RIPS (rapid intuitive pathogen surveillance): a tool for surveillance of genome sequence data from foodborne bacterial pathogens.

Monitoring data submitted to the National Center for Biotechnology Information's Pathogen Detection whole-genome sequence database, which includes the foodborne bacterial pathogens monocytogenes, , and , has proven effective for detecting emerging outbreaks. As part of the submission process, new sequence data are typed using a whole-genome multi-locus sequence typing scheme and clustered with sequences already in the database. Publicly available text files contain the results of these analyses.

Benchmarking short and long read polishing tools for nanopore assemblies: achieving near-perfect genomes for outbreak isolates.

Background: Oxford Nanopore provides high throughput sequencing platforms able to reconstruct complete bacterial genomes with 99.95% accuracy. However, even small levels of error can obscure the phylogenetic relationships between closely related isolates.

SARS-CoV-2 wastewater variant surveillance: pandemic response leveraging FDA's GenomeTrakr network.

Unlabelled: Wastewater surveillance has emerged as a crucial public health tool for population-level pathogen surveillance. Supported by funding from the American Rescue Plan Act of 2021, the FDA's genomic epidemiology program, GenomeTrakr, was leveraged to sequence SARS-CoV-2 from wastewater sites across the United States. This initiative required the evaluation, optimization, development, and publication of new methods and analytical tools spanning sample collection through variant analyses.

Application of quasimetagenomics methods to define microbial diversity and subtype in dairy and seafood production facilities.

In developed countries, the human diet is predominated by food commodities, which have been manufactured, processed, and stored in a food production facility. Little is known about the application of metagenomic sequencing approaches for detecting foodborne pathogens, such as , and characterizing microbial diversity in food production ecosystems. In this work, we investigated the utility of 16S rRNA amplicon and quasimetagenomic sequencing for the taxonomic and phylogenetic classification of culture enrichments of environmental swabs collected from dairy and seafood production facilities.

The worm Adult Activity Test (wAAT): A de novo mathematical model for detecting acute chemical effects in Caenorhabditis elegans.

We have adapted a semiautomated method for tracking Caenorhabditis elegans spontaneous locomotor activity into a quantifiable assay by developing a sophisticated method for analyzing the time course of measured activity. The 16-h worm Adult Activity Test (wAAT) can be used to measure C. elegans activity levels for efficient screening for pharmacological and toxicity-induced effects.

Assessment of plasmids for relating the 2020 Salmonella enterica serovar Newport onion outbreak to farms implicated by the outbreak investigation.

Background: The Salmonella enterica serovar Newport red onion outbreak of 2020 was the largest foodborne outbreak of Salmonella in over a decade. The epidemiological investigation suggested two farms as the likely source of contamination. However, single nucleotide polymorphism (SNP) analysis of the whole genome sequencing data showed that none of the Salmonella isolates collected from the farm regions were linked to the clinical isolates-preventing the use of phylogenetics in source identification.

Performance of methods for SARS-CoV-2 variant detection and abundance estimation within mixed population samples.

Background: The accurate identification of SARS-CoV-2 (SC2) variants and estimation of their abundance in mixed population samples (, air or wastewater) is imperative for successful surveillance of community level trends. Assessing the performance of SC2 variant composition estimators (VCEs) should improve our confidence in public health decision making. Here, we introduce a linear regression based VCE and compare its performance to four other VCEs: two re-purposed DNA sequence read classifiers (Kallisto and Kraken2), a maximum-likelihood based method (Lineage deComposition for Sars-Cov-2 pooled samples (LCS)), and a regression based method (Freyja).

PRAWNS: compact pan-genomic features for whole-genome population genomics.

Motivation: Scientists seeking to understand the genomic basis of bacterial phenotypes, such as antibiotic resistance, today have access to an unprecedented number of complete and nearly complete genomes. Making sense of these data requires computational tools able to perform multiple-genome comparisons efficiently, yet currently available tools cannot scale beyond several tens of genomes.

Results: We describe PRAWNS, an efficient and scalable tool for multiple-genome analysis.

Transient and resident pathogens: Intra-facility genetic diversity of Listeria monocytogenes and Salmonella from food production environments.

Food production facilities are often routinely tested over time for the presence of foodborne pathogens (e.g., Listeria monocytogenes or Salmonella enterica subsp.

Using Evolutionary Analyses to Refine Whole-Genome Sequence Match Criteria.

Whole-genome sequence databases continue to grow. Collection times between samples are also growing, providing both a challenge for comparing recently collected sequence data to historical samples and an opportunity for evolutionary analyses that can be used to refine match criteria. We measured evolutionary rates for 22 serotypes.

Whole-Genome Assessment of Clinical Isolates Uncovers Potentially Novel Factors Influencing Carbapenem Resistance.

Carbapenems-one of the important last-line antibiotics for the treatment of gram-negative infections-are becoming ineffective for treating infections. Studies have identified multiple genes (and mechanisms) responsible for carbapenem resistance. In some strains, the presence/absence of putative resistance genes is not consistent with their resistance phenotype-indicating the genomic factors underlying carbapenem resistance in are not fully understood.

Interpretative Labor and the Bane of Nonstandardized Metadata in Public Health Surveillance and Food Safety.

Open-source DNA sequence databases have long been touted as beneficial to public health, including the facilitation of earlier detection and response to infectious disease outbreaks. Of critical importance to harnessing these benefits is the metadata that describe general and other domain-specific attributes (eg, collection location, isolate type) of a sample. Unlike the sequence data, metadata are often incomplete and lack adherence to an international standard.

Evaluating the accuracy of Listeria monocytogenes assemblies from quasimetagenomic samples using long and short reads.

Background: Whole genome sequencing of cultured pathogens is the state of the art public health response for the bioinformatic source tracking of illness outbreaks. Quasimetagenomics can substantially reduce the amount of culturing needed before a high quality genome can be recovered. Highly accurate short read data is analyzed for single nucleotide polymorphisms and multi-locus sequence types to differentiate strains but cannot span many genomic repeats, resulting in highly fragmented assemblies.

GalaxyTrakr: a distributed analysis tool for public health whole genome sequence data accessible to non-bioinformaticians.

Background: Processing and analyzing whole genome sequencing (WGS) is computationally intense: a single Illumina MiSeq WGS run produces ~ 1 million 250-base-pair reads for each of 24 samples. This poses significant obstacles for smaller laboratories, or laboratories not affiliated with larger projects, which may not have dedicated bioinformatics staff or computing power to effectively use genomic data to protect public health. Building on the success of the cloud-based Galaxy bioinformatics platform ( http://galaxyproject.

Gen-FS coordinated proficiency test data for genomic foodborne pathogen surveillance, 2017 and 2018 exercises.

The US PulseNet and GenomeTrakr laboratory networks work together within the Genomics for Food Safety (Gen-FS) consortium to collect and analyze genomic data for foodborne pathogen surveillance (species include Salmonella enterica, Listeria monocytogenes, Escherichia coli (STECs), and Campylobactor). In 2017 these two laboratory networks started harmonizing their respective proficiency test exercises, agreeing on distributing a single strain-set and following the same standard operating procedure (SOP) for genomic data collection, running a jointly coordinated annual proficiency test exercise. In this data release we are publishing the reference genomes and raw data submissions for the 2017 and 2018 proficiency test exercises.

Genomic diversity of isolated from papaya samples collected during multiple outbreaks in 2017.

In 2017, the US Food and Drug Administration investigated the sources of multiple outbreaks of salmonellosis. Epidemiologic and traceback investigations identified Maradol papayas as the suspect vehicles. During the investigations, the genomes of 55 that were isolated from papaya samples were sequenced.

Within-species contamination of bacterial whole-genome sequence data has a greater influence on clustering analyses than between-species contamination.

Although it is assumed that contamination in bacterial whole-genome sequencing causes errors, the influences of contamination on clustering analyses, such as single-nucleotide polymorphism discovery, phylogenetics, and multi-locus sequencing typing, have not been quantified. By developing and analyzing 720 Listeria monocytogenes, Salmonella enterica, and Escherichia coli short-read datasets, we demonstrate that within-species contamination causes errors that confound clustering analyses, while between-species contamination generally does not. Contaminant reads mapping to references or becoming incorporated into chimeric sequences during assembly are the sources of those errors.

Whole genome sequencing uses for foodborne contamination and compliance: Discovery of an emerging contamination event in an ice cream facility using whole genome sequencing.

We review how FDA surveillance identifies several ways that whole genome sequencing (WGS) improves actionable outcomes for public health and compliance in a case involving Listeria monocytogenes contamination in an ice cream facility. In late August 2017 FDA conducted environmental sampling inside an ice cream facility. These isolates were sequenced and deposited into the GenomeTrakr databases.

Phylogenomic Pipeline Validation for Foodborne Pathogen Disease Surveillance.

Foodborne pathogen surveillance in the United States is transitioning from strain identification using restriction digest technology (pulsed-field gel electrophoresis [PFGE]) to shotgun sequencing of the entire genome (whole-genome sequencing [WGS]). WGS requires a new suite of analysis tools, some of which have long histories in academia but are new to the field of public health and regulatory decision making. Although the general workflow is fairly standard for collecting and analyzing WGS data for disease surveillance, there are a number of differences in how the data are collected and analyzed across public health agencies, both nationally and internationally.

Genetic Diversity of Salmonella and Listeria Isolates from Food Facilities.

Food production-related facilities (farms, packing houses, etc.) are monitored for foodborne pathogens, and data from these facilities can provide a rich source of information about the population structure and genetic diversity of Salmonella and Listeria. This information is of both academic interest for understanding the evolutionary forces acting on these organisms and of practical interest to those responsible for controlling pathogens in facilities and to those analyzing data from facilities in the context of public health decision making.

Interpreting Whole-Genome Sequence Analyses of Foodborne Bacteria for Regulatory Applications and Outbreak Investigations.

Whole-genome sequence (WGS) analysis has revolutionized the food safety industry by enabling high-resolution typing of foodborne bacteria. Higher resolving power allows investigators to identify origins of contamination during illness outbreaks and regulatory activities quickly and accurately. Government agencies and industry stakeholders worldwide are now analyzing WGS data routinely.

GenomeTrakr proficiency testing for foodborne pathogen surveillance: an exercise from 2015.

Pathogen monitoring is becoming more precise as sequencing technologies become more affordable and accessible worldwide. This transition is especially apparent in the field of food safety, which has demonstrated how whole-genome sequencing (WGS) can be used on a global scale to protect public health. GenomeTrakr coordinates the WGS performed by public-health agencies and other partners by providing a public database with real-time cluster analysis for foodborne pathogen surveillance.

Segal's Law, 16S rRNA gene sequencing, and the perils of foodborne pathogen detection within the American Gut Project.

Obtaining human population level estimates of the prevalence of foodborne pathogens is critical for understanding outbreaks and ameliorating such threats to public health. Estimates are difficult to obtain due to logistic and financial constraints, but citizen science initiatives like that of the American Gut Project (AGP) represent a potential source of information concerning enteric pathogens. With an emphasis on genera and , we sought to document the prevalence of those two taxa within the AGP samples.