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SupplementalMethods_PPanGGOLiN.md

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"Genomic Stability and Genetic Defense Systems in Dolosigranulum pigrum a Candidate Beneficial Bacterium from the Human Microbiome" Supplemental Methods

PPanGGOLiN ANALYSIS

PPanGGOLiN v1.1.141 (Gautreau et al. 2020; Bazin et al. 2020) was installed in a Python environment called PPanGGOLiN.

Importing Prokka annotations and Anvi’o 7 clusters

In order to import the Anvi’o clustering into PPanGGOLiN we needed:

  1. A .tsv file listing in the first column the gene family names, and in the second column the gene ID that is used in the annotation files.
  2. The annotated genomes with gene IDs that match the ones listed in the previous .tsv file.

Using the anvi-summarize output analysis_Anvio7/Pangenomic_Results_Dpig/PAN_DPIG_prokka_gene_clusters_summary.txt.gz described before we can create the .tsv file:

Dpig_Anvio7 <- read_delim("analysis_Anvio7/Pangenomic_Results_Dpig/PAN_DPIG_prokka_gene_clusters_summary.txt.gz", "\t")
Dpig_Anvio7 <- Dpig_Anvio7 %>% 
  unite(new_id, genome_name:gene_callers_id, sep = "___", remove = FALSE)
Clusters_Dpig_Anvio7 <- select(Dpig_Anvio7, c(gene_cluster_id, new_id))
write_delim(Clusters_Dpig_Anvio7, "analysis_PPanGGOLiN_Anvio7/Clusters_Dpig_Anvio7.tsv", col_names=FALSE)

IMPORTANT: The gene_callers_id provided by Anvi’o don’t match the original ones in the Prokka annotation. The Prokka annotated genomes were parsed into two text files, one for gene calls and one for annotations, with the script gff_parser.py. By default Prokka annotates also tRNAs, rRNAs and CRISPR regions. However, gff_parser.py will only utilize open reading frames reported by Prodigal in the Prokka output in order to be compatible with the pangenomic Anvi’o pipeline. While parsing new gene_callers_id are generated only for the ORFs that will be imported into Anvi’o. Fortunately anvi-get-sequences-for-gene-calls can be used to export new .gff files with only the ORFs and these can be used for PPanGGOLiN, instead of the original Prokka .gff files:

#conda activate anvio-7
mkdir -p "analysis_Anvio7/Exported_gffs"

path_f="analysis_Anvio7/Contigs_db_prokka_Dpig"
path_o="analysis_Anvio7/Exported_gffs"

for file in $path_f/*.db; do
    FILENAME=`basename ${file%.*}`
    anvi-get-sequences-for-gene-calls -c $file --export-gff3 \
                                      -o $path_o/Anvio7_$FILENAME.gff
      
done

We created lists with the file names for both the Anvi’o exported .gff (analysis_PPanGGOLiN_Anvio7/Anvio7GenomesExported.gff.txt) and the renamed .fasta files (analysis_PPanGGOLiN_Anvio7/Anvio7GenomesReformatted.fa.txt) and use them to run the annotate PPanGGOLiN subcommand. Then, the cluster PPanGGOLiN subcommand was run using the .tsv file created before from the anvi-summarize output.

#conda activate PPanGGOLiN
ppanggolin annotate --anno analysis_PPanGGOLiN_Anvio7/Anvio7GenomesExported.gff.txt --fasta analysis_PPanGGOLiN_Anvio7/Anvio7GenomesReformatted.fa.txt -o analysis_PPanGGOLiN_Anvio7/OutputFromAnvio7 --basename FromAnvio7

ppanggolin cluster -p analysis_PPanGGOLiN_Anvio7/OutputFromAnvio7/FromAnvio7.h5 --clusters analysis_PPanGGOLiN_Anvio7/Clusters_Dpig_Anvio7.tsv --infer_singletons

Graphing and Partitioning

The PPanGGOLiNgraph subcommand has only a single other option, which is ‘-r’ or ‘–remove_high_copy_number.’ If used, it will remove the gene families that are too duplicated in your genomes. This is useful if you want to visualize your pangenome afterward and want to remove the biggest hubs to have a clearer view. It can also be used to limit the influence of very duplicated genes such as transposase or ABC transporters in the partition step.

We ran the PPanGGOLiN graph and partition subcommands with default parameters.

#conda activate PPanGGOLiN
ppanggolin graph -p analysis_PPanGGOLiN_Anvio7/OutputFromAnvio7/FromAnvio7.h5

ppanggolin partition -p analysis_PPanGGOLiN_Anvio7/OutputFromAnvio7/FromAnvio7.h5

Writing outpus

For details on PPanGGOLiN outputs see: https://github.com/labgem/PPanGGOLiN/wiki/Outputs

#conda activate PPanGGOLiN
ppanggolin write -p analysis_PPanGGOLiN_Anvio7/OutputFromAnvio7/FromAnvio7.h5 -o analysis_PPanGGOLiN_Anvio7/OutputFromAnvio7 --light_gexf --gexf  --csv --Rtab --stats --partitions --projection --families_tsv -f

Finding Regions of Genome Plasticity

For details on RGPs see: https://github.com/labgem/PPanGGOLiN/wiki/Regions-of-Genome-Plasticity

#conda activate PPanGGOLiN
ppanggolin rgp -p analysis_PPanGGOLiN_Anvio7/OutputFromAnvio7/FromAnvio7.h5

ppanggolin spot -p analysis_PPanGGOLiN_Anvio7/OutputFromAnvio7/FromAnvio7.h5 --label_priority ID --draw_hotspots -o analysis_PPanGGOLiN_Anvio7/OutputFromAnvio7/spots_ID -f

ppanggolin write -p analysis_PPanGGOLiN_Anvio7/OutputFromAnvio7/FromAnvio7.h5 -o analysis_PPanGGOLiN_Anvio7/OutputFromAnvio7 --regions --spots -f

Summary of used parameters

The PPanGGOLiN info subcommand indicates, for each steps of the analysis, the PPanGGOLiN parameters that were used and the source of the data if appropriate.

#conda activate PPanGGOLiN
ppanggolin info -p analysis_PPanGGOLiN_Anvio7/OutputFromAnvio7/FromAnvio7.h5 --parameters

The output for this command was:

  • annotation

    • read_annotations_from_file: True

  • cluster

    • read_clustering_from_file: True
    • infer_singletons: True

  • graph

    • removed_high_copy_number_families: False

  • partition

    • beta: 2.5
    • free_dispersion: False
    • max_node_degree_for_smoothing: 10
    • computed_K: True
    • K: 3

  • RGP

    • persistent_penalty: 3
    • variable_gain: 1
    • min_length: 3000
    • min_score: 4
    • dup_margin: 0.05

  • spots

    • set_size : 3
    • overlapping_match : 2
    • set_size: 3
    • overlapping_match: 2
    • exact_match: 1

REFERENCES

Bazin, Adelme, Guillaume Gautreau, Claudine Médigue, David Vallenet, and Alexandra Calteau. 2020. “panRGP: A Pangenome-Based Method to Predict Genomic Islands and Explore Their Diversity.” Bioinformatics 36 (Supplement_2): i651–58. https://doi.org/10.1093/bioinformatics/btaa792.

Gautreau, Guillaume, Adelme Bazin, Mathieu Gachet, Rémi Planel, Laura Burlot, Mathieu Dubois, Amandine Perrin, et al. 2020. “PPanGGOLiN: Depicting Microbial Diversity via a Partitioned Pangenome Graph.” Edited by Christos A. Ouzounis. PLOS Computational Biology 16 (3): e1007732. https://doi.org/10.1371/journal.pcbi.1007732.