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The wide availability of whole-genome sequencing (WGS) and an abundance of open-source software have made detection of single-nucleotide polymorphisms (SNPs) in bacterial genomes an increasingly accessible and effective tool for comparative analyses. Thus, ensuring that real nucleotide differences between genomes (i.e., true SNPs) are detected at high rates and that the influences of errors (such as false positive SNPs, ambiguously called sites, and gaps) are mitigated is of utmost importance. The choices researchers make regarding the generation and analysis of WGS data can greatly influence the accuracy of short-read sequence alignments and, therefore, the efficacy of such experiments. We studied the effects of some of these choices, including: i) depth of sequencing coverage, ii) choice of reference-guided short-read sequence assembler, iii) choice of reference genome, and iv) whether to perform read-quality filtering and trimming, on our ability to detect true SNPs and on the frequencies of errors. We performed benchmarking experiments, during which we assembled simulated and real Listeria monocytogenes strain 08-5578 short-read sequence datasets of varying quality with four commonly used assemblers (BWA, MOSAIK, Novoalign, and SMALT), using reference genomes of varying genetic distances, and with or without read pre-processing (i.e., quality filtering and trimming). We found that assemblies of at least 50-fold coverage provided the most accurate results. In addition, MOSAIK yielded the fewest errors when reads were aligned to a nearly identical reference genome, while using SMALT to align reads against a reference sequence that is ∼0.82% distant from 08-5578 at the nucleotide level resulted in the detection of the greatest numbers of true SNPs and the fewest errors. Finally, we show that whether read pre-processing improves SNP detection depends upon the choice of reference sequence and assembler. In total, this study demonstrates that researchers should test a variety of conditions to achieve optimal results.

Determining associations between intestinal bacteria and continuously measured physiological outcomes is important for understanding the bacteria-host relationship but is not straightforward since abundance data (compositional data) are not normally distributed. To address this issue, we developed a fully Bayesian linear regression model (BRACoD; B ayesian R egression A nalysis of Co mpositional D ata) with physiological measurements (continuous data) as a function of a matrix of relative bacterial abundances. Bacteria can be classified as operational taxonomic units or by taxonomy (genus, family, etc.). Bacteria associated with the physiological measurement were identified using a Bayesian variable selection method: Stochastic Search Variable Selection. The output is a list of inclusion probabilities ( p ^ ) and coefficients that indicate the strength of the association ( β ^ i n c l u d e d ) for each bacterial taxa. Tests with simulated communities showed that adopting a cut point value of p ^ ≥ 0.3 for identifying included bacteria optimized the true positive rate (TPR) while maintaining a false positive rate (FPR) of ≤ 5%. At this point, the chances of identifying non-contributing bacteria were low and all well-established contributors were included. Comparison with other methods showed that BRACoD (at p ^ ≥ 0.3) had higher precision and a higher TPR than a commonly used center log transformed LASSO procedure (clr-LASSO) as well as higher TPR than an off-the-shelf Spike and Slab method after center log transformation (clr-SS). BRACoD was also less likely to include non-contributing bacteria that merely correlate with contributing bacteria. Analysis of a rat microbiome experiment identified 47 operational taxonomic units that contributed to fecal butyrate levels. Of these, 31 were positively and 16 negatively associated with butyrate. Consistent with their known role in butyrate metabolism, most of these fell within the Lachnospiraceae and Ruminococcaceae. We conclude that BRACoD provides a more precise and accurate method for determining bacteria associated with a continuous physiological outcome compared to clr-LASSO. It is more sensitive than a generalized clr-SS algorithm, although it has a higher FPR. Its ability to distinguish genuine contributors from correlated bacteria makes it better suited to discriminating bacteria that directly contribute to an outcome. The algorithm corrects for the distortions arising from compositional data making it appropriate for analysis of microbiome data.

Background Salmonella enterica is a leading cause of foodborne illness worldwide resulting in considerable public health and economic costs. Testing for the presence of this pathogen in food is often hampered by the presence of background microflora that may present as Salmonella (false positives). False positive isolates belonging to the genus Citrobacter can be difficult to distinguish from Salmonella due to similarities in their genetics, cell surface antigens, and other phenotypes. In order to understand the genetic basis of these similarities, a comparative genomic approach was used to define the pan-, core, accessory, and unique coding sequences of a representative population of Salmonella and Citrobacter strains. Results Analysis of the genomic content of 58  S. enterica strains and 37 Citrobacter strains revealed the presence of 31,130 and 1540 coding sequences within the pan- and core genome of this population. Amino acid sequences unique to either Salmonella ( n  = 1112) or Citrobacter ( n  = 195) were identified and revealed potential niche-specific adaptations. Phylogenetic network analysis of the protein families encoded by the pan-genome indicated that genetic exchange between Salmonella and Citrobacter may have led to the acquisition of similar traits and also diversification within the genera. Conclusions Core genome analysis suggests that the Salmonella enterica and Citrobacter populations investigated here share a common evolutionary history. Comparative analysis of the core and pan-genomes was able to define the genetic features that distinguish Salmonella from Citrobacter and highlight niche specific adaptations.

Background Human norovirus is the leading cause of viral gastroenteritis globally, and the GII.4 has been the most predominant genotype for decades. This genotype has numerous variants that have caused repeated epidemics worldwide. However, the molecular evolutionary signatures among the GII.4 variants have not been elucidated throughout the viral genome. Method A metagenomic, next-generation sequencing method, based on Illumina RNA-Seq, was applied to determine norovirus sequences from clinical samples. Results Herein, the obtained deep-sequencing data was employed to analyze full-genomic sequences from GII.4 variants prevailing in Canada from 2012 to 2016. Phylogenetic analysis demonstrated that the majority of these sequences belong to New Orleans 2009 and Sydney 2012 strains, and a recombinant sequence was also identified. Genome-wide similarity analyses implied that while the capsid gene is highly diverse among the isolates, the viral protease and polymerase genes remain relatively conserved. Numerous amino acid substitutions were observed at each putative antigenic epitope of the VP1 protein, whereas few amino acid changes were identified in the polymerase protein. Co-infection with other enteric RNA viruses was investigated and the astrovirus genome was identified in one of the samples. Conclusions Overall this study demonstrated the application of whole genome sequencing as an important tool in molecular characterization of noroviruses.

Over the past decade, frozen fruits have been a major vehicle of foodborne illnesses mainly attributed to norovirus (NoV) and hepatitis A virus (HAV) infections. Fresh produce may acquire viral contamination by direct contact with contaminated surface, water or hands, and is then frozen without undergoing proper decontamination. Due to their structural integrity, foodborne viruses are able to withstand hostile conditions such as desiccation and freezing, and endure for a long period of time without losing their infectivity. Additionally, these foods are often consumed raw or undercooked, which increases the risk of infection. Herein, we searched published literature and databases of reported outbreaks as well as the databases of news articles for the viral outbreaks associated with the consumption of frozen produce between January 2008 and December 2018; recorded the worldwide distribution of these outbreaks; and analysed the implication of consumption of different types of contaminated frozen food. In addition, we have briefly discussed the factors that contribute to an increased risk of foodborne viral infection following the consumption of frozen produce. Our results revealed that frozen fruits, especially berries and pomegranate arils, contributed to the majority of the outbreaks, and that most outbreaks were reported in industrialised countries.

Background Next-generation sequencing provides a powerful means of molecular characterization . However, methods such as single-nucleotide polymorphism detection or whole-chromosome sequence analysis are computationally expensive, prone to errors, and are still less accessible than traditional typing methods. Here, we present the Listeria monocytogenes core-genome sequence typing method for molecular characterization. This method uses a high-confidence core (HCC) genome, calculated to ensure accurate identification of orthologs. We also developed an evolutionarily relevant nomenclature based upon phylogenetic analysis of HCC genomes. Finally, we created a pipeline (LmCGST; https://sourceforge.net/projects/lmcgst/files/ ) that takes in raw next-generation sequencing reads, calculates a subject HCC profile, compares it to an expandable database, assigns a sequence type, and performs a phylogenetic analysis. Results We analyzed 29 high-quality, closed Listeria monocytogenes chromosome sequences and identified loci that are reliable targets for automated molecular characterization methods. We identified 1013 open-reading frames that comprise our high-confidence core (HCC) genome. We then populated a database with HCC profiles from 114 taxa. We sequenced 84 randomly selected isolates from the Listeriosis Reference Service for Canada’s collection and analysed them with the LmCGST pipeline. In addition, we generated pulsed-field gel electrophoresis, ribotyping, and in silico multi-locus sequence typing (MLST) data for the 84 isolates and compared the results to those obtained using the CGST method. We found that all of the methods yielded results that are generally congruent. However, due to the increased numbers of categories, the CGST method provides much greater discriminatory power than the other methods tested here. Conclusions We show that the CGST method provides increased discriminatory power relative to typing methods such as pulsed-field gel electrophoresis, ribotyping, and multi-locus sequence typing while it addresses several shortcomings of other methods of molecular characterization with next-generation sequence data. It uses discrete, well-defined groupings (types) of organisms that are phylogenetically relevant and easily interpreted. In addition, the CGST scheme can be expanded to include additional loci and HCC profiles in the future. In total, the CGST method provides an approach to the molecular characterization of Listeria monocytogenes with next-generation sequence data that is highly reproducible, easily standardized, portable, and accessible.

The trypsin gene families of primate species are composed of members who share a remarkable level of sequence similarity. Here, we investigated the gene conversions occurring within the trypsin gene family in five primate species. A total of 36 conversion events, with an average length (±standard deviation) of 1,526 ± 1,124 nucleotides, were detected using two methods. Such extensive gene conversions are likely both the cause and the consequence of the high sequence similarity between primate trypsin genes. In the trypsins encoded by these genes, both the overall amino acid sequences and critical amino acid residues are conserved. Therefore, the numerous long gene conversions we detected between trypsin genes did not alter any of their functionally important amino acid sites. This suggest that, in the trypsin genes of the five primate species studied here, strong purifying selection against gene conversions is occurring in regions containing functionally important residues.