Five years after the outbreak of the SARS-CoV-2 pandemic in 2020, diagnostic laboratories have moved from massive sequencing of thousands of samples to routine surveillance of SARS-CoV-2 cases, as with all other respiratory viruses. Surveillance remains of paramount importance to prevent a further SARS-CoV-2 surge, as the virus has been shown to mutate rapidly and can render available drugs and vaccines ineffective. During the pandemic, several bioinformatics pipelines and workflows have been developed to streamline analysis, shorten turnaround time and ensure reproducibility. As the number of samples decreases, laboratories are moving towards more flexible sequencing strategies and optimizing the cost per sample. However, workflow redesigns, even if individual steps have proven successful time and time again, can lead to challenges when changes in a bioinformatics pipeline are introduced (e.g. version updates, implementation of new features, etc.), a new combination of viral mutations emerge or a change in wet-lab procedures leads to unpredictable results. Here, we present a report of misidentified frameshift mutations in the consensus sequence of SARS-CoV-2, which led to an incorrect assumption of mutations in the spike and nucleocapsid viral proteins with the potential to affect PCR detection or even antigen testing. This investigation exemplifies the need for better awareness of the challenges that can occur even when using routinely applied protocols and analytical workflows and highlights the need for cooperation between experts of NGS, bioinformaticians and decision-makers towards more harmonized data workflows.

OBJECTIVE: This study aimed to employ advanced bioinformatics and modern sequencing approaches to solve a diagnostic problem of persistent Campylobacter spp. molecular detection yet negative culture results from four consecutive stool samples of a previously healthy patient with newly diagnosed selective IgA deficiency and prolonged diarrhoea. METHODS: Metagenomic next-generation sequencing (mNGS) based on short-paired end reads with basic bioinformatic read classification analysis was used at first. Due to ambiguous results, advanced bioinformatics involving contigs construction and classification, reference genome mappings and reads filtering with BBSplit, additionally coupled with metagenomic long-reads sequencing and Full-length 16S rRNA metabarcoding were employed to further elucidate the results. Virulence factors were analysed using the Prokka Genome Annotation tool. Modified classical bacteriology methods were finally used for further clarification. RESULTS: Short-pair end reads analysis identified several Campylobacter species in all four samples. After advanced bioinformatic approaches were applied, candidatus C. infans was suspected as the putative pathogen. This result was further supported by metagenomic long-reads sequencing and Full-length 16S rRNA metabarcoding. Nevertheless, after modifying the culture conditions based on mNGS results, a mixed culture of candidatus C. infans and C.ureolyticus was obtained. Sequencing of the mixed culture resulted in an 87.48% and 73.47% genome coverage of candidatus C. infans and C. ureolyticus, respectively. In the candidatus C. infans genome more virulence factors hits were found than in the C. ureolyticus genome thus supporting the first as the most probable cause of symptoms. CONCLUSION: This study shows the pivotal role and strengths of mNGS in unravelling an unusual case of diarrhoea and demonstrates how mNGS can guide established microbiological methods to improve on current limitations. However, it also emphasises the need for careful interpretation of sequencing data, particularly for closely related bacterial species from clinical samples that are known to support complex microbial communities.

Over the past decade, there have been many improvements in the field of metagenomics, including sequencing technologies, advances in bioinformatics and the development of reference databases, but a one-size-fits-all sequencing and bioinformatics pipeline does not yet seem achievable. In this study, we address the bioinformatics part of the analysis by combining three methods into a three-step workflow that increases the sensitivity and specificity of clinical metagenomics and improves pathogen detection. The individual tools are combined into a user-friendly workflow suitable for analysing short paired-end (PE) and long reads from metagenomics datasets-MetaAll. To demonstrate the applicability of the developed workflow, four complicated clinical cases with different disease presentations and multiple samples collected from different biological sites as well as the CAMI Clinical pathogen detection challenge dataset were used. MetaAll was able to identify putative pathogens in all but one case. In this case, however, traditional microbiological diagnostics were also unsuccessful. In addition, co-infection with Haemophilus influenzae and Human rhinovirus C54 was detected in case 1 and co-infection with SARS-Cov-2 and Influenza A virus (FluA) subtype H3N2 was detected in case 3. In case 2, in which conventional diagnostics could not find a pathogen, mNGS pointed to Klebsiella pneumoniae as the suspected pathogen. Finally, this study demonstrated the importance of combining read classification, contig validation and targeted reference mapping for more reliable detection of infectious agents in clinical metagenome samples.

The emergence and rapid spread of SARS-CoV-2 prompted the global community to identify innovative approaches to diagnose infection and sequence the viral genome because at several points in the pandemic positive case numbers exceeded the laboratory capacity to characterize sufficient samples to adequately respond to the spread of emerging variants. From week 10, 2020, to week 13, 2023, Slovenian routine complete genome sequencing (CGS) surveillance network yielded 41 537 complete genomes and revealed a typical molecular epidemiology with early lineages gradually being replaced by Alpha, Delta, and finally Omicron. We developed a targeted next-generation sequencing based variant surveillance strategy dubbed Spike Screen through sample pooling and selective SARS-CoV-2 spike gene amplification in conjunction with CGS of individual cases to increase throughput and cost-effectiveness. Spike Screen identifies variant of concern (VOC) and variant of interest (VOI) signature mutations, analyses their frequencies in sample pools, and calculates the number of VOCs/VOIs at the population level. The strategy was successfully applied for detection of specific VOC/VOI mutations prior to their confirmation by CGS. Spike Screen complemented CGS efforts with an additional 22 897 samples sequenced in two time periods: between week 42, 2020, and week 24, 2021, and between week 37, 2021, and week 2, 2022. The results showed that Spike Screen can be applied to monitor VOC/VOI mutations among large volumes of samples in settings with limited sequencing capacity through reliable and rapid detection of novel variants at the population level and can serve as a basis for public health policy planning.