BMScan: using whole genome similarity to rapidly and accurately identify bacterial meningitis causing species

• DOI: 10.1186/s12879-018-3324-1
• Publication Date: 2018-08-15T08:20:56Z
• Authors: Nadav Topaz; Dave Boxrud; Adam C. Retchless; Megan Nichols; How-Yi Chang; Fang Hu; Xin Wang
• Keywords: Bacterial genome size; Neonatal meningitis; RefSeq; Identification

Bacterial meningitis is a life-threatening infection that remains public health concern. commonly caused by the following species: Neisseria meningitidis, Streptococcus pneumoniae, Listeria monocytogenes, Haemophilus influenzae and Escherichia coli. Here, we describe BMScan (Bacterial Meningitis Scan), whole-genome analysis tool for species identification of bacterial meningitis-causing closely-related pathogens, an essential step case management disease surveillance. relies on reference collection contains genomes 17 focal to scan against identify given species. We established this supplementing publically available from RefSeq with isolate collections Centers Disease Control Laboratory Minnesota Department Health Public Laboratory, then filtered them down representative set which capture diversity each Using collection, evaluated two genomic comparison algorithms, Mash Average Nucleotide Identity, their ability accurately rapidly our species.We found results were strongly correlated ANI identification, while providing significant reduction in run-time. This drastic difference run-time enabled rapid scanning large genome collections, which, when combined species-specific threshold values, facilitated development BMScan. validation 15,503 interest, identified 99.97% within 16 min 47 s.Identification pathogenic critical confirmation further strain characterization. employs thresholds previously-validated, genome-wide similarity statistics compiled curated uncharacterized pathogens closely related pathogens. will facilitate transition laboratories traditional phenotypic detection methods whole sequencing based identification.