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Pan-Genome Analysis¶

In this tutorial we will learn how to determine a pan-genome from a collection of isolate genomes.

This tutorial is inspired from Genome annotation and Pangenome Analysis from the CBIB in Santiago, Chile

Getting the data¶

We'll data from this article and analyse the core and accessory genomes of E. coli strains

Firstly, download the supplementary csv file containing information on all the strains using in the study.

Link

Then, open it in Excel (or any software that opens .csv files) and select 32 strains. Download these 32 strains from the ENA website!

Note

Alternatively you can use ena browser tools to download the files. It is available as a bioconda recipe.

# for getting 10 random strains at the command-line
cut -d',' -f 1 journal.pcbi.1006258.s010.csv | tail -n +2 | shuf | head -10 > strains.txt
cat strains.txt | parallel enaGroupGet -f fastq {}

if you wish to use the same strains as your instructor:

curl -O -J -L https://osf.io/s43mv/download
cat strains.txt | parallel enaGroupGet -f fastq {}

and then put all the reads in the same directory

mkdir reads
mv ERS*/*/*.fastq.gz reads/
rm -r ERS*

Assemble and Annotate the strains¶

You'll assemble your strains with megahit.

mkdir assemblies
for r1 in reads/*_1.fastq.gz
do
    prefix=$(basename $r1 _1.fastq.gz)
    r2=reads/${prefix}_2.fastq.gz
    megahit -1 $r1 -2 $r2 -o ${prefix} --out-prefix ${prefix}
    mv ${prefix}/${prefix}.contigs.fa assemblies/
    rm -r ${prefix}
done

and use prokka to annotate

mkdir annotation
for assembly in assemblies/*.fa
do
    prefix=$(basename $assembly .contigs.fa)
    prokka --usegenus --genus Escherichia --species coli --strain ${prefix} \
        --outdir ${prefix} --prefix ${prefix} ${assembly}
    mv ${prefix}/${prefix}.gff annotation/
    rm -r ${prefix}
done

Pan-genome analysis¶

put all the .gff files in the same folder (e.g., ./gff) and run Roary

roary -f roary -e -n -v annotation/*.gff

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.

Plotting the result¶

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:

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

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

wget https://raw.githubusercontent.com/sanger-pathogens/Roary/master/contrib/roary_plots/roary_plots.py
python roary_plots.py
roary_plots.py my_tree.newick gene_presence_absence.csv

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