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Madurella mycetomatis is one of the main causative agents of mycetoma, a debilitating neglected tropical disease. Improved understanding of the genomic diversity of the fungal and bacterial causes of mycetoma is essential to advances in diagnosis and treatment. Here, we describe a high-quality genome assembly of M. mycetomatis and results of the whole genome sequence analysis of 26 isolates from Sudan. We demonstrate evidence of at least seven genetically diverse lineages and extreme clonality among isolates within these lineages. We also performed shotgun metagenomic analysis of DNA extracted from mycetoma grains and showed that M. mycetomatis reads were detected in all sequenced samples with the average of 11,317 reads (s.d. +/- 21,269) per sample. In addition, 10 (12%) of the 81 tested grain samples contained bacterial reads including Streptococcus sp., Staphylococcus sp. and others.

Abstract Trimming adapters and low-quality bases from next-generation sequencing (NGS) data is crucial for optimal analysis. We evaluated six trimming programs, implementing five different algorithms, for their effectiveness in trimming adapters and improving quality, contig assembly, and single-nucleotide polymorphism (SNP) quality and concordance for poliovirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and norovirus paired data sequenced on Illumina iSeq and MiSeq platforms. Trimmomatic and BBDuk effectively removed adapters from all datasets, unlike FastP, AdapterRemoval, SeqPurge, and Skewer. All trimmers improved read quality (Q ≥ 30, 87.8 − 96.1%) compared to raw reads (83.6 − 93.2%). Trimmers implementing traditional sequence-matching (Trimmomatic and AdapterRemoval) and overlapping algorithm (FastP) retained the highest-quality reads. While all trimmers improved the maximum contig length and genome coverage for iSeq and MiSeq viral assemblies, BBDuk-trimmed reads assembled the shortest contigs. SNP concordance was consistently high (> 97.7 − 100%) across trimmers. However, BBDuk-trimmed reads had the lowest quality SNPs. Overall, the two adapter trimmers that utilized the traditional sequence-matching algorithm performed consistently across the viral datasets analyzed. Our findings guide software selection and inform future versatile trimmer development for viral genome analysis.

ABSTRACT Next-generation sequencing (NGS) is a powerful tool for detecting and investigating viral pathogens; however, analysis and management of the enormous amounts of data generated from these technologies remains a challenge. Here, we present VPipe (the Viral NGS Analysis Pipeline and Data Management System), an automated bioinformatics pipeline optimized for whole-genome assembly of viral sequences and identification of diverse species. VPipe automates the data quality control, assembly, and contig identification steps typically performed when analyzing NGS data. Users access the pipeline through a secure web-based portal, which provides an easy-to-use interface with advanced search capabilities for reviewing results. In addition, VPipe provides a centralized system for storing and analyzing NGS data, eliminating common bottlenecks in bioinformatics analyses for public health laboratories with limited on-site computational infrastructure. The performance of VPipe was validated through the analysis of publicly available NGS data sets for viral pathogens, generating high-quality assemblies for 12 data sets. VPipe also generated assemblies with greater contiguity than similar pipelines for 41 human respiratory syncytial virus isolates and 23 SARS-CoV-2 specimens. IMPORTANCE Computational infrastructure and bioinformatics analysis are bottlenecks in the application of NGS to viral pathogens. As of September 2021, VPipe has been used by the U.S. Centers for Disease Control and Prevention (CDC) and 12 state public health laboratories to characterize >17,500 and 1,500 clinical specimens and isolates, respectively. VPipe automates genome assembly for a wide range of viruses, including high-consequence pathogens such as SARS-CoV-2. Such automated functionality expedites public health responses to viral outbreaks and pathogen surveillance.

Background Whole genome sequencing (WGS) has gained increasing importance in responses to enteric bacterial outbreaks. Common analysis procedures for WGS, single nucleotide polymorphisms (SNPs) and genome assembly, are highly dependent upon WGS data quality. Methods Raw, unprocessed WGS reads from Escherichia coli, Salmonella enterica, and Shigella sonnei outbreak clusters were characterized for four quality metrics: PHRED score, read length, library insert size, and ambiguous nucleotide composition. PHRED scores were strongly correlated with improved SNPs analysis results in E. coli and S. enterica clusters. Results Assembly quality showed only moderate correlations with PHRED scores and library insert size, and then only for Salmonella. To improve SNP analyses and assemblies, we compared seven read-healing pipelines to improve these four quality metrics and to see how well they improved SNP analysis and genome assembly. The most effective read healing pipelines for SNPs analysis incorporated quality-based trimming, fixed-width trimming, or both. The Lyve-SET SNPs pipeline showed a more marked improvement than the CFSAN SNP Pipeline, but the latter performed better on raw, unhealed reads. For genome assembly, SPAdes enabled significant improvements in healed E. coli reads only, while Skesa yielded no significant improvements on healed reads. Conclusions PHRED scores will continue to be a crucial quality metric albeit not of equal impact across all types of analyses for all enteric bacteria. While trimming-based read healing performed well for SNPs analyses, different read healing approaches are likely needed for genome assembly or other, emerging WGS analysis methodologies.

Candida auris is an urgent public health threat characterized by high drug-resistant rates and rapid spread in healthcare settings worldwide. As part of the C. auris response, molecular surveillance has helped public health officials track the global spread and investigate local outbreaks. Here, we describe whole-genome sequencing analysis methods used for routine C. auris molecular surveillance in the United States; methods include reference selection, reference preparation, quality assessment and control of sequencing reads, read alignment, and single-nucleotide polymorphism calling and filtration. We also describe the newly developed pipeline MycoSNP, a portable workflow for performing whole-genome sequencing analysis of fungal organisms including C. auris., (© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

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.

The recent emergence of a multidrug-resistant yeast, Candida auris , has drawn attention to the closely related species from the Candida haemulonii complex that include C. haemulonii, Candida duobushaemulonii, Candida pseudohaemulonii , and the recently identified Candida vulturna . Here, we used antifungal susceptibility testing and whole-genome sequencing (WGS) to investigate drug resistance and genetic diversity among isolates of C. haemulonii complex from different geographic areas in order to assess population structure and the extent of clonality among strains. Although most isolates of all four species were genetically distinct, we detected evidence of the in-hospital transmission of C. haemulonii and C. duobushaemulonii in one hospital in Panama, indicating that these species are also capable of causing outbreaks in healthcare settings. We also detected evidence of the rising azole resistance among isolates of C. haemulonii and C. duobushaemulonii in Colombia, Panama, and Venezuela linked to substitutions in ERG11 gene as well as amplification of this gene in C. haemulonii in isolates in Colombia suggesting the presence of evolutionary pressure for developing azole resistance in this region. Our results demonstrate that these species need to be monitored as possible causes of outbreaks of invasive infection., (Copyright © 2020 Gade, Muñoz, Sheth, Wagner, Berkow, Forsberg, Jackson, Ramos-Castro, Escandón, Dolande, Ben-Ami, Espinosa-Bode, Caceres, Lockhart, Cuomo and Litvintseva.)

Foodborne salmonellosis causes an estimated 1 million illnesses and 400 deaths annually in the United States (1). In recent years, salmonellosis outbreaks have been caused by foods not typically associated with Salmonella. On May 2, 2017, PulseNet, CDC's national molecular subtyping network for foodborne disease surveillance, identified a cluster of 14 Salmonella Chailey isolates with a rare pulsed-field gel electrophoresis (PFGE) pattern. On May 29, Canadian health officials informed CDC that they were also investigating a cluster of five Salmonella Chailey infections in British Columbia with the same PFGE pattern. Nineteen cases were identified and investigated by CDC, U.S. state health departments, the Public Health Agency of Canada, and the British Columbia Centre for Disease Control. Isolates from all cases were highly related by whole genome sequencing (WGS). Illness onset dates ranged from March 10 to May 7, 2017. Initial interviews revealed that infected persons consumed various fresh foods and shopped at grocery chain A; focused questionnaires identified precut coconut pieces from grocery chain A as a common vehicle. The Canadian Food Inspection Agency (CFIA) and the U.S. Food and Drug Administration (FDA) conducted a traceback investigation that implicated a single lot of frozen, precut coconut as the outbreak source. Grocery chain A voluntarily removed precut coconut pieces from their stores. This action likely limited the size and scope of this outbreak., Competing Interests: All authors have completed and submitted the ICMJE form for disclosure of potential conflicts of interest. No potential conflicts of interest were disclosed.

Competing Interests: No conflicts of interest were reported.

In January 2017, CDC identified a cluster of Salmonella enterica serotype Newport infections with isolates sharing an indistinguishable pulsed-field gel electrophoresis (PFGE) pattern, JJPX01.0010 (pattern 10), through PulseNet, the national molecular subtyping network for foodborne disease surveillance. This report summarizes the investigation by CDC, state and local health and agriculture departments, and the U.S. Department of Agriculture's Food Safety and Inspection Service (USDA-FSIS) and discusses the possible role of dairy cows as a reservoir for strains of Salmonella that persistently cause human illness. This investigation combined epidemiologic and whole genome sequencing (WGS) data to link the outbreak to contaminated ground beef; dairy cows were hypothesized to be the ultimate source of Salmonella contamination., Competing Interests: No conflicts of interest were reported.

Background: In 2016, a multijurisdictional team investigated an outbreak of Shiga toxin-producing Escherichia coli (STEC) serogroup O121 and O26 infections linked to contaminated flour from a large domestic producer., Methods: A case was defined as infection with an outbreak strain in which illness onset was between December 21, 2015, and September 5, 2016. To identify exposures associated with the outbreak, outbreak cases were compared with non-STEC enteric illness cases, matched according to age group, sex, and state of residence. Products suspected to be related to the outbreak were collected for STEC testing, and a common point of contamination was sought. Whole-genome sequencing was performed on isolates from clinical and food samples., Results: A total of 56 cases were identified in 24 states. Univariable exact conditional logistic-regression models of 22 matched sets showed that infection was significantly associated with the use of one brand of flour (odds ratio, 21.04; 95% confidence interval [CI], 4.69 to 94.37) and with tasting unbaked homemade dough or batter (odds ratio, 36.02; 95% CI, 4.63 to 280.17). Laboratory testing isolated the outbreak strains from flour samples, and whole-genome sequencing revealed that the isolates from clinical and food samples were closely related to one another genetically. Trace-back investigation identified a common flour-production facility., Conclusions: This investigation implicated raw flour as the source of an outbreak of STEC infections. Although it is a low-moisture food, raw flour can be a vehicle for foodborne pathogens.

High consumption rates and a multitude of brands make multistate foodborne outbreaks of Salmonella infections associated with chicken challenging to investigate, but whole genome sequencing is a powerful tool that can be used to assist investigators. Whole genome sequencing of pathogens isolated from clinical, environmental, and food samples is increasingly being used in multistate foodborne outbreak investigations to determine with unprecedented resolution how closely related these isolates are to one another genetically. In 2014, federal and state health officials investigated an outbreak of 146 Salmonella Heidelberg infections in 24 states. A follow-up analysis was conducted after the conclusion of the investigation in which 27 clinical and 24 food isolates from the outbreak underwent whole genome sequencing. These isolates formed seven clades, the largest of which contained clinical isolates from a subcluster of case patients who attended a catered party. One isolate from a chicken processed by a large producer was closely related genetically (zero to three single-nucleotide polymorphism differences) to the clinical isolates from these subcluster case patients. Chicken from this large producer was also present in the kitchen of the caterer on the day before the event, thus providing additional evidence that the chicken from this producer was the outbreak source. This investigation highlights how whole genome sequencing can be used with epidemiologic and traceback evidence to identify chicken sources of foodborne outbreaks.

Modern epidemiology of foodborne bacterial pathogens in industrialized countries relies increasingly on whole genome sequencing (WGS) techniques. As opposed to profiling techniques such as pulsed-field gel electrophoresis, WGS requires a variety of computational methods. Since 2013, United States agencies responsible for food safety including the CDC, FDA, and USDA, have been performing whole-genome sequencing (WGS) on all Listeria monocytogenes found in clinical, food, and environmental samples. Each year, more genomes of other foodborne pathogens such as Escherichia coli, Campylobacter jejuni , and Salmonella enterica are being sequenced. Comparing thousands of genomes across an entire species requires a fast method with coarse resolution; however, capturing the fine details of highly related isolates requires a computationally heavy and sophisticated algorithm. Most L. monocytogenes investigations employing WGS depend on being able to identify an outbreak clade whose inter-genomic distances are less than an empirically determined threshold. When the difference between a few single nucleotide polymorphisms (SNPs) can help distinguish between genomes that are likely outbreak-associated and those that are less likely to be associated, we require a fine-resolution method. To achieve this level of resolution, we have developed Lyve-SET, a high-quality SNP pipeline. We evaluated Lyve-SET by retrospectively investigating 12 outbreak data sets along with four other SNP pipelines that have been used in outbreak investigation or similar scenarios. To compare these pipelines, several distance and phylogeny-based comparison methods were applied, which collectively showed that multiple pipelines were able to identify most outbreak clusters and strains. Currently in the US PulseNet system, whole genome multi-locus sequence typing (wgMLST) is the preferred primary method for foodborne WGS cluster detection and outbreak investigation due to its ability to name standardized genomic profiles, its central database, and its ability to be run in a graphical user interface. However, creating a functional wgMLST scheme requires extended up-front development and subject-matter expertise. When a scheme does not exist or when the highest resolution is needed, SNP analysis is used. Using three Listeria outbreak data sets, we demonstrated the concordance between Lyve-SET SNP typing and wgMLST. Availability : Lyve-SET can be found at https://github.com/lskatz/Lyve-SET.

Introduction: Recently, Salmonella enterica serovar Poona caused a multistate outbreak, with 245 out of 907 cases occurring in California. We report a comparison of pulsed-field gel electrophoresis (PFGE) results with whole genome sequencing (WGS) for genotyping of Salmonella Poona isolates., Methods: CA Salmonella Poona isolates, collected from July to August 2015, were genotyped by PFGE using XbaI restriction enzyme. WGS was done using Nextera XT library kit with 2x300 bp or 2x250 bp sequencing chemistry on the Illumina MiSeq Sequencer.  Reads were mapped to the de novo assembled serovar Poona draft genome (48 contigs, N50= 223,917) from the outbreak using CLCbio GW 8.0.2. The phylogenetic tree was generated based on hqSNPs calling. Genomes were annotated with CGE and PHAST online tools. In silico MLST was performed using the CGE online tool., Results: Human (14) and cucumber (2) Salmonella Poona isolates exhibited 3 possibly related PFGE patterns (JL6X01.0018 [predominant], JL6X01.0375, JL6X01.0778).  All isolates that were related by PFGE also clustered together according to the WGS. One isolate with a divergent PFGE pattern (JL6X01.0776) served as an outlier in the phylogenetic analysis and substantially differed from the outbreak clade by WGS. All outbreak isolates were assigned to MLST sequence type 447. The majority of the outbreak-related isolates possessed the same set of Salmonella Pathogenicity Islands with few variations. One outbreak isolate was sequenced and analyzed independently by CDC and CDPH laboratories; there was 0 SNP difference in results. Additional two isolates were sequenced by CDC and the raw data was processed through CDPH and CDC analysis pipelines. Both data analysis pipelines also generated concordant results.  Discussion: PFGE and WGS results for the recent CA Salmonella enterica serovar Poona outbreak provided concordant assignment of the isolates to the outbreak cluster. WGS allowed more robust determination of genetic relatedness, provided information regarding MLST-type, pathogenicity genes, and bacteriophage content. WGS data obtained independently at two laboratories showed complete agreement.