Performance and Accuracy of Four Open-Source Tools for In Silico Serotyping of Salmonella spp. Based on Whole-Genome Short-Read Sequencing Data.

We compared the performance of four open-source in silico Salmonella typing tools (SeqSero, SeqSero2, Salmonella In Silico Typing Resource [SISTR], and Metric Oriented Sequence Typer [MOST]) to assess their potential for replacing laboratory serological testing with serovar predictions from whole-genome sequencing data. We conducted a retrospective analysis of 1,624 Salmonella isolates of 72 serovars submitted to the German National Salmonella Reference Laboratory between 1999 and 2019. All isolates are derived from animal and foodstuff origins. We conducted Illumina short-read sequencing and compared the in silico serovar prediction results with the results of routine laboratory serotyping. We found the best-performing in silico serovar prediction tool to be SISTR, with 94% correctly typed isolates, followed by SeqSero2 (87%), SeqSero (81%), and MOST (79%). Furthermore, we found that mapping-based tools like SeqSero and SeqSero2 (allele mode) were more reliable for the prediction of monophasic variants, while sequence type and cluster-based methods like MOST and SISTR (core-genome multilocus sequence type [cgMLST]), showed greater resilience when confronted with GC-biased sequencing data. We showed that the choice of library preparation kit could substantially affect O antigen detection, due to the low GC content of the wzx and wzy genes. Although the accuracy of computational serovar predictions is still not quite on par with traditional serotyping by Salmonella reference laboratories, the command-line tools investigated in this study perform a rapid, efficient, inexpensive, and reproducible analysis, which can be integrated into in-house characterization pipelines. Based on our results, we find SISTR most suitable for automated, routine serotyping for public health surveillance of SalmonellaIMPORTANCESalmonella spp. are important foodborne pathogens. To reduce the number of infected patients, it is essential to understand which subtypes of the bacteria cause disease outbreaks. Traditionally, characterization of Salmonella requires serological testing, a laboratory method by which Salmonella isolates can be classified into over 2,600 distinct subtypes, called serovars. Due to recent advances in whole-genome sequencing, many tools have been developed to replace traditional testing methods with computational analysis of genome sequences. It is crucial to validate that these tools, many already in use for routine surveillance, deliver accurate and reliable serovar information. In this study, we set out to compare which of the currently available open-source command-line tools is most suitable to replace serological testing. A thorough evaluation of the differing computational approaches is highly important to ensure the backward compatibility of serotyping data and to maintain comparability between laboratories.

Distantly Related Rotaviruses in Common Shrews, Germany, 2004-2014.

We screened samples from common shrews (Sorex araneus) collected in Germany during 2004-2014 and identified 3 genetically divergent rotaviruses. Virus protein 6 sequence similarities to prototype rotaviruses were low (64.5% rotavirus A, 50.1% rotavirus C [tentative species K], 48.2% rotavirus H [tentative species L]). Shrew-associated rotaviruses might have zoonotic potential.

PAIPline: pathogen identification in metagenomic and clinical next generation sequencing samples.

Motivation: Next generation sequencing (NGS) has provided researchers with a powerful tool to characterize metagenomic and clinical samples in research and diagnostic settings. NGS allows an open view into samples useful for pathogen detection in an unbiased fashion and without prior hypothesis about possible causative agents. However, NGS datasets for pathogen detection come with different obstacles, such as a very unfavorable ratio of pathogen to host reads. Alongside often appearing false positives and irrelevant organisms, such as contaminants, tools are often challenged by samples with low pathogen loads and might not report organisms present below a certain threshold. Furthermore, some metagenomic profiling tools are only focused on one particular set of pathogens, for example bacteria. Results: We present PAIPline, a bioinformatics pipeline specifically designed to address problems associated with detecting pathogens in diagnostic samples. PAIPline particularly focuses on userfriendliness and encapsulates all necessary steps from preprocessing to resolution of ambiguous reads and filtering up to visualization in a single tool. In contrast to existing tools, PAIPline is more specific while maintaining sensitivity. This is shown in a comparative evaluation where PAIPline was benchmarked along other well-known metagenomic profiling tools on previously published well-characterized datasets. Additionally, as part of an international cooperation project, PAIPline was applied to an outbreak sample of hemorrhagic fevers of then unknown etiology. The presented results show that PAIPline can serve as a robust, reliable, user-friendly, adaptable and generalizable stand-alone software for diagnostics from NGS samples and as a stepping stone for further downstream analyses. Availability and implementation: PAIPline is freely available under https://gitlab.com/rki_bioinformatics/paipline.

PriLive: privacy-preserving real-time filtering for next-generation sequencing.

Motivation: In next-generation sequencing, re-identification of individuals and other privacy-breaching strategies can be applied even for anonymized data. This also holds true for applications in which human DNA is acquired as a by-product, e.g. for viral or metagenomic samples from a human host. Conventional data protection strategies including cryptography and post-hoc filtering are only appropriate for the final and processed sequencing data. This can result in an insufficient level of data protection and a considerable time delay in the further analysis workflow. Results: We present PriLive, a novel tool for the automated removal of sensitive data while the sequencing machine is running. Thereby, human sequence information can be detected and removed before being completely produced. This facilitates the compliance with strict data protection regulations. The unique characteristic to cause almost no time delay for further analyses is also a clear benefit for applications other than data protection. Especially if the sequencing data are dominated by known background signals, PriLive considerably accelerates consequent analyses by having only fractions of input data. Besides these conceptual advantages, PriLive achieves filtering results at least as accurate as conventional post-hoc filtering tools. Availability and implementation: PriLive is open-source software available at https://gitlab.com/rki_bioinformatics/PriLive. Supplementary information: Supplementary data are available at Bioinformatics online.

LiveKraken--real-time metagenomic classification of illumina data.

Motivation: In metagenomics, Kraken is one of the most widely used tools due to its robustness and speed. Yet, the overall turnaround time of metagenomic analysis is hampered by the sequential paradigm of wet and dry lab. In urgent experiments, it can be crucial to gain a timely insight into a dataset. Results: Here, we present LiveKraken, a real-time read classification tool based on the core algorithm of Kraken. LiveKraken uses streams of raw data from Illumina sequencers to classify reads taxonomically. This way, we are able to produce results identical to those of Kraken the moment the sequencer finishes. We are furthermore able to provide comparable results in early stages of a sequencing run, allowing saving up to a week of sequencing time on an Illumina HiSeq in High Throughput Mode. While the number of classified reads grows over time, false classifications appear in negligible numbers and proportions of identified taxa are only affected to a minor extent. Availability and implementation: LiveKraken is available at https://gitlab.com/rki_bioinformatics/LiveKraken. Supplementary information: Supplementary data are available at Bioinformatics online.

HiLive: real-time mapping of illumina reads while sequencing.

Motivation: Next Generation Sequencing is increasingly used in time critical, clinical applications. While read mapping algorithms have always been optimized for speed, they follow a sequential paradigm and only start after finishing of the sequencing run and conversion of files. Since Illumina machines write intermediate output results, HiLive performs read mapping while still sequencing and thereby drastically reduces crucial overall sample analysis time, e.g. in precision medicine. Methods: We present HiLive as a novel real time read mapper that implements a k-mer based alignment strategy. HiLive continuously reads intermediate BCL files produced by Illumina sequencers and then extends initial k-mer matches by increasingly produced data from the sequencer. Results: We applied HiLive on real human transcriptome data to show that final read alignments are reported within few minutes after the end of a full Illumina HiSeq 1500 run, while already the necessary conversion to FASTQ files as the standard input to current read mapping methods takes roughly five times as long. Further, we show on simulated and real data that HiLive has comparable accuracy to recent read mappers. Availability and Implementation: HiLive and its source code are freely available from https://gitlab.com/SimonHTausch/HiLive . Contact: renardB@rki.de. Supplementary information: Supplementary data are available at Bioinformatics online.

Berlin Squirrelpox Virus, a New Poxvirus in Red Squirrels, Berlin, Germany.

Near Berlin, Germany, several juvenile red squirrels (Sciurus vulgaris) were found with moist, crusty skin lesions. Histology, electron microscopy, and cell culture isolation revealed an orthopoxvirus-like infection. Subsequent PCR and genome analysis identified a new poxvirus (Berlin squirrelpox virus) that could not be assigned to any known poxvirus genera.

Genome sequence analysis of a novel rotavirus strain indicates a broad genetic diversity of rotavirus A in shrews.

Rotavirus A (RVA) is an etiologic agent of diarrhea in humans and animals. It shows a high degree of genetic heterogeneity. Although distinct associations of RVA genotypes with certain host species are common, interspecies-transmission has also been described. Recently, RVA strains, which are genetically distinct and cluster basally to all other RVA strains in phylogenetic trees, have been identified in common shrews (Sorex araneus). Here, the genome sequence analysis of another RVA strain (RVA/Common Shrew-wt/GER/KS11-0893/2010/G42P[58]) from a common shrew from Germany is described. Generally, the strain shows low sequence identities to established strains, which is reflected by the assessment of the novel genotypes G42-P[58]-I32-R28-C24-M24-A39-N28-T28-E32-H28 to its genome segments. Specifically, the strain is phylogenetically distant from previously described RVA strains of common shrews, whereas it is more closely related to other avian and mammalian RVA strains including those from Asian house shrews (Suncus murinus). The results indicate that a broad variety of diverse RVA strains can be found in shrews suggesting a significant role of these animals in rotavirus evolution.

Clonal relation between Salmonella enterica subspecies enterica serovar Dublin strains of bovine and food origin in Germany.

Salmonella enterica subspecies enterica serovar Dublin (S. Dublin) is a host-adapted serovar causing enteritis and/or systemic diseases in cattle. As the serovar is not host-restricted, it may cause infections in other animals, including humans with severe illness and higher mortality rates than other non-typhoidal serovars. As human infections are mainly caused by contaminated milk, milk products and beef, information on the genetic relationship of S. Dublin strains from cattle and food should be evaluated. Whole-genome sequencing (WGS) of 144 S. Dublin strains from cattle and 30 strains from food origin was performed. Multilocus sequence typing (MLST) revealed mostly sequence type ST-10 from both, cattle and food isolates. In total, 14 of 30 strains from food origin were clonally related to at least one strain from cattle, as detected by core-genome single nucleotide polymorphisms typing as well as core-genome MLST. The remaining 16 foodborne strains fit into the genome structure of S. Dublin in Germany without outliers. WGS proved to be a powerful tool not only to gain information on the epidemiology of Salmonella strains but also to detect clonal relations between organisms isolated from different stages of production. This study has shown a high genetic correlation between S. Dublin strains from cattle and food and, therefore, the potential to cause human infections. S. Dublin strains of both origins share an almost identical set of virulence factors, emphasizing their potential to cause severe clinical manifestations in animals, but also in humans and thus the need for effective control of S. Dublin in a farm-to-fork strategy.

Genome analysis of the novel putative rotavirus species K.

Rotaviruses are causative agents of diarrhea in humans and animals. Currently, the species rotavirus A-J (RVA-RVJ) and the putative species RVK and RVL are defined, mainly based on their genome sequence identities. RVK strains were first identified in 2019 in common shrews (Sorex aranaeus) in Germany; however, only short sequence fragments were available so far. Here, we analyzed the complete coding regions of strain RVK/shrew-wt/GER/KS14-0241/2013, which showed highest sequence identities with RVC. The amino acid sequence identity of VP6, which is used for rotavirus species definition, reached only 51% with other rotavirus reference strains thus confirming classification of RVK as a separate species. Phylogenetic analyses for the deduced amino acid sequences of all 11 virus proteins showed, that for most of them RVK and RVC formed a common branch within the RVA-like phylogenetic clade. Only the tree for the highly variable NSP4 showed a different branching; however, with very low bootstrap support. Comparison of partial nucleotide sequences of other RVK strains from common shrews of different regions in Germany indicated a high degree of sequence variability (61-97% identity) within the putative species. These RVK strains clustered separately from RVC genotype reference strains in phylogenetic trees indicating diversification of RVK independent from RVC. The results indicate that RVK represents a novel rotavirus species, which is most closely related to RVC.

Whole Genome Sequence Analysis of a Prototype Strain of the Novel Putative Rotavirus Species L.

Rotaviruses infect humans and animals and are a main cause of diarrhea. They are non-enveloped viruses with a genome of 11 double-stranded RNA segments. Based on genome analysis and amino acid sequence identities of the capsid protein VP6, the rotavirus species A to J (RVA-RVJ) have been defined so far. In addition, rotaviruses putatively assigned to the novel rotavirus species K (RVK) and L (RVL) have been recently identified in common shrews (Sorex araneus), based on partial genome sequences. Here, the complete genome sequence of strain KS14/0241, a prototype strain of RVL, is presented. The deduced amino acid sequence for VP6 of this strain shows only up to 47% identity to that of RVA to RVJ reference strains. Phylogenetic analyses indicate a clustering separated from the established rotavirus species for all 11 genome segments of RVL, with the closest relationship to RVH and RVJ within the phylogenetic RVB-like clade. The non-coding genome segment termini of RVL showed conserved sequences at the 5'-end (positive-sense RNA strand), which are common to all rotaviruses, and those conserved among the RVB-like clade at the 3'-end. The results are consistent with a classification of the virus into a novel rotavirus species L.

Genetic and biological characteristics of species A rotaviruses detected in common shrews suggest a distinct evolutionary trajectory.

Species A rotaviruses (RVAs) are important aetiological agents of severe diarrhoea in young children. They are also widely distributed in mammals and birds, and increasing evidence indicates the possibility of zoonotic transmission of RVA strains between animals and humans. Moreover, reassortment of the eleven segments of the RVA genome can result in rapid biological changes and may influence pathogenic properties. Here, the nearly complete genome of an RVA strain from a common shrew (Sorex araneus) was sequenced, which showed high nucleotide sequence similarity to additionally determined partial sequences from common shrew RVAs but only very low identity (below 68 per cent) to RVAs from other animal species and humans. New genotypes were assigned to most genome segments of the novel common shrew RVA strain KS14/269, resulting in the genome constellation G39-P[55]-I27-R26-C22-M22-A37-N26-T26-E30-H26. Phylogenetic analyses clustered the common shrew RVAs as ancestral branches of other mammalian and avian RVAs for most of the genome segments, which is in contrast to the phylogeny of the hosts. Nevertheless, conserved sequences typical for all RVAs were identified at the 5'- and 3'- non-coding segment termini. To explore whether the common shrew RVA can exchange genetic material with other mammalian RVAs by reassortment, a reverse genetics system based on the simian RVA strain SA11 was used. However, no viable reassortants could be rescued by exchanging the VP4-, VP6-, or VP7-encoding genome segment alone or in combinations. It can be concluded that highly divergent RVAs are present in common shrews, indicating an evolution of these viruses largely separated from other mammalian and avian RVAs. The zoonotic potential of the virus seems to be low but needs to be further analysed in future.

PathoLive-Real-Time Pathogen Identification from Metagenomic Illumina Datasets.

Over the past years, NGS has become a crucial workhorse for open-view pathogen diagnostics. Yet, long turnaround times result from using massively parallel high-throughput technologies as the analysis can only be performed after sequencing has finished. The interpretation of results can further be challenged by contaminations, clinically irrelevant sequences, and the sheer amount and complexity of the data. We implemented PathoLive, a real-time diagnostics pipeline for the detection of pathogens from clinical samples hours before sequencing has finished. Based on real-time alignment with HiLive2, mappings are scored with respect to common contaminations, low-entropy areas, and sequences of widespread, non-pathogenic organisms. The results are visualized using an interactive taxonomic tree that provides an easily interpretable overview of the relevance of hits. For a human plasma sample that was spiked in vitro with six pathogenic viruses, all agents were clearly detected after only 40 of 200 sequencing cycles. For a real-world sample from Sudan, the results correctly indicated the presence of Crimean-Congo hemorrhagic fever virus. In a second real-world dataset from the 2019 SARS-CoV-2 outbreak in Wuhan, we found the presence of a SARS coronavirus as the most relevant hit without the novel virus reference genome being included in the database. For all samples, clinically irrelevant hits were correctly de-emphasized. Our approach is valuable to obtain fast and accurate NGS-based pathogen identifications and correctly prioritize and visualize them based on their clinical significance: PathoLive is open source and available on GitLab and BioConda.

Future-proofing and maximizing the utility of metadata: The PHA4GE SARS-CoV-2 contextual data specification package.

BACKGROUND: The Public Health Alliance for Genomic Epidemiology (PHA4GE) (https://pha4ge.org) is a global coalition that is actively working to establish consensus standards, document and share best practices, improve the availability of critical bioinformatics tools and resources, and advocate for greater openness, interoperability, accessibility, and reproducibility in public health microbial bioinformatics. In the face of the current pandemic, PHA4GE has identified a need for a fit-for-purpose, open-source SARS-CoV-2 contextual data standard. RESULTS: As such, we have developed a SARS-CoV-2 contextual data specification package based on harmonizable, publicly available community standards. The specification can be implemented via a collection template, as well as an array of protocols and tools to support both the harmonization and submission of sequence data and contextual information to public biorepositories. CONCLUSIONS: Well-structured, rich contextual data add value, promote reuse, and enable aggregation and integration of disparate datasets. Adoption of the proposed standard and practices will better enable interoperability between datasets and systems, improve the consistency and utility of generated data, and ultimately facilitate novel insights and discoveries in SARS-CoV-2 and COVID-19. The package is now supported by the NCBI's BioSample database.

Decentralized Investigation of Bacterial Outbreaks Based on Hashed cgMLST.

Whole-genome sequencing (WGS)-based outbreak investigation has proven to be a valuable method for the surveillance of bacterial pathogens. Its utility has been successfully demonstrated using both gene-by-gene (cgMLST or wgMLST) and single-nucleotide polymorphism (SNP)-based approaches. Among the obstacles of implementing a WGS-based routine surveillance is the need for an exchange of large volumes of sequencing data, as well as a widespread reluctance to share sequence and metadata in public repositories, together with a lacking standardization of suitable bioinformatic tools and workflows. To address these issues, we present chewieSnake, an intuitive and simple-to-use cgMLST workflow. ChewieSnake builds on the allele calling software chewBBACA and extends it by the concept of allele hashing. The resulting hashed allele profiles can be readily compared between laboratories without the need of a central allele nomenclature. The workflow fully automates the computation of the allele distance matrix, cluster membership, and phylogeny and summarizes all important findings in an interactive HTML report. Furthermore, chewieSnake can join allele profiles generated at different laboratories and identify shared clusters, including a stable and intercommunicable cluster nomenclature, thus facilitating a joint outbreak investigation. We demonstrate the feasibility of the proposed approach with a thorough method comparison using publically available sequencing data for Salmonella enterica. However, chewieSnake is readily applicable to all bacterial taxa, provided that a suitable cgMLST scheme is available. The workflow is freely available as an open-source tool and can be easily installed via conda or docker.

Species-Specific Quality Control, Assembly and Contamination Detection in Microbial Isolate Sequences with AQUAMIS.

Sequencing of whole microbial genomes has become a standard procedure for cluster detection, source tracking, outbreak investigation and surveillance of many microorganisms. An increasing number of laboratories are currently in a transition phase from classical methods towards next generation sequencing, generating unprecedented amounts of data. Since the precision of downstream analyses depends significantly on the quality of raw data generated on the sequencing instrument, a comprehensive, meaningful primary quality control is indispensable. Here, we present AQUAMIS, a Snakemake workflow for an extensive quality control and assembly of raw Illumina sequencing data, allowing laboratories to automatize the initial analysis of their microbial whole-genome sequencing data. AQUAMIS performs all steps of primary sequence analysis, consisting of read trimming, read quality control (QC), taxonomic classification, de-novo assembly, reference identification, assembly QC and contamination detection, both on the read and assembly level. The results are visualized in an interactive HTML report including species-specific QC thresholds, allowing non-bioinformaticians to assess the quality of sequencing experiments at a glance. All results are also available as a standard-compliant JSON file, facilitating easy downstream analyses and data exchange. We have applied AQUAMIS to analyze ~13,000 microbial isolates as well as ~1000 in-silico contaminated datasets, proving the workflow's ability to perform in high throughput routine sequencing environments and reliably predict contaminations. We found that intergenus and intragenus contaminations can be detected most accurately using a combination of different QC metrics available within AQUAMIS.

Outcome of Different Sequencing and Assembly Approaches on the Detection of Plasmids and Localization of Antimicrobial Resistance Genes in Commensal Escherichia coli.

Antimicrobial resistance (AMR) is a major threat to public health worldwide. Currently, AMR typing changes from phenotypic testing to whole-genome sequence (WGS)-based detection of resistance determinants for a better understanding of the isolate diversity and elements involved in gene transmission (e.g., plasmids, bacteriophages, transposons). However, the use of WGS data in monitoring purposes requires suitable techniques, standardized parameters and approved guidelines for reliable AMR gene detection and prediction of their association with mobile genetic elements (plasmids). In this study, different sequencing and assembly strategies were tested for their suitability in AMR monitoring in Escherichia coli in the routines of the German National Reference Laboratory for Antimicrobial Resistances. To assess the outcomes of the different approaches, results from in silico predictions were compared with conventional phenotypic- and genotypic-typing data. With the focus on (fluoro)quinolone-resistant E.coli, five qnrS-positive isolates with multiple extrachromosomal elements were subjected to WGS with NextSeq (Illumina), PacBio (Pacific BioSciences) and ONT (Oxford Nanopore) for in depth characterization of the qnrS1-carrying plasmids. Raw reads from short- and long-read sequencing were assembled individually by Unicycler or Flye or a combination of both (hybrid assembly). The generated contigs were subjected to bioinformatics analysis. Based on the generated data, assembly of long-read sequences are error prone and can yield in a loss of small plasmid genomes. In contrast, short-read sequencing was shown to be insufficient for the prediction of a linkage of AMR genes (e.g., qnrS1) to specific plasmid sequences. Furthermore, short-read sequencing failed to detect certain duplications and was unsuitable for genome finishing. Overall, the hybrid assembly led to the most comprehensive typing results, especially in predicting associations of AMR genes and mobile genetic elements. Thus, the use of different sequencing technologies and hybrid assemblies currently represents the best approach for reliable AMR typing and risk assessment.

What WGS Reveals about Salmonella enterica subsp. enterica in Wildlife in Germany.

The aim of this study was to gain an overview of the genetic diversity of Salmonella found in wildlife in Germany. We were particularly interested in exploring whether wildlife acts as a reservoir of certain serovars/subtypes or antimicrobial resistance (AMR) genes. Moreover, we wanted to explore the potential of Salmonella in spreading from wildlife to livestock and humans. To answer these questions, we sequenced 260 Salmonella enterica subsp. enterica isolates sampled between 2002 and 2020 from wildlife across Germany, using short-read whole genome sequencing. We found, consistent with previous findings, that some Salmonella sequence types are associated with certain animal species, such as S. Choleraesuis ST145 with wild boar and S. Enteritidis ST183 with hedgehogs. Antibiotic resistance was detected in 14.2% of all isolates, with resistance against important WATCH group antibiotics present in a small number of isolates. We further found that wildlife isolates do not form separate phylogenetic clusters distant to isolates from domestic animals and foodstuff, thus indicating frequent transmission events between these reservoirs. Overall, our study shows that Salmonella in German wildlife are diverse, with a low AMR burden and close links to Salmonella populations of farm and food-production environments.