Rapid and accurate SNP genotyping of clonal bacterial pathogens with BioHansel
Authors: Labbé Geneviève , Kruczkiewicz Peter , Mabon Philip , Robertson James , Schonfeld Justin , Kein Daniel , Rankin Marisa A. , Gopez Matthew , Hole Darian , Son David , Knox Natalie , Laing Chad R. , Bessonov Kyrylo , Taboada Eduardo , Yoshida Catherine , Ziebell Kim , Nichani Anil , Johnson Roger P. , Van Domselaar Gary , Nash John H.E. 
Published: January 2020

BioHansel performs high-resolution genotyping of bacterial isolates by identifying phylogenetically informative single nucleotide polymorphisms (SNPs), also known as canonical SNPs, in whole genome sequencing (WGS) data. The application uses a fastk-mer matching algorithm to map pathogen WGS data to canonical SNPs contained in hierarchically structured schemas and assigns genotypes based on the detected SNP profile. Using modest computing resources, BioHansel efficiently types isolates from raw sequence reads or assembled contigs in a matter of seconds, making it attractive for use by public health, food safety, environmental, and agricultural authorities that wish to apply WGS methodologies for their surveillance, diagnostics, and research programs. BioHansel currently provides canonical SNP genotyping schemas for four prevalentSalmonellaserovars—Typhi, Typhimurium, Enteritidis and Heidelberg—as well as a schema forMycobacterium tuberculosis. Users can also supply their own schemas for genotyping other organisms. BioHansel’s quality assurance system assesses the validity of the genotyping results and can identify low quality data, contaminated datasets, and misidentified organisms. BioHansel is targeted to support surveillance, source attribution, risk assessment, diagnostics, and rapid screening for public health purposes, such as product recalls. BioHansel is an open source application with packages available for PyPI, Conda, and the Galaxy workflow manager. In summary, BioHansel performs efficient, rapid, accurate, and high-resolution classification of bacterial genomes from sequence reads or assembled contigs on standard computing hardware. BioHansel is suitable for use as a general research tool as well as in fully operationalized WGS workflows at the front lines of infectious disease surveillance, diagnostics, and outbreak investigation and response.

Impact statement

Public health, food safety, environmental, and agricultural authorities are currently engaged in a global effort to incorporate whole genome sequencing technologies into their infectious disease research, surveillance, and outbreak investigation programs. Its widespread adoption, however, has been impeded by two major obstacles: the need for high performance computing to generate results and the expert knowledge required to interpret and communicate those results. BioHansel addresses these limitations by rapidly genotyping pathogens from whole genome sequence data in an accurate, simple, familiar, and easily sharable manner using standard computing resources. BioHansel provides a compact and readily interpretable genotype based on canonical SNP genotyping schemas. BioHansel’s genotyping nomenclature encodes the pathogen’s position in its population structure, which simplifies and facilitates its comparison with actively circulating strains and historical strains. The genotyping information provided by BioHansel can identify points of intervention to prevent the spread of pathogenic bacteria, screen for the presence of priority pathogens, and perform source attribution and risk assessment. Thus, BioHansel serves as a readily accessible and powerful WGS method, implementable on a laptop, for genotyping pathogens to detect, monitor, and control the emergence and spread of infectious disease through surveillance, screening, diagnostics, and outbreak investigation and response activities.

Data summary

BioHansel is a Python 3 application available as PyPI, Conda Galaxy Tool Shed packages. It is an open source application distributed under the Apache License, Version 2.0. Source code is available athttps://github.com/phac-nml/biohansel. The BioHansel user guide is available athttps://bio-hansel.readthedocs.io/en/readthedocs/. Supplementary Materials are available athttps://github.com/phac-nml/biohansel-manuscript-supplementary-data.

The authors confirm all supporting data, code and protocols have been provided within the article or through supplementary data files.