RPinecone: Define sub-lineages of a clonal expansion via a phylogenetic tree. Issue 4 (28th March 2019)

• Record Type: Journal Article
• Authors: Wailan, Alexander M.; Coll, Francesc; Heinz, Eva; Tonkin-Hill, Gerry; Corander, Jukka; Feasey, Nicholas A.; Thomson, Nicholas R.
• Is Part Of: Microbial genomics. Volume 5:Issue 4(2019)
• Publication Date: 2019-03-28
• Subjects: R -- epidemiology -- phylogenetic tree -- lineage definition -- methicillin-resistant Staphylococcus aureus -- Salmonella Typhi; Microbial genomics -- Periodicals; 572.8629
• DOI: 10.1099/mgen.0.000264
• Languages: English
• ISSNs: 2057-5858

Abstract

Abstract : The ability to distinguish different circulating pathogen clones from each other is a fundamental requirement to understand the epidemiology of infectious diseases. Phylogenetic analysis of genomic data can provide a powerful platform to identify lineages within bacterial populations, and thus inform outbreak investigation and transmission dynamics. However, resolving differences between pathogens associated with low-variant (LV) populations carrying low median pairwise single nucleotide variant (SNV) distances remains a major challenge. Here we present rPinecone, an R package designed to define sub-lineages within closely related LV populations. rPinecone uses a root-to-tip directional approach to define sub-lineages within a phylogenetic tree according to SNV distance from the ancestral node. The utility of this software was demonstrated using both simulated outbreaks and real genomic data of two LV populations: a hospital outbreak of methicillin-resistant Staphylococcus aureus and endemic Salmonella Typhi from rural Cambodia. rPinecone identified the transmission branches of the hospital outbreak and geographically confined lineages in Cambodia. Sub-lineages identified by rPinecone in both analyses were phylogenetically robust. It is anticipated that rPinecone can be used to discriminate between lineages of bacteria from LV populations where other methods fail, enabling a deeper understanding of infectious disease epidemiology for public health purposes.