Abstract
Defining the population structure of a pathogen is a key part of epidemiology, as genomically related isolates are likely to share key clinical features such as antimicrobial resistance profiles and invasiveness. Multiple different methods are currently used to cluster together closely related genomes, potentially leading to inconsistency between studies. Here, we use a global dataset of 26 306 Streptococcus pneumoniae genomes to compare four clustering methods: gene-by-gene seven-locus MLST, core genome MLST (cgMLST)-based hierarchical clustering (HierCC) assignments, life identification number (LIN) barcoding and k-mer-based PopPUNK clustering (known as GPSCs in this species). We compare the clustering results with phylogenetic and pan-genome analyses to assess their relationship with genome diversity and evolution, as we would expect a good clustering method to form a single monophyletic cluster that has high within-cluster similarity of genomic content. We show that the four methods are generally able to accurately reflect the population structure based on these metrics and that the methods were broadly consistent with each other. We investigated further to study the discrepancies in clusters. The greatest concordance was seen between LIN barcoding and HierCC (adjusted mutual information score=0.950), which was expected given that both methods utilize cgMLST, but have different methods for defining an individual cluster and different core genome schema. However, the existence of differences between the two methods shows that the selection of a core genome schema can introduce inconsistencies between studies. GPSC and HierCC assignments were also highly concordant (AMI=0.946), showing that k-mer-based methods which use the whole genome and do not require the careful selection of a core genome schema are just as effective at representing the population structure. Additionally, where there were differences in clustering between these methods, this could be explained by differences in the accessory genome that were not identified in cgMLST. We conclude that for S. pneumoniae, standardized and stable nomenclature is important as the number of genomes available expands. Furthermore, the research community should transition away from seven-locus MLST, whilst cgMLST, GPSC and LIN assignments should be used more widely. However, to allow for easy comparison between studies and to make previous literature relevant, the reporting of multiple clustering names should be standardized within the research.
Original language | English |
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Article number | 001278 |
Journal | Microbial Genomics |
Volume | 10 |
Issue number | 8 |
Early online date | 28 Aug 2024 |
DOIs | |
Publication status | Published - 31 Aug 2024 |
Data Availability Statement
Genome sequences are deposited in the European Nucleotide Archive, and accession numbers can be found in the supplementary data file (Data S1, avilable in the online version of this article). Metadata of the pneumococcal isolates in this study have been submitted as a supplementary file (Data S1, available in the online version of this article) and are also available on the Monocle Database available at https://data.monocle.sanger.ac.uk/. The authors confirm that all supporting data, code and protocols have been provided within the article or through supplementary data files.Keywords
- epidemiology
- MLST
- pneumococcal genomics
- population structure
- Streptococcus pneumoniae
- whole genome sequencing
ASJC Scopus subject areas
- Epidemiology
- Microbiology
- Molecular Biology
- Genetics