Microbial strains that belong to the same species can be extremely close at the genomic level, yet still display meaningful biological, technological or epidemiological differences. To compare such closely related strains, researchers often need a high-resolution genomic approach.
SNP analysis, based on the detection of single nucleotide polymorphisms, is one of the most powerful methods for comparing closely related microbial genomes. It can support strain typing, phylogenetic reconstruction, traceability, stability monitoring and industrial strain characterisation.
A SNP, or single nucleotide polymorphism, is a variation affecting a single nucleotide position in a genome. For example, one strain may carry an adenine at a given genomic position, while another strain carries a guanine at the same position.
Although each individual SNP represents a very small difference, the combination of many SNPs across a genome can provide a precise signature of strain relatedness. This makes SNP analysis particularly useful when comparing isolates from the same species, lineage or industrial strain collection.
SNP analysis generally starts with sequencing data from several microbial isolates. Reads are aligned against a reference genome, or genomes are assembled and compared to identify nucleotide differences. Bioinformatics filters are then applied to remove low-quality variants, ambiguous positions and regions that may generate unreliable results.
The resulting set of high-confidence SNPs can be used to calculate genetic distances, build phylogenetic trees, identify strain clusters or detect mutations associated with phenotypes of interest.
SNP analysis provides a high-resolution view of microbial diversity. It is particularly useful when strains are too closely related to be distinguished by broader methods such as species identification, marker sequencing or gene presence and absence analysis.
In microbial genomics, SNP analysis can help determine whether isolates are genetically identical, closely related or clearly distinct. This information can be important for strain authentication, contamination investigation, production monitoring or comparison of proprietary strains with public genomes.
In industrial microbiology, SNP analysis can support the monitoring of production strains over time. During repeated subculturing, scale-up, fermentation or exposure to stress, microbial strains may accumulate mutations. Some of these mutations may be neutral, while others may influence performance, stability or safety.
SNP analysis can also help compare candidate strains, investigate unexpected changes in phenotype, document genetic stability or support intellectual property strategies based on genomic characterisation.
SNPs are frequently used to reconstruct phylogenetic relationships between closely related microbial isolates. A phylogenetic tree based on high-quality SNPs can reveal clusters, lineages and evolutionary distances that are not visible from species-level identification alone.
This approach is widely used in microbial epidemiology, food safety, environmental microbiology, veterinary microbiology and industrial strain management.
SNP analysis is one component of comparative genomics. It complements pangenome analysis, core genome analysis, accessory genome analysis and synteny analysis. While pangenome analysis focuses on gene presence and absence, SNP analysis focuses on nucleotide-level variation.
Together, these approaches provide a stronger understanding of microbial diversity, strain relatedness and genome evolution.
For a broader introduction, read our article on comparative genomics for industrial strain selection.
Biomanda provides bioinformatics services for microbial genomics and comparative genomics. Depending on the project, Biomanda can support sequencing data processing, read alignment, variant calling, SNP filtering, phylogenetic analysis, strain clustering and biological interpretation.
The objective is to transform sequencing data into reliable information that can support strain comparison, genomic traceability, R&D decisions, industrial monitoring and microbial characterisation.
SNP analysis is a high-resolution method for comparing closely related microbial strains. By identifying nucleotide-level differences across genomes, it helps distinguish strains, reconstruct their relationships and monitor genomic evolution.
For industrial microbiology, fermentation, food safety and applied biotechnology, SNP analysis provides an essential tool for strain comparison, traceability and genomic stability assessment.