Comparing microbial strains requires methods that can distinguish closely related isolates with sufficient resolution. Two widely used approaches are cgMLST and SNP analysis. Both are based on genome sequencing, but they do not analyse genomic variation in the same way.
Understanding the differences between cgMLST and SNP analysis is important when choosing a strategy for strain typing, phylogeny, traceability, genomic surveillance or industrial strain comparison.
cgMLST stands for core genome multilocus sequence typing. It extends the principle of classical MLST by analysing hundreds or thousands of genes that belong to the core genome of a species or defined microbial group.
For each gene included in the cgMLST scheme, different sequence variants are assigned allele numbers. Strains can then be compared according to their allelic profiles. This makes cgMLST a structured and standardised approach for comparing isolates.
SNP analysis compares genomes at the nucleotide level by identifying single nucleotide polymorphisms. These are individual base differences observed between genomes or between sequencing reads and a reference genome.
SNP analysis can provide very high resolution, especially when comparing closely related strains. It is often used for phylogenetic reconstruction, outbreak investigation, strain stability monitoring and fine-scale genomic comparison.
The main difference is the unit of comparison. cgMLST compares allele profiles across conserved genes, while SNP analysis compares individual nucleotide positions across genomes.
cgMLST is generally easier to standardise across laboratories because results are expressed as allele numbers within a predefined scheme. SNP analysis can offer higher resolution, but it is more sensitive to the choice of reference genome, variant filtering strategy, genome quality and bioinformatics pipeline.
cgMLST is useful when results need to be comparable across studies, databases or laboratories. Because it relies on predefined loci and allele calling, it provides a stable framework for microbial typing and genomic surveillance.
This approach is particularly valuable for epidemiology, food safety, public health surveillance and any context where standardised strain comparison is needed.
SNP analysis is highly informative when the objective is to compare very closely related strains with maximum resolution. It can detect small genetic differences that may not be captured by allelic typing alone.
In industrial microbiology, SNP analysis can be useful for monitoring strain stability, identifying microevolution during passages, comparing production batches or investigating unexpected phenotypic changes.
cgMLST depends on the quality and relevance of the scheme used. If the scheme does not cover the diversity of the analysed strains, interpretation can be limited. It also focuses on core genes and may not capture important differences in the accessory genome.
SNP analysis requires careful bioinformatics filtering and interpretation. Recombination, low-quality sequencing, contaminated assemblies or an unsuitable reference genome can affect the reliability of results.
The best approach depends on the question. cgMLST is well suited for standardised typing, database comparison and routine surveillance. SNP analysis is better suited for high-resolution comparison, fine phylogeny, strain stability and detailed investigation of closely related isolates.
In many projects, both approaches can be complementary. cgMLST can provide a structured overview, while SNP analysis can refine the comparison when a higher level of resolution is needed.
For industrial microbiology, cgMLST and SNP analysis can support strain authentication, traceability, stability monitoring, contamination investigation and strain portfolio management.
When combined with pangenome analysis, synteny analysis and genome annotation, these approaches provide a more complete understanding of microbial strain diversity and genomic evolution.
Both cgMLST and SNP analysis are part of the broader field of comparative genomics. They focus mainly on conserved genomic regions and nucleotide-level variation, while other approaches such as pangenome analysis and accessory genome comparison explore differences in gene content.
For related concepts, read our articles on comparative genomics for industrial strain selection, SNP analysis for microbial strain comparison and core genome vs accessory genome.
Biomanda provides bioinformatics services for microbial genomics and comparative genomics. Depending on the project, Biomanda can support genome assembly, annotation, cgMLST analysis, SNP calling, variant filtering, phylogenetic reconstruction, strain clustering and biological interpretation.
The objective is to help companies and research teams choose the most appropriate genomic strategy for strain comparison, traceability, industrial monitoring and R&D decision-making.
cgMLST and SNP analysis are both powerful approaches for microbial strain comparison. cgMLST provides a standardised and reproducible framework, while SNP analysis offers high-resolution nucleotide-level comparison.
The choice between them depends on the objective of the project. For many microbial genomics studies, combining both approaches with broader comparative genomics can provide the most informative result.