Swine_ExPEC_genomes_analysis

The following is a description of all these files.

Detection_Genomic_Islands

• get_genes_backbone.py

  Python script for the detection of Genomic Islands on the chromosomes.

• python.sh

  Running the above Python script to detect GI1 and GI2.

• tableS1_new.txt

  Input file for the Python script. This file contains details of 499 isolates.

• 499_ARG_strAB_IS_minid90_mincov80.txt

  Input file for the Python script. This file contains ARG and IS annotations for 499 genomes.

• IS1H,sul2,strA,strB__backbone.txt

  The result of GI1 detection. This file contains information about Genomic Island 1—strB, strA, sul2, IS1H located on the same contig.

• IS1H,sul2,strA,strB__backbone.xlsx

  Proofreading based on the result of GI1 detection above. Strains highlighted in yellow have been excluded.

• IS1H,sul2,strA,strB_bar_input.txt

  This file contains details of isolates that harbor GI1.

• IS5075,sul2,strA,strB__backbone.txt

  The result of GI2 detection. This file contains information about Genomic Island 2—strB, strA, sul2, IS5075 located on the same contig.

• IS5075,sul2,strA,strB__backbone.xlsx

  Proofreading based on the result of GI2 detection above. Strains highlighted in yellow have been excluded.

• IS5075,sul2,strA,strB_bar_input.txt

  This file contains details of isolates that harbor GI2.

Visualization

• UpSet_plot

  • upsetR.R

    R script for employing UpSetR v1.4.0 to visualize phenotype data

  • AR_profile_input.csv

    Input file for the R script. This file contains antibiotic resistance phenotypes of 485 isolates.

  • AR_profile.pdf

    UpSet plot illustrating the diversity of antibiotic resistance profiles for ExPEC isolates. This figure was based on the output result from the above R code, and was subsequently retouched using Adobe Illustrator.

• ARP_profile_in_four_STs

  • bar_facet.R

    R script for employing ggplot2 v3.3.6 to visualize phenotype data in four STs and all 485 isolates.

  • AR_profile_all_bar.csv

    Input file for the R script. This file contains the prevalence of antimicrobial resistance in four common STs and all 485 isolates..

  • ARP_lineage_drug.pdf

    Histogram illustrating the prevalence of antimicrobial resistance phenotypes. This figure was retouched using Adobe Illustrator to split it into Fig. 2b and Fig. 2c.

• Gene_arrow_map

  • gggenes.R

    R script for employing gggenes v0.4.1 to illustrate gene arrow maps of ARGs and their neighboring ISs in all complete genomes.

  • all_IS_arg_change_filter_gggenes_remove.txt

    Input file for the R script. This file contains position, strand, and class of genes.

  • gggenes.pdf

    The output result from the above R code.

  • Figure_4.pdf

    Based on the figure 'gggenes.pdf', we subsequently used Adobe Illustrator to retouch and combine as Fig. 4a.

  • Figure_5.pdf

    Based on the figure 'gggenes.pdf', we subsequently used Adobe Illustrator to retouch and combine as Fig. 5a-d.

• pheatmap_in_Fig4

  • pheatmap.R

    R script for employing pheatmap v1.0.12 to visualize Jaccard similarity matrix of plasmid genomes.

  • distances.csv

    Input file for the R script 'pheatmap.R'.

  • pheatmap.pdf

    The output result from the R script 'pheatmap.R'.

  • bar.R

    R script for employing ggplot2 v3.3.6 to illustrate the stacked histograms, which show the distribution ExPEC isolates for various phylogroups harboring genomic islands and the number of genomes for different phylogroups harboring each plasmid.

  • strAB_all_coverage_0.9_0.8_chr.txt

    Input file for the R script 'bar.R'.

  • bar.pdf

    The output result from the R script 'bar.R'.

• pheatmap_in_Fig5

  • pheatmap.R

    R script for employing pheatmap v1.0.12 to visualize Jaccard similarity matrix of plasmid genomes.

  • distances.csv

    Input file for the R script 'pheatmap.R'.

  • pheatmap.pdf

    The output result from the R script 'pheatmap.R'.

  • bar.R

    R script for employing ggplot2 v3.3.6 to illustrate the stacked histograms, which show the number of genomes for different phylogroups harboring each plasmid.

  • all_coverage_0.9_0.8_retain_high_change_group.txt

    Input file for the R script 'bar.R'.

  • bar.pdf

    The output result from the R script 'bar.R'.

• barplot_for_annual_count

  • bar.R

    R script for employing ggplot2 v3.3.6 to illustrate the bar plot, which show the annual count of isolated strains.

  • year.txt

    Input file for the R script.

  • year.pdf

    The output result from the above R code.

• pie_chart_for_tissues

  • source.R

    R script for employing ggplot2 v3.3.6 to illustrate the pie chart, which show the distribution of isolates from various tissues.

  • source.txt

    Input file for the R script.

  • source.pdf

    The output result from the above R code.

• pie_chart_for_MLST_and_serotype

  • pie.R

    R script for employing ggplot2 v3.3.6 to illustrate two pie charts, which show the distribution of 499 ExPEC isolates across different MLSTs and serotypes.

  • mlst_input.txt & ser_com_input.txt

    Input files for the R script.

  • mlst.pdf & ser_com.pdf

    The output result from the above R code.

  • Figure_S2.pdf

    The image is generated by combining the above pictures using Adobe Illustrator.