Correction: NGS read classification using AI.

[This corrects the article DOI: 10.1371/journal.pone.0261548.].

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.

Critical Assessment of Metagenome Interpretation: the second round of challenges.

Evaluating metagenomic software is key for optimizing metagenome interpretation and focus of the Initiative for the Critical Assessment of Metagenome Interpretation (CAMI). The CAMI II challenge engaged the community to assess methods on realistic and complex datasets with long- and short-read sequences, created computationally from around 1,700 new and known genomes, as well as 600 new plasmids and viruses. Here we analyze 5,002 results by 76 program versions. Substantial improvements were seen in assembly, some due to long-read data. Related strains still were challenging for assembly and genome recovery through binning, as was assembly quality for the latter. Profilers markedly matured, with taxon profilers and binners excelling at higher bacterial ranks, but underperforming for viruses and Archaea. Clinical pathogen detection results revealed a need to improve reproducibility. Runtime and memory usage analyses identified efficient programs, including top performers with other metrics. The results identify challenges and guide researchers in selecting methods for analyses.

NGS read classification using AI.

Clinical metagenomics is a powerful diagnostic tool, as it offers an open view into all DNA in a patient's sample. This allows the detection of pathogens that would slip through the cracks of classical specific assays. However, due to this unspecific nature of metagenomic sequencing, a huge amount of unspecific data is generated during the sequencing itself and the diagnosis only takes place at the data analysis stage where relevant sequences are filtered out. Typically, this is done by comparison to reference databases. While this approach has been optimized over the past years and works well to detect pathogens that are represented in the used databases, a common challenge in analysing a metagenomic patient sample arises when no pathogen sequences are found: How to determine whether truly no evidence of a pathogen is present in the data or whether the pathogen's genome is simply absent from the database and the sequences in the dataset could thus not be classified? Here, we present a novel approach to this problem of detecting novel pathogens in metagenomic datasets by classifying the (segments of) proteins encoded by the sequences in the datasets. We train a neural network on the sequences of coding sequences, labeled by taxonomic domain, and use this neural network to predict the taxonomic classification of sequences that can not be classified by comparison to a reference database, thus facilitating the detection of potential novel pathogens.

Fostering global data sharing: highlighting the recommendations of the Research Data Alliance COVID-19 working group.

The systemic challenges of the COVID-19 pandemic require cross-disciplinary collaboration in a global and timely fashion. Such collaboration needs open research practices and the sharing of research outputs, such as data and code, thereby facilitating research and research reproducibility and timely collaboration beyond borders. The Research Data Alliance COVID-19 Working Group recently published a set of recommendations and guidelines on data sharing and related best practices for COVID-19 research. These guidelines include recommendations for clinicians, researchers, policy- and decision-makers, funders, publishers, public health experts, disaster preparedness and response experts, infrastructure providers from the perspective of different domains (Clinical Medicine, Omics, Epidemiology, Social Sciences, Community Participation, Indigenous Peoples, Research Software, Legal and Ethical Considerations), and other potential users. These guidelines include recommendations for researchers, policymakers, funders, publishers and infrastructure providers from the perspective of different domains (Clinical Medicine, Omics, Epidemiology, Social Sciences, Community Participation, Indigenous Peoples, Research Software, Legal and Ethical Considerations). Several overarching themes have emerged from this document such as the need to balance the creation of data adherent to FAIR principles (findable, accessible, interoperable and reusable), with the need for quick data release; the use of trustworthy research data repositories; the use of well-annotated data with meaningful metadata; and practices of documenting methods and software. The resulting document marks an unprecedented cross-disciplinary, cross-sectoral, and cross-jurisdictional effort authored by over 160 experts from around the globe. This letter summarises key points of the Recommendations and Guidelines, highlights the relevant findings, shines a spotlight on the process, and suggests how these developments can be leveraged by the wider scientific community.

An environment for sustainable research software in Germany and beyond: current state, open challenges, and call for action.

Research software has become a central asset in academic research. It optimizes existing and enables new research methods, implements and embeds research knowledge, and constitutes an essential research product in itself. Research software must be sustainable in order to understand, replicate, reproduce, and build upon existing research or conduct new research effectively. In other words, software must be available, discoverable, usable, and adaptable to new needs, both now and in the future. Research software therefore requires an environment that supports sustainability. Hence, a change is needed in the way research software development and maintenance are currently motivated, incentivized, funded, structurally and infrastructurally supported, and legally treated. Failing to do so will threaten the quality and validity of research. In this paper, we identify challenges for research software sustainability in Germany and beyond, in terms of motivation, selection, research software engineering personnel, funding, infrastructure, and legal aspects. Besides researchers, we specifically address political and academic decision-makers to increase awareness of the importance and needs of sustainable research software practices. In particular, we recommend strategies and measures to create an environment for sustainable research software, with the ultimate goal to ensure that software-driven research is valid, reproducible and sustainable, and that software is recognized as a first class citizen in research. This paper is the outcome of two workshops run in Germany in 2019, at deRSE19 - the first International Conference of Research Software Engineers in Germany - and a dedicated DFG-supported follow-up workshop in Berlin.

Seasonal recurrence of cowpox virus outbreaks in captive cheetahs (Acinonyx jubatus).

Cowpox virus infections in captive cheetahs (Acinonyx jubatus) with high morbidity and mortality have already been reported in the UK and Russia in the 1970s. However, most of the reported cases have been singular events. Here, we report a total of five cowpox virus outbreaks in cheetahs in the same safari park in Denmark between 2010 and 2014. Nine cheetahs showed varying severity of clinical disease; two of them died (22%). All episodes occurred between August and October of the respective year. No other carnivores kept at the same institution nor the keepers taking care of the animals were clinically affected. The clinical picture of cowpox was confirmed by extensive laboratory investigations including histopathological and molecular analyses as well as cell culture isolation of a cowpox virus. High anti-orthopoxvirus antibody titers were detected in all 9 diseased cheetahs compared to seven contact cheetahs without clinical signs and 13 cheetahs not in direct contact. Additionally, whole genome sequencing from one sample of each cluster with subsequent phylogenetic analysis showed that the viruses from different outbreaks have individual sequences but clearly form a clade distinct from other cowpox viruses. However, the intra-clade distances are still larger than those usually observed within clades of one event. These findings indicate multiple and separate introductions of cowpox virus, probably from wild rodent populations, where the virus keeps circulating naturally and is only sporadically introduced into the cheetahs. Sero-positivity of voles (Arvicola amphibious) caught in zoo grounds strengthens this hypothesis. As a consequence, recommendations are given for medical and physical management of diseased cheetahs, for hygienic measures as well as for pre-shipment isolation before cheetah export from zoo grounds.

Rapid characterisation of Klebsiella oxytoca isolates from contaminated liquid hand soap using mass spectrometry, FTIR and Raman spectroscopy.

Microbiological monitoring of consumer products and the efficiency of early warning systems and outbreak investigations depend on the rapid identification and strain characterisation of pathogens posing risks to the health and safety of consumers. This study evaluates the potential of three rapid analytical techniques for identification and subtyping of bacterial isolates obtained from a liquid hand soap product, which has been recalled and reported through the EU RAPEX system due to its severe bacterial contamination. Ten isolates recovered from two bottles of the product were identified as Klebsiella oxytoca and subtyped using matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI TOF MS), near-infrared Fourier transform (NIR FT) Raman spectroscopy and Fourier transform infrared (FTIR) spectroscopy. Comparison of the classification results obtained by these phenotype-based techniques with outcomes of the DNA-based methods pulsed-field gel electrophoresis (PFGE), multi-locus sequence typing (MLST) and single nucleotide polymorphism (SNP) analysis of whole-genome sequencing (WGS) data revealed a high level of concordance. In conclusion, a set of analytical techniques might be useful for rapid, reliable and cost-effective microbial typing to ensure safe consumer products and allow source tracking.

RAMBO-K: Rapid and Sensitive Removal of Background Sequences from Next Generation Sequencing Data.

BACKGROUND: The assembly of viral or endosymbiont genomes from Next Generation Sequencing (NGS) data is often hampered by the predominant abundance of reads originating from the host organism. These reads increase the memory and CPU time usage of the assembler and can lead to misassemblies. RESULTS: We developed RAMBO-K (Read Assignment Method Based On K-mers), a tool which allows rapid and sensitive removal of unwanted host sequences from NGS datasets. Reaching a speed of 10 Megabases/s on 4 CPU cores and a standard hard drive, RAMBO-K is faster than any tool we tested, while showing a consistently high sensitivity and specificity across different datasets. CONCLUSIONS: RAMBO-K rapidly and reliably separates reads from different species without data preprocessing. It is suitable as a straightforward standard solution for workflows dealing with mixed datasets. Binaries and source code (java and python) are available from http://sourceforge.net/projects/rambok/.

Multiplex Pyrosequencing®: Simultaneous Genotyping Based on SNPs from Distant Genomic Regions.

Pyrosequencing(®) is a technique that allows the quick sequencing of short stretches of DNA, and results can be inspected in real time as they are generated. As such, it is a highly useful tool for the typing of organisms, based on single-nucleotide polymorphisms (SNPs). However, if a single polymorphism is not sufficient for typing and several distant genomic regions need to be examined, multiple Pyrosequencing reactions have to be performed for each organism. This strongly increases both workload and reagent costs. Alternatively, multiplex Pyrosequencing can be performed, in which the multiple sequencing reactions for all analyzed genomic regions are performed in a single reaction. However, when using this method, special care has to be taken while designing the assay and analyzing the results.Here we present a detailed protocol using our newly developed software mPSQed for assay design and MultiPSQ for data analysis.

Comparative genomic analysis of two novel sporadic Shiga toxin-producing Escherichia coli O104:H4 strains isolated 2011 in Germany.

A large outbreak of gastrointestinal disease occurred in 2011 in Germany which resulted in almost 4000 patients with acute gastroenteritis or hemorrhagic colitis, 855 cases of a hemolytic uremic syndrome and 53 deaths. The pathogen was an uncommon, multiresistant Escherichia coli strain of serotype O104:H4 which expressed a Shiga toxin characteristic of enterohemorrhagic E. coli and in addition virulence factors common to enteroaggregative E. coli. During post-epidemic surveillance of Shiga toxin-producing E. coli (STEC) all but two of O104:H4 isolates were indistinguishable from the epidemic strain. Here we describe two novel STEC O104:H4 strains isolated in close spatiotemporal proximity to the outbreak which show a virulence gene panel, a Shiga toxin-mediated cytotoxicity towards Vero cells and aggregative adherence to Hep-2 cells comparable to the outbreak strain. They differ however both from the epidemic strain and from each other, by their antibiotic resistance phenotypes and some other features as determined by routine epidemiological subtyping methods. Whole genome sequencing of these two strains, of ten outbreak strain isolates originating from different time points of the outbreak and of one historical sporadic EHEC O104:H4 isolate was performed. Sequence analysis revealed a clear phylogenetic distance between the two variant strains and the outbreak strain finally identifying them as epidemiologically unrelated isolates from sporadic cases. These findings add to the knowledge about this emerging pathogen, illustrating a certain diversity within the bacterial core genome as well as loss and gain of accessory elements. Our results do also support the view that distinct new variants of STEC O104:H4 repeatedly might originate from yet unknown reservoirs, rather than that there would be a continuous diversification of a single epidemic strain established and circulating in Germany after the large outbreak in 2011.