Optimal Representative Strain selector-a comprehensive pipeline for selecting next-generation reference strains of bacterial species

Abstract

Although it is common practice to use historically established 'reference strains' or 'type strains' for laboratory experiments, this approach often overlooks how effectively these strains represent the full ecological, genetic and functional diversity of the species within a specific ecological niche. In this context, this study proposes the Optimal Representative Strain (ORS) selector tool (https://zenodo.org/doi/10.5281/zenodo.13772191), an innovative bioinformatic pipeline capable of evaluating how a strain represents its whole species from a genetic and functional perspective, in addition to considering its ecological distribution in a particular ecological niche. Based on publicly available genomes, the strain that best fits all these three microbiological aspects is designated as an optimal representative strain. Moreover, a user-friendly software called Local Alternative Optimal Representative Strain selector was developed to allow researchers to screen their local library of bacterial strains for an optimal available alternative based on the reference optimal representative strain. Five different bacterial species, i.e. Lacticaseibacillus paracasei, Lactobacillus delbrueckii, Streptococcus thermophilus, Bacteroides thetaiotaomicron and Lactococcus lactis, were tested in three different environments to evaluate the performance of the bioinformatic pipeline in selecting optimal representative strains.

Figure 1.

The graph illustrates the workflow for the establishment of a genomic database, comprehensive of all the summarized steps required, from the study design to the definition of genomic and environmental databases. The software tools utilized are indicated alongside their corresponding steps in the workflow, and each arrow represents a different processing step (procedural).

Figure 2.

The graph illustrates the workflow of the two different pipelines followed to retrieve ecological (green) and genomic (orange) scores required to define the representative score. The software tools utilized are indicated alongside their corresponding steps in the workflow, and each arrow represents a different processing step (procedural).

Figure 3.

The graph illustrates the workflow followed to retrieve the functional (dark red) score required to define the representative score. The software tools utilized are indicated alongside their corresponding steps in the workflow, and each arrow represents a different processing step (procedural).

Figure 4.

Results of the ORS selector pipeline obtained for the test dataset. The distribution of the five studied bacterial species’ acquired representative score values is displayed in a graph. Each dot reports the score of a genome in each of the three ecological niches. Each graph reports the unique code (GCA or GCF code) of the strain with the highest representative score for each ecological niche. The order of genomes is according to the environment with the highest score.

Figure 5.

The image depicts the procedure used to retrieve the local alternative optimal representative strain to be used as a substitute for the closest optimal representative strain. The software tools utilized are indicated alongside their corresponding steps in the workflow, and each arrow represents a distinct processing step (procedural).