Real-Time Pathogen Detection in the Era of Whole-Genome Sequencing and Big Data: Comparison of k-mer and Site-Based Methods for Inferring the Genetic Distances among Tens of Thousands of Salmonella Samples

• Published in: PloS one Vol. 11; no. 11; p. e0166162
• Main Authors: Pettengill, James B., Pightling, Arthur W., Baugher, Joseph D., Rand, Hugh, Strain, Errol
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 10.11.2016; Public Library of Science (PLoS)
• Subjects: Analysis; Animals; Archives & records; Bacteria; BASIC BIOLOGICAL SCIENCES; Big data; Bioinformatics; Biological evolution; Biology; Biology and Life Sciences; Computational Biology - methods; Computer applications; Data bases; Data management; Data processing; DNA sequencing; Empirical analysis; Engineering and Technology; Food contamination; Food safety; Gene sequencing; Genetic aspects; Genetic distance; Genome, Bacterial - genetics; Genomes; Genomics; Humans; Listeria; Medicine and Health Sciences; Metadata; Methods; Multilocus sequence typing; Multilocus Sequence Typing - methods; Nucleotide sequence; Nutrition; Outbreaks; Pathogens; Phylogenetics; Phylogeny; Public health; Real time; Reproducibility of Results; Research and Analysis Methods; Salmonella; Salmonella - classification; Salmonella - genetics; Salmonella - physiology; Salmonella enterica; Salmonella Infections - microbiology; Salmonellosis; Sequence Analysis, DNA - methods; Species Specificity; Time Factors
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0166162

The adoption of whole-genome sequencing within the public health realm for molecular characterization of bacterial pathogens has been followed by an increased emphasis on real-time detection of emerging outbreaks (e.g., food-borne Salmonellosis). In turn, large databases of whole-genome sequence data are being populated. These databases currently contain tens of thousands of samples and are expected to grow to hundreds of thousands within a few years. For these databases to be of optimal use one must be able to quickly interrogate them to accurately determine the genetic distances among a set of samples. Being able to do so is challenging due to both biological (evolutionary diverse samples) and computational (petabytes of sequence data) issues. We evaluated seven measures of genetic distance, which were estimated from either k-mer profiles (Jaccard, Euclidean, Manhattan, Mash Jaccard, and Mash distances) or nucleotide sites (NUCmer and an extended multi-locus sequence typing (MLST) scheme). When analyzing empirical data (whole-genome sequence data from 18,997 Salmonella isolates) there are features (e.g., genomic, assembly, and contamination) that cause distances inferred from k-mer profiles, which treat absent data as informative, to fail to accurately capture the distance between samples when compared to distances inferred from differences in nucleotide sites. Thus, site-based distances, like NUCmer and extended MLST, are superior in performance, but accessing the computing resources necessary to perform them may be challenging when analyzing large databases.