangenome 1 Chromosome) : a snakemake workflow for creating a pangenome at a chromosomic scale. Tools used within the workflow :

The file architecture for the workflow is as follow :

Pan1c/
├── config.yaml
├── example
│   ├── CICC1445.fa.gz
│   ├── config_CICD.yaml
│   ├── R64.fa.gz
│   ├── SX2.fa.gz
│   └── workflow.svg
├── getApps.sh
├── README.md
├── runSnakemake.sh
├── scripts
│   ├── getPanachePAV.sh
│   ├── inputClustering.py
│   ├── ragtagChromInfer.sh
│   ├── statsAggregation.py
│   └── workflowStats.py
└── Snakefile

Example DAG

This DAG shows the worflow for a pangenome of Arabidospis Thaliana using the TAIR10.1 reference. Workflow DAG

Prepare your data

This workflow can take chromosome level assemblies as well as contig level assemblies.
Fasta files need to be compressed using bgzip2 (included in PanGeTools). Records id must follow this pattern : <haplotype name>#<ctg|chr name>. (CHM13#chr01 for example where the fasta file is named CHM13.fa.gz).
Because of the clustering step, this pattern is only needed for the reference assembly.

Note : Input files should be read-only to prevent snakemake to mess with them (which seems to happen in some rare cases).

Download apptainer images

Before running the worflow, some apptainer images needs to be downloaded. Use the script getApps.sh to do so :

./getApps.sh -a <apps directory>

Running the workflow

Clone this repository and create a data/haplotypes directory where you will place all your haplotypes.
Update the reference name and the apptainer image directory in config.yaml.
Then, modify the variables in runSnakemake.sh to match your requirements (number of threads, memory, job name, email, etc.).
Navigate to the root directory of the repository and execute sbatch runSnakemake.sh!