Pangebin

Setup Python virtual environment

• [For dev] Create the conda environment

  conda env create -n pangebin-dev -f config/condaenv_311-dev.yml

• [For dev] Activate the conda environment

  conda activate pangebin-dev

Usage

Example with the dataset SAMN16357463:

dataset_dir="test/SAMN16357463"

Note: each script has a command clean

./script.sh clean $dataset_dir

1. standardize GFA assembly graphs

   ./test/std_asm_graph.sh run $dataset_dir

2. make pangenome graph with nextflow (make sur you have installed the command for the nextflow profile)

   ./test/pangenome.sh run $dataset_dir

3. make pan-assembly graph

   ./test/panassembly.sh run $dataset_dir

4. Obtain the GC probability scores of the fragments

   ./test/gc_prob_scores.sh run $dataset_dir

5. Obtain gene density on the fragments

   1. Map the gene on the contigs from the two assemblers

      ./test/gene_mapping.sh blast $dataset_dir

   2. Filter the gene mappings

      ./test/gene_mapping.sh filter $dataset_dir

   3. Obtain the gene density on the fragments

      ./test/frag_gene_densities.sh run $dataset_dir

6. Obtain the seed from positive gene densities

   ./test/fragment_seeds.sh run $dataset_dir

7. Execute PlasBin-Flow modified for pan-assembly

   ./test/plasbin_panasm.sh run $dataset_dir

Pangebin-PlasBin-flow conversion

Use PlasBin-flow inputs to Pangebin

# pbf : PlasBin-flow
# pg : Pangebin
# Convert plasmidness
pangebin utils pbf-comp plm pbf_plasmidness.tsv pg_plasmidness.tsv
# Convert seeds
pangebin utils pbf-comp seeds pbf_seeds.tsv pg_seeds.tsv
# Recompute GC contents
pangebin sub gc from-gfa graph.gfa pg_gc_scores.tsv
# Run Pangebin on assembly graph
pangebin sub plasbin asm graph.gfa pg_seeds.tsv pg_gc_scores.tsv pg_plasmidness.tsv --outdir pg_outdir

Convert Pangebin outputs to PlasBin-flow output

pangebin utils pbf-comp bins pg_outdir pbf_bins.tsv

Going further into the details

Understanding GFA tags system:

• doc/gfa/standardized.md
• doc/gfa/panassembly.md