Phylogeny informed international clone assignment with PhyloMLST
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Multilocus sequence typing (MLST) remains the predominant method for typing bacterial strains. A common method for investigating particularly successful epidemic lineages within a species is to cluster isolates with similar MLST profiles into clonal complexes (CCs). Some CCs, such as international clones (ICs) in A. baumannii , are identified with specific sequence types (STs) and are of particular importance to human health. Although theoretically simple, there is a lack of convenient, user-friendly tools perform this analysis. Here we present PhyloMLST, a tool to cluster bacterial isolates into CCs and map them to ICs using the output from existing MLST tools and user-provided STs. As there is potential for aberrant IC assignment arising from excessively large CCs constructed with spurious links, PhyloMLST provides additional functionality to correct IC assignment with a user-provided phylogenetic tree. Although designed with A. baumannii in mind, PhyloMLST can be applied to any bacteria where construction and investigation of CCs based on MLST is performed.
Impact statement
Many bacterial pathogens are characterised by successful epidemic lineages that are responsible for a substantial number of infections, may be more virulent, and may carry an abundance of antimicrobial resistance genes. These epidemic lineages are comprised of a number of multilocus sequence typing (MLST) sequence types (STs), clustered into clonal complexes (CCs). To date, identifying which STs belong to which epidemic lineage has been challenging, with no simple analytical tools available. Here, we present PhyloMLST - a phylogenetically-aware method for assigning STs to epidemic lineages. The customisable nature of the tool will enable researchers to straightforwardly characterise any population of bacteria that they are working on using MLST data and user-defined definitions of epidemic lineages.
Data summary
The PhyloMLST source code and example data shown here is available at https://github.com/Mattn286/PhyloMLST .
