GenomeGraphR: A user-friendly open-source web application for foodborne pathogen Whole Genome Sequencing data integration, analysis, and visualization.

Abstract

20Food safety risk assessments and large-scale epidemiological investigations have the potential 21 to provide better and new types of information when whole genome sequence (WGS) data are 22 effectively integrated. Today, the NCBI Pathogen Detection database WGS collections have 23 grown significantly through improvements in technology, coordination, and collaboration, 24 such as the GenomeTrakr and PulseNet networks. However, high-quality genomic data is not 25 often coupled with high-quality epidemiological or food chain metadata. We have created a 26 set of tools for cleaning, curation, integration, analysis and visualization of microbial genome 27 sequencing data. It has been tested using Salmonella enterica and Listeria monocytogenes 28 data sets provided by NCBI Pathogen Detection (160,000 sequenced isolates). 29GenomeGraphR presents foodborne pathogen WGS data and associated curated metadata in a 30 user-friendly interface that allows a user to query a variety of research questions such as, 31 transmission sources and dynamics, global reach, and persistence of genotypes associated 32 with contamination in the food supply and foodborne illness across time or space. The 33 application is freely available (https://fda-riskmodels.foodrisk.org/genomegraphr/). 34 35 Detection 52 (https://www.ncbi.nlm.nih.gov/pathogens) have grown significantly through improvements in 53 technology, coordination, and collaboration of the GenomeTrakr and PulseNet networks in 54 the US. Presently, the two networks sequence and release about 5,000 new bacterial genomes 55 per month [11,12] which makes their analysis and interpretation increasingly demanding. 56Our original goal was to explore what we, as risk assessors and epidemiologists, could learn 57 from the NCBI WGS data. We found, however, that despite extensive efforts by the 58

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

“…Importantly, as genomes for different pathogens vary in size and complexity, the coverage needed to obtain a good L. monocytogenes or C. jejuni assembly does appear to be less than for larger, more complex genomes like Shigella and E. coli . GenomeTrakr recommendations for coverage represent a compromise between sequencing cost and data quality (Table 1 and [ 29 ]). As isolates are typically barcoded and multiplexed, often including isolates from multiple different surveillance efforts on the same sequencing run (e.g.…”

“…As a result of building interoperable systems, researchers can now use public data from the INSDC in conjunction with private data from individual or industry labs, using platforms such as Integrated Rapid Infectious Disease Analysis (IRIDA) [22], INNUENDO [23], PathogenWatch [24] NextStrain [13], IDseq [25], and CGE Evergreen [26]. And finally, for FDA-specific missions, open source tools to support genomic epidemiology (GalaxyTrakr) [27], AMR surveillance (Resistome Tracker) [28], and risk assessment (GenomeGraphR) [29] have been created.…”

Accurate reconstruction of bacterial pan- and core- genomes with PEPPAN

“…The numbers of genomes per microbial species have increased very dramatically over the last decade, and databases now exist that include 100,000s of assembled genomes from a single bacterial genus (Sanaa et al 2019;Zhou et al 2020). However, the question of an open pan-genome has not yet been revisited with these large datasets.…”

Salmonella enterica serovar Typhimurium from Wild Birds in the United States Represent Distinct Lineages Defined by Bird Type

“…Clustering involved two steps: First, related isolates were clustered based on whole genome MLST (wgMLST) scheme of Salmonella with a 25-allele cut-off; Once wgMLST clusters were created, SNPs are called by aligning assemblies against a reference genome chosen from each wgMLST cluster of closely related isolates, and SNP clusters and phylogenetic trees were inferred. A SNP cluster was classified as a cluster of isolates where each isolate was less than or equal to 50 SNPs distant from other members of the cluster (40). Individual phylogenetic trees for each SNP cluster, together with the metadata information of isolates in the same cluster ( Table S2 ), were used to examine relationship between wild bird isolates and other isolates.…”