The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online ( http://bioinf.spbau.ru/spades ). It is distributed as open source software.

SPAdes, a new genome assembly algorithm and its applications to single-cell sequencing ( 7th Annual SFAF Meeting, 2012)

This Opinion considers the application of whole genome sequencing (WGS) and metagenomics for outbreak investigation, source attribution and risk assessment of food-borne pathogens. WGS offers the highest level of bacterial strain discrimination for food-borne outbreak investigation and source-attribution as well as potential for more precise hazard identification, thereby facilitating more targeted risk assessment and risk management. WGS improves linking of sporadic cases associated with different food products and geographical regions to a point source outbreak and can facilitate epidemiological investigations, allowing also the use of previously sequenced genomes. Source attribution may be favoured by improved identification of transmission pathways, through the integration of spatial-temporal factors and the detection of multidirectional transmission and pathogen-host interactions. Metagenomics has potential, especially in relation to the detection and characterisation of non-culturable, difficult-to-culture or slow-growing microorganisms, for tracking of hazard-related genetic determinants and the dynamic evaluation of the composition and functionality of complex microbial communities. A SWOT analysis is provided on the use of WGS and metagenomics for Salmonella and Shigatoxin-producing Escherichia coli (STEC) serotyping and the identification of antimicrobial resistance determinants in bacteria. Close agreement between phenotypic and WGS-based genotyping data has been observed. WGS provides additional information on the nature and localisation of antimicrobial resistance determinants and on their dissemination potential by horizontal gene transfer, as well as on genes relating to virulence and biological fitness. Interoperable data will play a major role in the future use of WGS and metagenomic data. Capacity building based on harmonised, quality controlled operational systems within European laboratories and worldwide is essential for the investigation of cross-border outbreaks and for the development of international standardised risk assessments of food-borne microorganisms.

/pdf/whole-genome-sequencing-and-metagenomics-for-outbreak-2elx1fnxbe.pdf

Whole genome sequencing and metagenomics for outbreak investigation, source attribution and risk assessment of food-borne microorganisms

Whole genome sequencing (WGS) of foodborne pathogens has become an effective method for investigating the information contained in the genome sequence of bacterial pathogens. In addition, its highly discriminative power enables the comparison of genetic relatedness between bacteria even on a sub-species level. For this reason, WGS is being implemented worldwide and across sectors (human, veterinary, food, and environment) for the investigation of disease outbreaks, source attribution, and improved risk characterization models. In order to extract relevant information from the large quantity and complex data produced by WGS, a host of bioinformatics tools has been developed, allowing users to analyze and interpret sequencing data, starting from simple gene-searches to complex phylogenetic studies. Depending on the research question, the complexity of the dataset and their bioinformatics skill set, users can choose between a great variety of tools for the analysis of WGS data. In this review, we describe the relevant approaches for phylogenomic studies for outbreak studies and give an overview of selected tools for the characterization of foodborne pathogens based on WGS data. Despite the efforts of the last years, harmonization and standardization of typing tools are still urgently needed to allow for an easy comparison of data between laboratories, moving towards a one health worldwide surveillance system for foodborne pathogens.

/pdf/typing-methods-based-on-whole-genome-sequencing-data-3ueyepkjqf.pdf

Typing methods based on whole genome sequencing data

Waterborne Campylobacter jejuni outbreaks are common in the Nordic countries, and PFGE (pulsed field gel electrophoresis) remains the genotyping method of choice in outbreak investigations. However, PFGE cannot assess the clonal relationship between isolates, leading to difficulties in molecular epidemiological investigations. Here, we explored the applicability of whole genome sequencing to outbreak investigation by re-analysing three C. jejuni strains (one isolated from water and two from patients) from an earlier resolved Finnish waterborne outbreak from the year 2000. One of the patient strains had the same PFGE profile, as well as an identical overall gene synteny and three polymorphisms in comparison with the water strain. However, the other patient isolate, which showed only minor differences in the PFGE pattern relative to the water strain, harboured several polymorphisms as well as rearrangements in the integrated element CJIE2. We reconstructed the genealogy of these strains with ClonalFrame including in the analysis four C. jejuni isolated from chicken in 2012 having the same PFGE profile and sequence type as the outbreak strains. The three outbreak strains exhibited a paraphyletic relationship, implying that the drinking water from 2000 was probably contaminated with at least two different, but related, C. jejuni strains. Our results emphasize the capability of whole genome sequencing to unambiguously resolve the clonal relationship between isolates of C. jejuni in an outbreak situation and evaluate the diversity of the C. jejuni population.

Genome analysis of Campylobacter jejuni strains isolated from a waterborne outbreak

The holistic approach of One Health, which sees human, animal, plant, and environmental health as a unit, rather than discrete parts, requires not only interdisciplinary cooperation, but standardized methods for communicating and archiving data, enabling participants to easily share what they have learned and allow others to build upon their findings. Ongoing work by NCBI and the GenomeTrakr project illustrates how open data platforms can help meet the needs of federal and state regulators, public health laboratories, departments of agriculture, and universities. Here we describe how microbial pathogen surveillance can be transformed by having an open access database along with Best Practices for contributors to follow. First, we describe the open pathogen surveillance framework, hosted on the NCBI platform. We cover the current community standards for WGS quality, provide an SOP for assessing your own sequence quality and recommend QC thresholds for all submitters to follow. We then provide an overview of NCBI data submission along with step by step details. And finally, we provide curation guidance and an SOP for keeping your public data current within the database. These Best Practices can be models for other open data projects, thereby advancing the One Health goals of Findable, Accessible, Interoperable and Re-usable (FAIR) data.

https://link.springer.com/content/pdf/10.1186/s42522-020-00026-3.pdf

Optimizing open data to support one health: best practices to ensure interoperability of genomic data from bacterial pathogens

In response to the EFSA call “New approaches in identifying and characterizing microbial and chemical hazards”, the project INNUENDO (https://sites.google.com/site/theinnuendoproject/) aimed to design an analytical platform and standard procedures for the use of whole-genome sequencing in surveillance and outbreak investigation of food-borne pathogens. The project firstly attempted to identify existing flaws and needs, and then to provide applicable cross-sectorial solutions. The project focused in developing a platform for small countries with limited economical and personnel resources. To achieve these goals, we applied a user-centered design strategy involving the end-users, such as microbiologists in public health and veterinary authorities, in every step of the design, development and implementation phases. As a result, we delivered the INNUENDO Platform V1.0 (https://innuendo.readthedocs.io/en/latest/), a stand-alone, portable, open-source, end-to-end system for the management, analysis, and sharing of bacterial genomic data. The platform uses Nextflow workflow manager to assemble analytical software modules in species-specific protocols that can be run using a user-friendly interface. The reproducibility of the process is ensured by using Docker containers and throught the annotation of the whole process using an ontology. Several modules, available at https://github.com/TheInnuendoProject, have been developed including: genome assembly and species confirmation; fast genome clustering; in silico typing; standardized species-specific phylogenetic frameworks for Campylobacter jejuni, Yersinia enterocolitica, Salmonella enterica and Escherichia coli based on an innovative gene-by-gene methodology; quality control measures from raw reads to allele calling; reporting system; a built-in communication protocols and a strain classification system enabling smooth communication during outbreak investigation. As proofof-concepts, the proposed solutions have been thoroughly tested in simulated outbreak conditions by several public health and veterinary agencies across Europe. The results have been widely disseminated through several channels (web-sites, scientific publications, organization of workshops). The INNUENDO Platform V1.0 is effectively one of the models for the usage of open-source software in genomic epidemiology.

https://efsa.onlinelibrary.wiley.com/doi/pdf/10.2903/sp.efsa.2018.EN-1498

Jasmin Joenperä

Papers

INNUENDO: A cross-sectoral platform for the integration of genomics in the surveillance of food-borne pathogens

Incidence and characteristics of food-related criminal cases in Finland