The man, the plant, and the insect: shooting host specificity determinants in Serratia marcescens pangenome

The repository with working scripts, figures, data, and supplementary materials for pangenome analysis of 73 Serratia marcescens genomes.

Contents

This repository contains scripts for statistical analysis of pangenome, phylogeny, and phenotypical associations. Please consult the Methods section in the paper for extra details:

Shikov A.E., Merkushova A.V., Nizhnikov A.A., Antonets K.S. (2023) The man, the plant, and the insect: shooting host specificity determinants in Serratia marcescens pangenome. Front. Microbiol. 14:1211999. https://doi.org/10.3389/fmicb.2023.1211999

Figures

Figures are available in the pics/ directory. For the description, please consult the Results section of the article.

Supplementary

All supplementary material is located in the supplementary/ directory. Supplementary_figures.docx is a Microsoft Word document with a description of all supplementary figures. Supplementary_tables.xlsx is an Exel table with all supplementary tables.

• The supplementary/pics/ folder contains individual supplementary figures.
• For convenience, in the supplementary/tables/ folder individual supporting tables in CSV format are provided. For a detailed description of the tables, please consult the Supplementary_tables.xlsx file.

Data

Analyzed data are included in the data/ directory.

• data/pangenomes/ folder contains gene presence/absence tables obtained using Panaroo, PEPPAN, and Roary.
• data/trees/ folder contains phylogenetic inferences in Newick format based on core gene alignments (aligned with MAFFT of Prank), either partitioned or unpartitioned, and the results of clusterizations based on gene presence/absence patterns.
• data/scoary/ folder includes raw Scoary results attributed to particular hosts, namely, humans, plants, and insects.

Scripts

The scripts/ directory includes all code used for pangenome analysis.

• IO_lib.py is an ancillary script with functions for processing CSV files;
• download_serratia_assemblies.sh was used to download Serratia marcescens genomes in fna and gff formats from the RefSeq database;
• search_entrez.py was applied to extract metadata of studied assemblies;
• metadata_downloader.py was used to obtain the list of affected hosts from the NCBI BioSample database;
• process_serratia_table.py was used to calculate mean GC content, the number of CDS, and genome length and summarize assemblies' metadata to a single table;
• add_numbers_to_presence.py was utilized to add the number of genomes in which gene clusters are presented to gene presence/absence tables;
• add_assembly_codes_to_peppan.py- script for renaming labels in Newick tree generated by PEPPAN on the basis of gene presence/absence patterns;
• calculate_mash_dist.py - script for creating matrix based on ANI values using mash utility;
• make_accessory_alignment.py was used to generate pseudoalignment representing the presence/absence of accessory genes;
• create_paritions.py was applied to obtain partitioned alignment as well as the partitioning scheme for all core genes in the pangenome;
• calculate_mean_support_new.py - script for calculating mean bootstrap support of the phylogenetic tree;
• compare_tree_topologies.py was utilized for comparing phylogenetic inferences using quartet distance metrics;
• make_table_for_scoary.py - script for generating a table with phenotypic traits used for Scoary analysis;
• filter_scoary_hits.py was applied to extract positively associated gene clusters from Scoary-generated results;
• calculate_number_of_host_genes_for_assemblies.py- script for summarizing the number of host specificity factors per assembly;
• add_genome_nums_to_VFDB.py was applied to add the number of genomes in which the cluster with virulence factors was found;
• filter_virDB_hits.py - script for filtering hits matching the VFDB database using identity and coverage;
• split_fasta_to_chunks.py - script for re-naming Panaroo-attributed gene codes to real protein accession numbers;
• assert_groups_to_genes_pos_scoary_hits.py was used for grouping protein sequences into 8 categories, namely, pangenomic (core, accessory, and unique), virulence (core and accessory), and specificity genes attributed to a particular host (human, insect, plant);
• prepare_ontology_data.py was utilized to generate a universe of functional annotation terms for the over-representation test;
• get_groups_genes_extracted_for_GO.py - script for grouping GO terms according to eight aforementioned groups;
• perform_enrichment_anaysis.py was applied to perform an over-representation test using hypergeometric distribution;
• create_matrix_for_enrichments.py was used for generating a dissimilarity matrix based on significant functional terms;
• Serratia_pangenome_plots.R - main R script for generating all figures, clusterizations, assessing pangenome openness, graph-wise testing GO terms using topGO, summarizing data on virulences/specificity factors and mobile genetic elements.