PPanGGOLiN: Depicting microbial species diversity via a Partitioned PanGenome Graph Of Linked Neighbors
PPanGGOLiN (Gautreau et al. 2020) is a software suite used to create and manipulate prokaryotic pangenomes from a set of either genomic DNA sequences or provided genome annotations. It is designed to scale up to tens of thousands of genomes. It has the specificity to partition the pangenome using a statistical approach rather than using fixed thresholds which gives it the ability to work with low-quality data such as Metagenomic Assembled Genomes (MAGs) or Single-cell Amplified Genomes (SAGs) thus taking advantage of large scale environmental studies and letting users study the pangenome of uncultivable species.
PPanGGOLiN builds pangenomes through a graphical model and a statistical method to partition gene families in persistent, shell and cloud genomes. It integrates both information on the presence/absence of protein-coding genes and their genomic neighborhood to build a graph of gene families where each node is a gene family, and each edge is a relation of genetic contiguity. The partitioning method promotes that two gene families that are consistent neighbors in the graph are more likely to belong to the same partition. It results in a Partitioned Pangenome Graph (PPG) made of persistent, shell and cloud nodes drawing genomes on rails like a subway map to help biologists navigate the great diversity of microbial life.
Moreover, the panRGP method (Bazin et al. 2020) included in PPanGGOLiN predicts, for each genome, Regions of Genome Plasticity (RGPs) that are clusters of genes made of shell and cloud genomes in the pangenome graph. Most of them arise from Horizontal gene transfer (HGT) and correspond to Genomic Islands (GIs). RGPs from different genomes are next grouped in spots of insertion based on their conserved flanking persistent genes.
Those RGPs can be further divided in conserved modules by panModule (Bazin et al. 2021). Those conserved modules correspond to groups of cooccurring and colocalized genes that are gained or lost together in the variable regions of the pangenome.
A complete documentation is available here.
PPanGGOLiN can be is easily installed via conda, accessible through the bioconda channel.
To ensure a smoother installation and avoid conflicting dependencies, it's highly recommended to create a dedicated environment for PPanGGOLiN:
# Install PPanGGOLiN into a new conda environment
conda create -n ppanggolin -c defaults -c conda-forge -c bioconda ppanggolin
# Check PPanGGOLiN install
conda activate ppanggolin
ppanggolin --versionA complete pangenomic analysis with PPanGGOLiN can be performed using the all subcommand. This workflow runs a series of PPanGGOLiN commands to generate a partitioned pangenome graph with predicted RGPs (Regions of Genomic Plasticity), spots of insertion and modules.
Execute the following command to run the all workflow:
ppanggolin all --fasta GENOMES_FASTA_LISTBy default, it uses parameters that we have found to be generally the best for working with species pangenomes. For further customization, you can adjust some parameters directly on the command line. Alternatively, you can use a configuration file to fine-tune the parameters of each subcommand used by the workflow (see here for more details).
The file GENOMES_FASTA_LIST is a tsv-separated file with the following organization :
- The first column contains a unique genome name (without space)
- The second column contains the path to the associated FASTA file
- Circular contig identifiers are indicated in the following columns
- Each line represents a genome
An example with 50 Chlamydia trachomatis genomes can be found in the testingDataset/ directory.
You can also give PPanGGOLiN your own annotations using .gff or .gbff/.gbk files instead of .fasta files, such as the ones provided by Bakta with the following command :
ppanggolin all --anno GENOMES_ANNOTATION_LISTAnother example of such a file can be found in the testingDataset/ directory.
A minimum of 5 genomes is generally required to perform a pangenomic analysis using the traditional core genome/accessory genome paradigm. It is recommended to use at least 15 genomes with genomic variation (and not only SNPs) to obtain robust results with the PPanGGOLiN statistical approach.
Upon executing the all command, multiple output files and graphics are generated (more information here). Most notably, it writes an HDF-5 file (pangenome.h5).
This file can be used as input to any of the subcommands to rerun parts of the analysis with different parameters,
write and draw different representations of the pangenome, or perform additional analyses with PPanGGOLiN.
PPanGGOLiN offers additional workflow commands that perform more specialized functions:
workflow: Generates a partitioned pangenome graph.panrgp: Combine theworkflowcommand and the prediction of RGPs (Regions of Genomic Plasticity) and insertion spots on top of the partitioned pangenome graph.panmodule: Combine theworkflowcommand and the prediction of Modules on top of the partitioned pangenome graph.
These commands utilize the same type of file input as the all command.
If you have any questions or issues with installing, using or understanding PPanGGOLiN, please do not hesitate to post an issue! We cannot correct bugs if we do not know about them, and will try to help you the best we can.
If you use this tool for your research, please cite:
Gautreau G et al. (2020) PPanGGOLiN: Depicting microbial diversity via a partitioned pangenome graph. PLOS Computational Biology 16(3): e1007732. https://doi.org/10.1371/journal.pcbi.1007732
If you use this tool to study genomic islands, please cite:
Bazin et al., panRGP: a pangenome-based method to predict genomic islands and explore their diversity, Bioinformatics, Volume 36, Issue Supplement_2, December 2020, Pages i651–i658, https://doi.org/10.1093/bioinformatics/btaa792
If you use this tool to study modules, please cite:
Bazin et al., panModule: detecting conserved modules in the variable regions of a pangenome graph. biorxiv. https://doi.org/10.1101/2021.12.06.471380