PPanGGOLiN complete workflow analyses

We tried to make PPanGGOLiN relatively easy to use by making a ‘complete workflow’ subcommand called all. It runs a pangenome analysis whose exact steps will depend on the input files you provide it with. In the end, you will have a partitioned pangenome graph with predicted RGP, spots and modules.

--- title: "Workflow Overview: Steps launched by the all command" align: center --- %%{init: {'theme':'default'}}%% graph LR i[input genomes] --> a r:::panrgp s:::panrgp m:::panmodule subgraph Pangenome creation a:::workflow c:::workflow g:::workflow p:::workflow a("annotate") --> c c(cluster) --> g(graph) g(graph) --> p(partition) end subgraph Functional module p --> m(module) end subgraph Region of Genomic Plasticity p --> r(rgp) r --> s(spot) end p --> f[pangenome.h5] s --> f m --> f classDef panrgp fill:#84d191 classDef panmodule fill:#d44066 classDef workflow fill:#d4ae40

The minimal subcommand only need your own annotations files (using .gff or .gbff/.gbk files) as long as they include the genomic dna sequences, such as the ones provided by Prokka or Bakta.

ppanggolin all --anno genomes.gbff.list

It uses parameters that we found to be generally the best when working with species pangenomes.

The file genomes.gbff.list is a tab-separated file with the following organisation :

  1. The first column contains a unique genome name

  2. The second column the path to the associated annotation file

  3. Each line represents a genome

An example with 50 Chlamydia trachomatis genomes can be found in the testingDataset directory.

You can also give PPanGGOLiN .fasta files, such as:

ppanggolin all --fasta genomes.fasta.list

Again you must use a tab-separated file but this time with the following organisation:

  1. The first column contains a unique genome name

  2. The second column the path to the associated FASTA file

  3. Circular contig identifiers are indicated in the following columns

  4. Each line represents a genome

Same, an example can be found in the testingDataset directory.

Tip

Downloading genomes from NCBI refseq or genbank for a species of interest can be easily accomplished using CLI tools like ncbi-genome-download or the genome updater script.

For instance to download the GTDB refseq genomes of Bradyrhizobium japonicum with genome updater, you can run the following command

genome_updater.sh -d "refseq"  -o "B_japonicum_genomes" -M "gtdb" -T "s__Bradyrhizobium japonicum"

After the completion of the all command, all of your genomes have had their genes predicted, the genes have been clustered into gene families, a pangenome graph has been successfully constructed and partitioned into three distinct partitions: persistent, shell, and cloud. Additionally, RGP, spots, and modules have been detected within your pangenome.

The results of the workflow is saved in the pangenome.h5 file, which is in the HDF-5 file format. When you run an analysis using this file as input, the results of that analysis will be added to the file to supplement the data that are already stored in it. The idea behind this is that you can store and manipulate your pangenome with PPanGGOLiN by using this file only. It will keep all the information about what was done, all the parameters used, and all the pangenome’s content.

Tip

Many option are available to tune your analysis. Take a look here.