Conducting comparative genomics with 11 strains, including your main strain, is feasible and can provide valuable insights into the core, accessory, and unique genes of the pangenome. However, the resolution and accuracy of the results will depend on factors such as the diversity of the strains selected, the quality of the genome assemblies, and the bioinformatics tools used for the analysis.

However, it's important to note that the number of strains used in the analysis can also affect the resolution and accuracy of the pangenome. Increasing the number of strains can help capture more diversity and provide a more comprehensive view of the pangenome. Therefore, while using RefSeq sequences can improve the quality of the analysis, increasing the number of strains beyond 11, if feasible, can further enhance the insights into the core, accessory, and unique genes of the pangenome.

Here, how you can you perform this analysis:

Methodology 1: Using BacWGSTdb, you can directly perform epidemiological outbreak analysis of your strain with other publicly available reference sequence of your strain’s species at global level. BacWGSTdb offred two type of analysis i.e. single genome analysis and multiple genome analysis (in case you sequence multiple samples).

Methodology 2: First download all available reference sequence of your strain’s species from NCBI database. Then perform pairwise genome distance using Mash. Then, selects top >10 closest non-duplicate genomes with similarities ≥99.5% for further downstream analysis. You can also collect the source of isolation, country, host etc. type information of the closely related strains from NCBI and BV-BRC databases. Then, you can perform following downstream analysis of your strain and its closest strains:

1. Average Nucleotide Identity (ANI)

2. Multi-Locus Sequence Typing (MLST; https://github.com/tseemann/mlst)

3. Serotyping (K-Locus and O-Locus; https://kaptive-web.erc.monash.edu)

4. Pangenome analysis using Roary pipeline.

5. Genome-wide phylogenetic analysisbased on the core single nucleotide polymorphisms (SNPs) of single-copy core genes, using the best-fitted model of evolution.

6. Visualization and annotation of minimum-spanning tree with bootstrap numbers using the online iTOL v6 web server (https://itol.embl.de).

References

1. Sahoo, R. K., Gaur, M., Dey, S., Sahoo, S., Das, A., & Subudhi, E. (2023). Genomic insight of extremely drug-resistant Klebsiella pneumoniae ST5378 from a paediatric bloodstream infection. Journal of Global Antimicrobial Resistance, 33, 227–230. https://doi.org/10.1016/j.jgar.2023.04.002.

2. Dey, S.; Gaur, M.; Sykes, E.M.E.; Prusty, M.; Elangovan, S.; Dixit, S.; Pati, S.; Kumar, A.; Subudhi, E. Unravelling the Evolutionary Dynamics of High-Risk Klebsiella pneumoniae ST147 Clones: Insights from Comparative Pangenome Analysis. Genes 2023, 14, 1037. https://doi.org/10.3390/genes14051037.