Pan-Genome Analysis#

In this tutorial we will learn how to determine a pan-genome from a collection of isolate genomes. The pangenome analysis will be based on the genome annotations made in the previous step, that we obtained thanks to Prokka. The presence/absence of genes will be used by Roary for determining genes belonging to the core genome, and the genes being accessory among the pangenome (not present in all strains).

We will use Roary for performing this pangenome analysis.

Prepare our computing environment#

We will first run the appropriate srun command to book the computing cores (cpus) on the cluster.

srun -p SELECTED_PARTITION --cpus-per-task 2 --pty bash -i

You are now on a computing node, with computing 2 cpus reserved for you. That way, you can run commands interactively.

If you want to exit the srun interactive mode, press CTRL+D or type exit

Input data#

Prokka requires assembled contigs. You can prepare you working directory for this annotation tutorial.

cd results/annotation
ls -l

Put all the .gff files in the same folder (e.g., ./gff) for running Roary

mkdir gff
cp K*prokka/*.gff gff/

Pan-genome analysis#

We will now run Roary from the gff directory

module load bioinfo/roary/3.12.0

cd gff
roary -p 4 -o 5_Kp_roary_clust -f 5_Kp_roary -e -n -r -v  *.gff

This can take 15-20 min. Roary will get all the coding sequences, convert them into protein, and create pre-clusters. Then, using BLASTP and MCL, Roary will create clusters, and check for paralogs. Finally, Roary will take every isolate and order them by presence/absence of orthologs. The summary output is present in the summary_statistics.txt file.

Additionally, Roary produces a gene_presence_absence.csv file that can be opened in any spreadsheet software to manually explore the results. In this file, you will find information such as gene name and gene annotation, and, of course, whether a gene is present in a genome or not.

Inspect the results files produced

cd 5_Kp_roary
ls -l

Plotting the results#

Roary comes with a python script that allows you to generate a few plots to graphically assess your analysis output.

First, we need to generate a tree file from the alignment generated by Roary:

FastTree -nt -gtr core_gene_alignment.aln > my_tree.newick

Then we can plot the Roary results with roary_plots.py, a community contributed python script to visualise roary results:

wget https://raw.githubusercontent.com/sanger-pathogens/Roary/master/contrib/roary_plots/roary_plots.py
module load system/python/3.8.12
pip install seaborn
python roary_plots.py
python roary_plots.py my_tree.newick gene_presence_absence.csv

then look at the 3 /png files that have been generated