Nov 07, 2022
Phylogenetic classification of bacterial populations using a combined analysis of core SNPs and the presence/absence of accessory genes
- Jared Johnson1,
- Chris Curtin1,
- Joy Waite-Cusic1
- 1Oregon State University
External link: https://github.com/johnjare/panaroo_scripts
Protocol Citation: Jared Johnson, Chris Curtin, Joy Waite-Cusic 2022. Phylogenetic classification of bacterial populations using a combined analysis of core SNPs and the presence/absence of accessory genes. protocols.io https://dx.doi.org/10.17504/protocols.io.x54v9y13mg3e/v1
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
We use this protocol and it's working
Created: April 24, 2022
Last Modified: November 07, 2022
Protocol Integer ID: 61322
Keywords: phylogenetics, bacterial populations, pangenome, accessory genome, SNPs
Funders Acknowledgement:
Western Dairy Center BUILD Dairy Program
Abstract
Whole genome sequencing-based typing methods have become the gold standard for phylogenetic classifications of bacterial populations. The most common approach is to compare single nucleotide polymorphisms (SNPs) across the core genome of a cohort of isolates. Greater confidence in SNP-based population assignments can be gained by extending this analysis to include the accessory genome. This is often accomplished by comparing allelic differences between isolates using whole genome multi-locus sequence typing (wgMLST). Unfortunately, allele-based methods, like wgMLST, suffer from being highly species-specific, therefore limiting their current application to only commonly studied bacteria. This work aimed to address this limitation by developing an alternative population classification method based on the accessory genome that could be more universally applied, regardless of the bacterial species being studied. This was accomplished by comparing population assignments based on core SNPs to those derived from the phylogenetic analysis of gene presence/absence matrices output by common pangenome assembly software (i.e., Panaroo or Roary).
Before start
This protocol assumes you have the following software installed:
Required Software:
- RAxML (https://cme.h-its.org/exelixis/web/software/raxml/)
Optional Software:
Downloading required scripts
Downloading required scripts
Genome assembly and quality check:
Genome assembly and quality check:
Assemble bacterial genomes using Unicycler (https://github.com/rrwick/Unicycler). The standard input for Illumina-only assemblies is shown.
unicycler -1 short_reads_1.fastq.gz -2 short_reads_2.fastq.gz -o output_dir
Check the quality of the assembled genomes and any reference genomes included in the analysis using BUSCO (https://busco.ezlab.org) and Quast (http://quast.sourceforge.net). Standard inputs are shown. This step is optional.
busco -i assembly.fasta -o output_dir -m genome -l reference_id
quast.py -i assembly_directory/*.fasta -o output_directory -r reference.fasta
Reject assemblies with large numbers of missing, fragmented, or duplicated (MFD) BUSCOs (e.g., assemblies with >1% MFD BUSCOs) and/or those with estimated genome sizes three standard deviations from the mean of high-quality reference genomes for that species.
Annotate assemblies
Annotate assemblies
Annotate all genome assemblies, including reference genomes if included in your analysis, using Prokka (https://github.com/tseemann/prokka). The standard input is shown below.
prokka --kingdom Bacteria --outdir output_directory --genus bacterial_genus --locustag isolate_id assembly.fasta
Move the resulting .gff annotation files to a single directory.
Pangenome assembly and quality check
Pangenome assembly and quality check
Assemble the pangenome using Panaroo (https://github.com/gtonkinhill/panaroo). The standard input with core gene alignment using MAFFT is shown.
panaroo -i gff_directory/*.gff -o output_directory -a core --aligner mafft
Check for any problematic genome assemblies in the pangenome using pan_check.R (https://github.com/johnjare/panaroo_scripts/tree/main/pan_check). This script will output a heatmap like shown in Figure 1. Notice that isolate_10 in Figure 1 is missing several genes shared by all other isolates, probably indicating poor assembly quality.
Rscript pan_check.R gene_presence_absence.Rtab
Figure 1. Example output from the ‘pan_check.R’ script.
If necessary, re-assemble the pangenome after removing any problematic genome assemblies identified in the previous step.
Extract SNPs and accessory genes
Extract SNPs and accessory genes
Extract single nucleotide polymorphisms (SNPs) from the core gene alignment using SNP-sites (https://github.com/sanger-pathogens/snp-sites). The standard inputs for the creation of an alignment (.aln) file and variant call format (.vcf) file are shown. The “-c” flag excludes gaps.
snp-sites -c core_gene_alignment.aln > core_snps.aln
snp-sites -c -v core_gene_alignment.aln > core_snps.vcf
Extract accessory genes using the accessory_binary_genes.py (https://github.com/johnjare/panaroo_scripts/tree/main/accessory_binary_genes). This script requires that Pandas is installed (https://pandas.pydata.org). The standard input for this command is shown. The output has a similar structure to a FASTA file but contains 1’s and 0’s in places of ACGT’s.
python3 accessory_binary_genes.py -i gene_presence_absence.Rtab -o accessory_genes.binary
Create phylogenetic trees
Create phylogenetic trees
Create phylogenetic trees from the “accessory_genes.binary" and "core_snps.aln" files generated in the previous step using RAXML. The standard input using the BINCAT and GTRCAT models are shown. Adjust as necessary based on your available resources, preferred seed numbers, and number of bootstraps. The command for rooting the tree is also shown, though this step is optional.
Accessory genome:
raxml -s accessory_genes.bin -n output_file_name -m BINCAT -f a -T 2 -x 11 -N 100 -p 11
raxml -T 3 -f I -m GTRCAT -t input_file -n output_file_name
SNPs:
raxml -s core_snps.aln -n output_file_name -m GTRCAT -f a -T 2 -x 11 -N 100 -p 11
raxml -T 3 -f I -m GTRCAT -t input_file -n output_file_name
Calculating pairwise SNP and gene differences
Calculating pairwise SNP and gene differences
Calculate pairwise gene and SNP differences using the pair_diff.py (https://github.com/johnjare/panaroo_scripts/tree/main/pair_diff). The standard input is shown.
python3 pair_diff.py -g gene_presence_absence.Rtab -s core_snps.vcf -o gene_snp_differences.csv