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With increasing rates of antibiotic resistance, bacterial infections have become more difficult to treat, elevating the importance of surveillance and prevention. Effective surveillance relies on the availability of rapid, cost-effective, and informative typing methods to monitor bacterial isolates. PCR-based typing assays are fast and inexpensive, but their utility is limited by the lack of targets which are capable of distinguishing between strains within a species. To identify highly informative PCR targets from the growing base of publicly available bacterial genome sequences, we developed pan-PCR. This computer algorithm uses existing genome sequences for isolates of a species of interest and identifies a set of genes whose patterns of presence or absence provide the best discrimination between strains in this species. A set of PCR primers targeting the identified genes is then designed, with each PCR product being of a different size to allow multiplexing. These target DNA regions and PCR primers can then be utilized to type bacterial isolates. To evaluate pan-PCR, we designed an assay for the emerging pathogen Acinetobacter baumannii. Taking as input a set of 29 previously sequenced genomes, pan-PCR identified 6 genetic loci whose presence or absence was capable of distinguishing all the input strains. This assay was applied to a set of patient isolates, and its discriminatory power was compared to that of multilocus sequence typing (MLST) and whole-genome optical maps. We found that the pan-PCR assay was capable of making clinically relevant distinctions between strains with identical MLST profiles and showed a discriminatory power similar to that of optical maps. Pan-PCR represents a tool capable of exploiting available genome sequence data to design highly discriminatory PCR assays. The ease of design and implementation makes this approach feasible for diagnostic facilities of all sizes.

Pan-PCR, a Computational Method for Designing Bacterium-Typing Assays Based on Whole-Genome Sequence Data