Selecting the best microbial strain is one of the most important decisions in a fermentation project. A strain can influence productivity, flavour, texture, stability, safety, robustness and the reproducibility of the final product.
Traditional strain selection relies on phenotypic tests and process evaluation. Genomics adds a complementary layer by helping understand why strains differ and by identifying genetic features that may support industrial performance.
The best strain is not universal. It depends on the objective of the fermentation process. A strain may be selected for acidification, aroma production, ethanol tolerance, enzyme production, probiotic potential, inhibition of contaminants, stress resistance or compatibility with other microorganisms.
Before comparing strains, it is therefore essential to define the expected technological traits and the constraints of the industrial process.
Phenotypic assays remain essential because they measure the actual behaviour of strains under experimental or industrial conditions. However, phenotypes can be difficult to interpret without genomic information.
Genome sequencing and comparative genomics can help explain observed differences by identifying genes, mutations, plasmids, metabolic pathways or genomic regions associated with strain-specific properties.
Industrial fermentation often exposes microorganisms to stress: temperature variation, acidity, osmotic pressure, ethanol, oxygen limitation, nutrient limitation or competition with other microorganisms.
Comparative genomics can help identify genomic features linked to stress response, membrane transport, carbohydrate metabolism, repair systems or adaptation to specific environments. These features can help prioritise strains for further testing.
A promising strain must remain stable during storage, propagation, scale-up and production. Genetic drift, SNP accumulation, plasmid loss or genomic rearrangements may affect performance over time.
Genomic comparison between early and later passages, or between production batches, can help evaluate whether a strain remains stable and suitable for industrial development.
Strain selection should also consider safety and undesirable genomic features. Depending on the application, this may include screening for virulence factors, antibiotic resistance genes, toxin-related genes, mobile genetic elements or other risk-associated markers.
Genomic screening does not replace regulatory evaluation, but it can provide important information during the early selection and documentation of candidate strains.
When several candidate strains are available, comparative genomics can help rank them according to genomic similarity, gene content, accessory genome features, SNP profiles, plasmid content or markers associated with desired traits.
This approach helps move from simple strain identification to informed strain prioritisation, reducing uncertainty before investing in scale-up or validation studies.
For industrial fermentation, relying on a single strain can be risky. Building a strain portfolio with complementary properties can improve flexibility, resilience and innovation capacity.
Genomics can help organise such portfolios by identifying related strains, unique genomic features, functional diversity and potential alternatives for future development.
Biomanda provides bioinformatics services for microbial genomics, comparative genomics and molecular biology. Depending on the project, Biomanda can support genome assembly, annotation, SNP analysis, pangenome analysis, synteny analysis, strain stability assessment, safety marker screening and primer or probe design.
The objective is to help companies transform sequencing data into practical information for microbial strain selection, fermentation R&D, industrial development and strain portfolio management.
Selecting the best microbial strain for fermentation requires combining biological testing, process knowledge and genomic characterisation. Genomics helps explain strain differences, assess stability, identify useful markers and reduce uncertainty during R&D.
For industrial fermentation, this integrated approach supports better strain selection and more robust microbial development strategies.