Large scale automated phylogenomical analysis of bacterial whole-genome isolates and the Evergreen platform

Abstract

9Public health authorities whole-genome sequence thousands of pathogenic isolates each month for microbial 10 diagnostics and surveillance of pathogenic bacteria. The computational methods have not kept up with the 11 deluge of data and need for real-time results. 12We have therefore created a bioinformatics pipeline for rapid subtyping and continuous phylogenomic analysis 13 of bacterial samples, suited for large-scale surveillance. To decrease the computational burden, a two-level 14clustering strategy is employed. The data is first divided into sets by matching each isolate to a closely related 15 reference genome. The reads then are aligned to the reference to gain a consensus sequence and SNP based 16 genetic distance is calculated between the sequences in each set. Isolates are clustered together with a 17 threshold of 10 SNPs. Finally, phylogenetic trees are inferred from the non-redundant sequences and the 18 clustered isolates are placed on a clade with the cluster representative sequence. The method was 19 benchmarked and found to be accurate in grouping outbreak strains together, while discriminating from non-20 outbreak strains. 21The pipeline was applied in Evergreen Online, which processes publicly available sequencing data from 22foodborne bacterial pathogens on a daily basis, updating the phylogenetic trees as needed. It has so far placed 23 more than 100,000 isolates into phylogenies, and has been able to keep up with the daily release of data. The 24 trees are continuously published on https://cge.cbs.dtu.dk/services/Evergreen 25 26 Keywords 27 Phylogenomics, WGS, subtyping, SNP, automation, epidemiology, outbreak investigation 28 29 30 31