Contigs from 10 published genomes and the 33 projects in this study were annotated using the Prokka20 prokaryotic annotation pipeline. For pan-genome analysis, the complete predicted protein coding sequences from all 43 genomes were searched against themselves using BLASTP with an e-value cutoff of 1e-05 for significance. The best BLASTP scores were used for identifying orthologous groups using the OrthoMCL algorithm21. A panmatrix using all orthologous gene was created and imported into the R-package, micropan, to generate visualizations to describe the Legionella pan-genome. MUSCLE62 was used with default settings to align genes and proteins within ortholog groups; each of the multiple sequence alignments (MSA) was filtered by GBLOCKS63 to remove gaps and highly divergent regions. Core genes are defined as the protein-coding gene clusters that have genes present from all 43 Legionella serogroups and species. All core protein coding MSA’s were concatenated to generate a super alignment for protein-based Legionella phylogeny. The whole genome nucleotide alignment was also generated using progressive MAUVE by concatenating all nucleotide sequences of each of the core genes22. This MAUVE alignment was used as input alignment for generating the whole genome nucleotide phylogeny, ClonalFrame, fineSTRUCTURE and BAPS analyses (see below). We also generated 2 separate MAUVE alignments: Lp only (intra-specific alignment; 1,028,806 bp); and all Legionella species (interspecific alignment; 759,392 bp) for a second version of ClonalFrame analysis (see below). The single nucleotide polymorphisms (SNPs) were extracted from the MAUVE core alignment based on reference strain Lp1 strain Philidelphia, and the core SNP positions were combined to prepare the genome-wide haplotype data. For all accessory gene clusters with at least 3 Legionella genomes present, MUSCLE MSAs were trimmed by GBLOCKS to remove gaps.