One Health Viral Metagenomics for Pandemic Preparedness: Validated mNGS Workflows for Viral Detection and Genome Recovery from Swab and Tissue Specimens

One Health Viral Metagenomics for Pandemic Preparedness: Validated mNGS Workflows for Viral Detection and Genome Recovery from Swab and Tissue Specimens | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article One Health Viral Metagenomics for Pandemic Preparedness: Validated mNGS Workflows for Viral Detection and Genome Recovery from Swab and Tissue Specimens Tristan Russell, Elisa Formiconi, Alison Murphy, Jimmy Hortion, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8563816/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Background Metagenomic next-generation sequencing (mNGS) is an untargeted approach that enables detection of pathogens directly from samples without prior knowledge of their genetic sequences. In the context of pandemic preparedness and One Health surveillance, there is a pressing need for validated viral mNGS workflows that perform reliably across diverse hosts sample types and pre-analytical conditions. Results The study designed and evaluated two mNGS workflows, one for swabs and one for complex tissue matrices, using a reference repository of clinical and post-mortem samples. The panel comprised swabs and tissue samples positive for 19 DNA and RNA viruses (including 12 species) from nine host species and nine anatomical sites, encompassing a range of transport media, storage temperatures and processing timelines. Quality control metrics were embedded throughout nucleic acid extraction, library preparation and sequencing to monitor performance and support interpretation. Overall, 89.5% of 19 known DNA and RNA viruses were detected, including from samples with low nucleic acid concentrations (< 1 ng/µl) and variable integrity and purity. The workflows identified viral co-infections that had not been detected by prior targeted testing, as well as Phocid herpesvirus 7 (PHV7) for which no complete reference genome was initially available. Conclusions These results demonstrate that the validated swab and tissue mNGS workflows are sufficiently robust and sensitive for deployment in investigations of suspected viral disease of unknown aetiology and for early detection of emerging viral threats at the animal–human interface. Metagenomic NGS Virus Detection Pandemic Preparedness Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Pandemic preparedness is a strategic priority for the European Union driven by the substantial morbidity and mortality associated with recent viral emergence events such as SARS-CoV-2 in humans and Avian influenza virus H5N1 in birds and cattle [ 1 ]. Zoonotic spillover between host species has underpinned the major pandemics of the last 50 years including those caused by SARS-CoV-2, HIV and Influenza H1N1 [ 2 ]. In response, the EU and World Health Organisation have adopted One Health strategies to pandemic preparedness and response frameworks, [ 1 , 3 ]. Surveillance initiatives, such as One Health – ALL Ireland for European Surveillance (OH-ALLIES), are being established to build capacity for detection of high-risk viral families circulating in animal populations while also enhancing the ability to discover previously unknown viruses in Ireland [ 4 ]. Within this context, “Pathogen X” and “Pathogen Y” refer to hypothetical, unknown human and veterinary agents, respectively, with pandemic potential and potential to cause significant health and socio-economic disruption [ 4 – 6 ]. Viruses are considered the most plausible Pathogen X or Pathogen Y agents due to their rapid evolution, host switching capacity and ability to alter their virulence before recognition [ 7 – 10 ]. Viral metagenomic next-generation sequencing (mNGS) enables unbiased detection of both known and novel viruses directly from clinical samples addressing gaps in targeted molecular diagnostics, which rely on prior knowledge of pathogen sequences and are not optimum when viral genomes are highly divergent or co-infections and unexpected pathogens are present [ 11 , 12 ]. mNGS has already made important contributions to the detection and characterisation of emerging viral pathogens, exemplified by the identification of Schmallenberg virus as the aetiological agent of large bovine and ovine abortion storms in Europe in 2011 [ 13 ], and the rapid characterisation of SARS-CoV-2 from the first COVID-19 cases in China [ 7 – 10 ], where early sequences facilitated PCR assays[ 14 , 15 ] and vaccine design [ 16 – 18 ], phylogenetic analyses to track viral spread [ 19 , 20 ], and inference of phenotype from genotype [ 9 ]. Co-infections can also be identified using mNGS [ 21 ]. These strengths distinguish mNGS from targeted methods, yet there are several challenges to mNGS-based pathogen discovery and surveillance. Practical concerns, especially in resource-limited settings, include its cost and requirements for specialist equipment, computing and personnel [ 22 – 24 ]. The issue of reduced analytical sensitivity relative to PCR due to low viral fraction in many samples and the need for careful interpretation to ensure specificity. Recently, technical advances in mNGS have made it more accessible[ 22 – 24 ] and various approaches, including methods of host depletion[ 25 – 27 ] and viral enrichment [ 28 , 29 ], have been developed to improve the sensitivity of viral mNGS. There is a particular need for rigorously validated, practical mNGS workflows that can operate reliably on the heterogeneous specimens encountered at the animal–human interface spanning both simple swab matrices and complex tissues. This study develops and validates two optimised mNGS workflows, one for swab samples and the other for complex tissue matrices (Fig. 1 ), each integrating comprehensive quality control (QC) measures, from nucleic acid extraction through library preparation and sequencing to bioinformatic analysis, to support robust virus discovery and characterisation. The methods detailed here cover the quality metrics, performance for detection of known and unknown viruses, and application of these workflows for consensus genome assembly, providing a framework for deployment in One Health surveillance and outbreak investigation. Methods Reference Biospecimen Repository A biospecimen repository of clinical samples positive for known viruses was assembled from five independent sources: the Department of Agriculture, Food and the Marine (DAFM), the Agriculture and Food Development Authority (Teagasc), UCD School of Veterinary Medicine, St Vincent’s University Hospital (SVUH) and Mater Misericordiae University Hospital (MMUH). Eight swab and six tissue samples representing nine anatomical sites, 12 viral species and nine host species were included for method development and evaluation (Table 1 ). All samples from non-human animals were obtained from naturally deceased animals during routine postmortem examinations without the use of anaesthesia or euthanasia. Swab specimens were collected using flocked swabs and a range of transport and storage conditions. DAFM collected swabs into universal transport medium (COPAN, 330C), Teagasc collected swabs (COPAN, 552C) into PrimeStore molecular transport media containing guanidine thiocyanate lysis buffer (Thermo Fisher Scientific, R13905), while SVUH and MMUH collected dry swabs (COPAN, 552C) without transport medium. SVUH and MMUH samples were extracted within 2 days of collection to avoid freeze-thaw cycles. DAFM and Teagasc samples were stored at -80°C and − 20°C, respectively, until extraction (Table 1 ). Tissue biopsies were collected during routine postmortem examinations. Grey seal ( Halichoerus grypus ) carcasses processed by UCD School of Veterinary Medicine were stored at -20°C prior to necropsies after which tissue biopsies were stored at -80°C before processing. Sika deer ( Cervus nippon ) and Asian elephant ( Elephas maximus ) tissues collected from fresh animals by UCD Veterinary Medicine were similarly stored at -80°C before processing. DAFM tissue samples were stored at -80°C and had undergone at least one freeze-thaw cycle before extraction (Table 1 ). Ethical and regulatory oversight was secured through institutional review processes. The UCD Animal Research Ethics Committee granted exemptions for the use of samples collected as part of routine diagnostics or postmortem from DAFM (AREC-E-24-39-Gautier), Teagasc (AREC-E-25-25-Gautier) and UCD School of Veterinary Medicine (AREC-E-22-04-Jahns), and the Human Research Ethics Committee provided approval to work with human clinical samples provided by hospitals (306-LS-CSD-25-Mallon). Table 1 Swab and tissue biospecimens used for viral mNGS workflow validation. Abbreviations: EEHV1A, Elephant endotheliotropic herpesvirus; IBV, Infectious bronchitis virus; MDV, Marek’s Disease virus; OHV2, Ovine herpesvirus 2; MTM, Molecular Transport Media; PHV1/7, Phocine herpesvirus 1/7; PMV1, Pigeon paramyxovirus 1; SBV, Schmallenberg virus; and UTM, Universal Transport Media. 1 Fetal abomasal fluid comprises swallowed amniotic fluid and gastric secretions – the abomasum is the fourth stomach of a ruminant. 2 Sample storage prior to extraction, and in some cases, storage of carcasses before postmortem. 3 Stored at -20°C when collected and transferred to -80°C on receipt. 4 Detected by end-point PCR and confirmed by Sanger sequencing. Sample Matrix Host Anatomical Site Source Date of Sampling Available Details on Animal Condition & Pathology Storage (°C) 2 Freeze-thaw cycles Known Virus (Genome) Detection 1 Swab (MTM) Bos taurus Abomasal fluid 1 Teagasc 02/03/2025 Autolysed Aborted Foetus -20 & -80 3 1 SBV (RNA) qPCR (CT = 32) 2 Swab (UTM) Sus scrofa Large Intestine DAFM 2015 Dead, Diarrhoea -80 ≥ 1 Rotavirus A (RNA) qPCR (CT = 27) Rotavirus B (RNA) qPCR (CT = 31) 3 Swab (UTM) Sus scrofa Small Intestine DAFM 2015 Dead, Diarrhoea -80 ≥ 1 Rotavirus A (RNA) qPCR (CT = 7) Rotavirus B (RNA) qPCR (CT = 17) Rotavirus C (RNA) qPCR (CT = 15) 4 Swab (UTM) Sus scrofa Faecal DAFM 2015 Dead, Diarrhoea -80 ≥ 1 Rotavirus A (RNA) qPCR (CT = 23) Rotavirus C (RNA) qPCR (CT = 22) 5 Swab (Dry) Homo sapiens Skin SVUH 03/2025 Live, Skin Poxes Processed on arrival 0 Mpox IIb (DNA) qPCR (CT = 27) 6 Swab (Dry) Homo sapiens Skin MMUH 07/02/2025 Live, Skin Poxes Processed on arrival 0 Mpox Ia (DNA) qPCR (CT = 24) 7 Swab (Dry) Homo sapiens Skin SVUH 09/2022 Live, Skin Poxes Processed on arrival 0 Mpox IIb (DNA) qPCR (CT=) 8 Swab (UTM) Gallus gallus Trachea DAFM 22/04/2024 Dead -80 ≥ 1 MDV (DNA) qPCR (CT = 33) 9 Tissue Elephas maximus Heart UCD Vet 24/07/2024 Dead, systemic haemorrhages, Intestinal ulcers -20 & -80 3 1 EEHV1A (DNA) PCR + 4 10 Tissue Cervus nippon Liver UCD Vet 19/09/2024 Dead, systemic vasculitis -20 & -80 3 1 OHV2 (DNA) PCR + 4 11 Tissue Columba livia Brain DAFM 01/07/2020 Dead -80 ≥ 1 PMV1 (RNA) qPCR (CT = 30) 12 Tissue Gallus gallus Intestine DAFM 25/01/2024 Dead -80 ≥ 1 IBV (RNA) qPCR (CT = 26) 13 Tissue Halichoerus grypus Brain UCD Vet 08/11/2022 Dead, stranded, mouth ulcers, septicaemia, umbilical abscess -20 & -80 3 2 PHV1 (DNA) PCR + 4 14 Tissue Halichoerus grypus Gingiva UCD Vet 17/03/2024 Dead, Stranded, Pneumonia, Septicaemia -20 & -80 3 2 PHV7 (DNA) PCR + 4 Biosafety and Risk Assessment All work with infectious material was conducted under appropriate biocontainment with risk assessment determining assignment to Biosafety Level 2 or 3 facilities according to national guidelines. Mpox-positive clinical swabs were handled and processed within a dedicated Biosafety Level 3 laboratory while all remaining animal and human diagnostic or postmortem specimens were manipulated under Biosafety Level 2 conditions using standard operating procedures and engineering controls to minimise exposure risk and prevent environmental release Total Nucleic Acid (TNA) Extraction from Swabs Dry Mpox-positive swabs were processed by adding 850 µl Buffer AVL, incubating for 10 minutes, and using 700 µl lysate for TNA extraction with the QIAamp Viral RNA Mini Kit (Qiagen, 52906) following the manufacturer’s instructions with the sole modification of replacing 6 µl carrier RNA with 6 µl linear acrylamide (5 mg/ml, Thermo Fisher Scientific; AM9520). Linear acrylamide was used instead of carrier RNA to prevent sequencing of carrier RNA. All other swabs were processed by vortexing for 5 seconds then 300 µl transport media was used for TNA extraction with the Liferiver Viral RNA Isolation Kit (P20211009) on the Liferiver automated extractor platform, following the manufacturer’s instructions except that 6 µl linear acrylamide (5 mg/ml) was substituted for the carrier RNA. RNA Extraction from Tissues RNA was extracted from tissue samples using the RNeasy Mini Kit (Qiagen, 74106). Tissue was cut on dry-ice into 10–20 mg pieces and transferred into 600 µl Buffer RLT supplemented with 10% β-mercaptoethanol. Samples were sonicated on the high setting of the Biorupter NextGen sonicator (diagenode) for three cycles of 30 second ON and 30 second OFF at 4°C followed by column-based homogenisation (BioTech, HCR003) at 14,000 x g for 120 seconds. RNA was purified using the Qiagen RNeasy Mini Kit and residual DNA was digested with the DNA-free DNA Removal Kit (Thermo Fisher Scientific, AM1906) according to manufacturer’s instructions. Quality Control for Nucleic Acid Extracts Nucleic acid extract purity and quantity was assessed using the ND-1000 NanoDrop spectrometer (Labtech International). RNA concentrations were determined using the Qubit RNA High Sensitivity RNA Kit (Thermo Fisher Scientific, Q32852) and DNA concentrations using the Qubit DNA High Sensitivity Kits (Thermo Fisher Scientific, Q33230). RNA from tissue extracts was assessed using the Bioanalyzer RNA Nano chip (Agilent, 5067 − 1512) or the Tapestation RNA ScreenTape (Agilent, 5067–5579). Swab extracts with concentrations below the limit of detection of these platforms were not subjected to integrity assessment. Double-stranded cDNA Synthesis First-strand cDNA synthesis was performed using SuperScript IV (Thermo Fisher Scientific, 18090050) by combining 11 µl TNA extract with 1 µl 10 mM deoxynucleotides and 1 µl 50 ng/µl random hexamers (Thermo Fisher Scientific, 51709), then incubating at 65°C for 5 minutes. Then, 4 µl 5X SSIV Buffer, 1 µl 100 mM DTT, 1 µl RNase OUT (Thermo Fisher Scientific, 100000840) and 1 µl SuperScript IV enzyme were added to reaction mixes, which were incubated at 23°C for 10 minutes, 50°C for 30 minutes and 80°C for 10 minutes. Second-strand cDNA synthesis was carried out using the Klenow Fragment (Thermo Fisher Scientific, EP0051) by addition of 0.5 µl 10 U per 1 µl Klenow Fragment, 1 µl 10 mM deoxynucleotides, 5 µl Klenow Fragment Buffer and 33.5 µl nuclease-free water to first-strand cDNA synthesis reaction mixes. The reaction was incubated at 37°C for 30 minutes, then heat inactivated at 80°C for 5 minutes. The final product contained genomic DNA and ds-cDNA. cDNA Library Preparation cDNA libraries were generated from 500 pg to 100 ng of RNA using the SMART-Seq Total RNA Pico Input with ZapR (Mammalian) rRNA Depletion Kit (Takara Biosciences, 634357) following the manufacturer’s instructions. Fragmentation times were adapted to RNA integrity metrics: 4 minutes for RIN > 7, 3 minutes for RIN 5–7, 2 minutes for RIN 4–5 with DV200 > 50%, and no fragmentation for RIN < 4 with DV200 30–50%. RNA extracts below the limit of detection of the Bioanalyzer and Tapestation were fragmented for 2 minutes. Following first-strand cDNA synthesis, five PCR cycles were used in first amplification to incorporate adapters and unique dual indexes (Takara Biosciences, 634756), and all purification steps employed NucleoMag NGS Cleanup and Size Select beads (Macherey-Nagel, 744970.50) at a bead:sample ratio of 0.8. Ribosomal RNA was depleted using the ZapR probes provided with the SMART-Seq Kit. A second-round PCR amplification was run for 14 cycles then a final library purification with a bead:sample ratio of 1.0. DNA Library Preparation DNA libraries were generated from 50 pg to 50 ng of DNA (genomic DNA plus ds-cDNA) using the ThruPLEX DNA-Seq Kit (Takara Biosciences, R400674) according to the manufacturer’s instructions. Sequencing adapters and unique dual indexes were incorporated using PCR with tailed primers. Cycle numbers were adjusted based on DNA input: 8 cycles for 30–50 ng, 9 cycles for 10–30 ng, 10 cycles for 3–10 ng, 11 cycles for 1.5-3 ng, 12 cycles for 0.5–1.5 ng, 14 cycles for 0.2–0.5 ng and 16 cycles for 0.05–0.2 ng. Final DNA libraries were purified using bead:sample ratio of 1.0. Libraries Quality Control Library fragment size distribution was assessed using either the Bioanalyzer DNA High Sensitivity Chip (Agilent, 5067 − 4626) or the DNA D1000 TapeStation (Agilent, 5067–5584). Libraries were quantified with the Qubit DNA Broad Range Kit (Thermo Fisher Scientific, Q33260) or Qubit DNA High Sensitivity Kits (Thermo Fisher Scientific, Q33230). Illumina Next-Generation Sequencing Next-Generation Sequencing was performed on the Illumina NextSeq 2000 platform using P2 Cartridges with SBS-XLEAP chemistry and 2X 150 bp paired-end reads (Illumina, 20100985). Libraries were loaded at 600 pM and spiked with 8% PhiX reference genome (Illumina; FC-110-3002). Resulting FASTQ files were deposited on the Sequence Read Archive (Supplementary Table S1 ) under BioProject PRJNA1371775, except for datasets derived from human clinical samples, which were excluded in accordance with ethical approval conditions. Taxonomic Identification with CZID Metagenomic reads were processed using the Chan Zuckerberg ID (CZID) web-based pipeline (version 8.3) for quality control (QC) and taxonomic assignment [ 30 ]. Initial QC steps included trimming low-quality base calls (< Q20) and removing reads with poor quality or low sequence complexity using Trimmomatic [ 31 ] and Price [ 30 ]. Where appropriate host genomes were available, host-derived reads were filtered by mapping against the host genome using Bowtie2 [ 32 ] and samtools [ 33 ]. Minimap2 [ 34 ] and Diamond [ 35 ] were used to perform nucleotide and protein searches against NCBI NT and NR databases (databases from 06/02/2024), respectively, followed by de novo assembly of classified reads into continuous sequences (contigs) with SPAdes [ 36 ]. Bowtie2 [ 32 ] was then used to map reads back to contigs to determine depth. Contigs were then searched against the NCBI NT and NR databases using BLASTN and BLASTX [ 37 ], respectively to refine taxonomic assignements. The resulting output was a table containing a list of taxonomic matches with associated read and contig counts for the NT and NR databases. Genome Assembly For viruses with suitable reference genomes, assemblies were generated by mapping reads to the closest reference sequence using Minimap2 [ 34 ]. Read depth was determined using mosdepth [ 38 ]. Consensus genomes were generated using samtools and bcftools [ 33 ]. Consensus genomes meeting predefined quality threshold (≥ 60% genome coverage and mean read depth ≥ 10X [ 39 ]) were annotated using VAPiD[ 40 ] and deposited on GenBank (Supplementary Table S2). For viruses lacking a close reference genome, an alternative workflow was applied: overlapping reads were first assembled de novo into contigs using SPAdes[ 36 ] passing the “—meta” flag. Diamond[ 35 ] was then used to translate reads into the six open reading frames and perform BLASTX[ 37 ] searches of contigs against the NCBI NR database. A reference sequence with full-genome assembly and consistently one of the best matches from the BLASTX search was selected for carrying out a reference-based TBLASTX[ 37 ] search to identify the coordinates along the genome where contigs mapped. Contig nucleotide sequences were then mapped back to these coordinates to generate a consensus sequence. “N” was assigned to positions with no coverage. Results Quality Control Overview mNGS workflows deployable for passive or active surveillance of DNA and RNA viruses derived from a range of sample matrices, host species and anatomical sites were designed and tested. The mNGS workflows developed within OH-ALLIES were systematically quality-controlled from sample acquisition through nucleic acid extraction, library preparation and sequencing to ensure robust viral detection across diverse relevant specimens (Fig. 1 ). At each step of the mNGS workflow, quantitative and qualitative QC metrics were recorded to monitor process performance, support troubleshooting and document assay robustness for pathogen discovery applications. Sample Acquisition To reflect realistic conditions of an outbreak scenario, where the source of a novel pathogen is uncertain and sample quality, handling and storage are often suboptimal, mNGS workflows were tested with 14 clinical specimens from the OH-ALLIES Reference Biospecimen Repository including eight swabs and six tissue biopsies collected from nine host species and nine anatomical sites (Table 1 ). Across this panel, targeted molecular testing had identified 12 viral species belonging to four RNA virus families ( Coronaviridae , Paramyxoviridae , Peribunyaviridae and Sedoreoviridae ) and two DNA virus families ( Orthoherpesviridae and Poxviridae ), providing a diverse benchmark for mNGS performance (Table 1 ). Sample handling and storage conditions were deliberately heterogenous, reflecting limited control over logistics during an emerging infectious event (Table 1 ). Samples had been stored at -20°C and − 80°C, in some cases for up to 10 years, and experienced between zero and at least two pre-extraction freeze-thaw events (Supplementary Table S3). Despite these constraints, known viruses remained detectable by mNGS from Samples 13 and 14 following repeated freeze-thaw cycles and prolonged periods of storage at -20°C, indicating that the workflows retained sensitivity under suboptimal pre-analytical conditions (Supplementary Table S3). Nucleic Acid Extraction Extracts from swabs and tissues encompassed a wide range of DNA and RNA concentrations (below the limit of detection to high nanogram per microliter levels), purity ratios (260/280 and 260/230) and RNA integrity metrics (RIN and DV200) (Supplementary Table S4). These measurements were used to adjust library preparation parameters, with nucleic acid concentrations informing the number of PCR cycles and RIN/DV200 guiding fragmentation times. Known viral targets, including SBV (sample1), Mpox IIb (Sample 5) and MDV (Sample 8), were consistently detected in extracts with low nucleic acid concentration, suboptimal purity and reduced RNA integrity (Supplementary Table S4). Overall, there was no apparent association between nucleic acid extract concentration or purity and qualitative viral detection by mNGS, demonstrating the robustness of workflows and their tolerance to degraded or impure input material (Fig. 2 A). Library Preparation Library QC focused on concentration and fragment length distribution as both parameters impact data quality and cluster generation on Illumina NGS instruments. All libraries fell within the expected range of fragment length window, (150–500 bp for cDNA libraries and 300–600 bp for DNA libraries) (Fig. 2 B), indicating that integrity-guided fragmentation settings were appropriate for cDNA libraries across the tested extract qualities. Libraries meeting or exceeding the minimum loading concentration (> 600 pM) were generated even from extracts with DNA and RNA concentrations below the limits of detection of high sensitivity fluorometric assays, validating the selected library preparation technologies for low input application. In some instances, library concentrations remained below the detection limit of detection, yet the corresponding datasets still yielded qualitative detection of the expected viruses. NGS Run Performance Sequencing was carried out on an Illumina NextSeq 2000 instrument using the SBS-XLEAP chemistry, selected for their combined capacity to deliver high throughput and high-quality short read data suited for pathogen discovery applications. Across runs, core instrument metrics such as yield, cluster occupancy and percentage of bases above Q30 exceeded manufacturer specifications (Supplementary Table S5), consistent with high-quality library preparation and optimal run set up. A target depth of 50 million reads per sample was applied to accommodate the typically low proportion of viral nucleic acid within total sequence output. For swab samples, this target was distributed equally between corresponding DNA cDNA libraries, with six of eight DNA libraries and five of six cDNA libraries exceeding these targets (Table 2 ). For tissue samples, 50 million reads per cDNA library was the target and this was achieved for five of six libraries (Table 2 ). Reads passing filters (post-run QC) and host filtering varied widely between libraries reflecting differences in sample composition and host background (Table 2 ). Both samples in which the known viruses were not detected by mNGS exceeded 50 million reads with relatively high proportions of passing-filter rates indicating that these metrics alone did not explain occasional false negatives. Table 2 Library-specific metrics for mNGS analysis obtained using CZID and qualitative detection by mNGS. Reads passing filters are those retained following QC and host filtering. Sample Run Library Type Total Reads Reads Passing Filters Detection by mNGS 9 1 cDNA 150,000,000 9,968,190 (6.65%) Yes 10 1 cDNA 141,458,898 11,233,174 (35.76%) Yes 11 1 cDNA 88,911,054 4,896,870 (3.46%) No 12 1 cDNA 150,000,000 2,475,474 (2.22%) Yes 13 1 cDNA 111,705,698 4,357,062 (4.9%) Yes 14 1 cDNA 31,411,964 3,007,886 (2.01%) Yes 6 2 cDNA 139,326,058 2,290,160 (1.64%) Yes 2 DNA 150,000,000 3,881,426 (2.59%) Yes 7 2 DNA 60,006,358 111,800 (0.19%) Yes 8 2 DNA 150,000,000 2,367,062 (1.58%) Yes 1 2 DNA 4,969,268 3,987,534 (80.24%) No 3 cDNA 8,153,070 3,877,596 (47.56%) Yes 2 3 cDNA 88,164,752 15,326,808 (17.38%) 1 of 2 3 DNA 46,143,002 2,231,588 (4.84%) No* 3 3 cDNA 150,000,000 6,399,876 (4.27%) Yes 3 DNA 41,962,422 2,252,322 (5.37%) 1 of 3 4 3 cDNA 110,879,828 10,615,516 (9.57%) 1 of 2 3 DNA 71,164,868 3,439,184 (4.83%) 1 of 2 5 3 cDNA 142,868,726 1,246,670 (66.97%) Yes 3 DNA 1,861,622 9,592,780 (6.71%) Yes Overall Performance Comprehensive metadata records captured sample matrix, host, anatomical site, pre-analytical handling extracts characteristics, library QC steps and run performance, confirming the workflows were stress tested across a broad spectrum of realistic conditions. Across this range qualitative detection of known viruses was achieved from low- and high-quality extracts and libraries providing evidence that mNGS workflows are robust for viral pathogen identification in heterogenous outbreak scenarios. Identification of Known Viruses To develop an NGS-based tool capable of detecting viral causes of infectious disease with unknown aetiology, swab and tissue-specific workflows were evaluated using clinical specimens known to contain diverse viruses. Potential targets include re-emerging viruses with available reference sequences that may escape targeted molecular assays due to phenotypic shifts or sequence variation in regions targeted by PCR, and truly emerging viruses for which reference sequences might not be available. The latter is covered in the “Identification of Unknown Viruses” section and includes PHV7, for which no complete reference genome was initially available. For viruses with reference sequences, untargeted shotgun NGS data were generated and analysed using the CZID metagenomics pipeline which aligns non-host reads against the GenBank nucleotide (NT) database for taxonomic identification (Table 3 ). Only reads specific to the virus taxa were considered and a minimum of two unique reads mapping to distinct genomic loci was required to call a detected virus a true hit consistent with pathogen detection practices [ 41 ]. Virus identification was subsequently verified by mapping reads to appropriate references to assemble consensus genomes as outlined in the “Consensus Genome Assembly” section, providing sequence-level confirmation and enabling downstream characterisation. Table 3 Detection of known viruses. Output from the CZID Metagenomics workflow including number of reads matching known virus, proportion of reads matching known virus (reads per million) and de novo assembled contigs. 1 The PHV7 reference sequence was unavailable in the GenBank NT database (06/02/2024). Sample Known Virus Library CZID Metagenomics Workflow Unique Reads Reads Per Million Contigs 1 SBV cDNA 36 4.4 0 DNA 0 0 0 2 Rotavirus A cDNA 2 0.1 0 DNA 0 0 0 Rotavirus B cDNA 0 0 0 DNA 0 0 0 3 Rotavirus A cDNA 1,735,102 249,386.5 76 DNA 18 7.2 0 Rotavirus B cDNA 78 11.2 1 DNA 0 0 0 Rotavirus C cDNA 44,690 6423.3 17 DNA 0 0 0 4 Rotavirus A cDNA 0 0 0 DNA 42 11.2 0 Rotavirus C cDNA 2 0.2 0 DNA 0 0 0 5 Mpox IIb cDNA 1331 715.0 1 DNA 333,126 2,332 107 6 Mpox Ia cDNA 21 4.5 0 DNA 201 11.2 6 7 Mpox IIb DNA 35,295 588.3 23 8 MDV DNA 2 0.1 0 9 EEHV1A cDNA 11,394 415.8 150 10 OHV2 cDNA 26 1.7 0 11 PMV1 cDNA 0 0 0 12 IBV cDNA 10 3 0 13 PHV1 cDNA 2 0.4 0 14 PHV7 1 cDNA 0 0 0 Known Virus Detection from Swab Specimens For swab specimens, the strategy targeted total nucleic acid to capture both DNA and RNA virus present as whole particles, and therefore both DNA and cDNA libraries were analysed. Across eight swab samples tested containing 12 known viruses, 11 were detected by mNGS with increased qualitative and quantitative detection of RNA and DNA viruses from cDNA and DNA libraries, respectively. Six RNA viruses were detected in cDNA libraries compared to two in DNA libraries and, of the two DNA viruses for which both DNA and cDNA libraries were analysed, there were 333,327 matching reads in DNA libraries compared to 1352 reads in cDNA libraries. One RNA virus (Rotavirus A in Sample 5) was only detected by mNGS analysis of the DNA library. Overall, the increased detection of RNA and DNA viruses from cDNA and DNA libraries, respectively, supports the strategy of dual DNA/RNA analysis for swab-based viral surveillance. Known Virus Detection from Tissue Specimens For tissue samples, a transcriptomics approach was prioritised on the premise that actively replicating viruses would generate viral transcripts in affected organs. Reference sequences for five known tissue-associated viruses were present in the GenBank NT database and four were detected using mNGS, including three herpesviruses and one coronavirus, demonstrating the detection of viral transcripts from DNA and RNA viruses using this workflow (Table 3 ). Successful detection of EEHV1A in heart tissue from an elephant with systemic haemorrhages illustrates how pathological examinations can guide optimal samples selection by targeting organs with macroscopically evident lesions for mNGS analysis (Supplementary Figure S1 ). Overall, the 14 specimens used in this evaluation contained 17 known viruses of which, 15 (88.2%) were identified including 11 of 12 in swab samples and 4 of 5 in tissue samples (Table 3 ). The detection rates support the distinct strategies of analysing DNA and RNA for detection of whole virus particles from swab samples and the transcriptomics approach for detecting actively replicating viruses from tissues. Identification of Unknown Viruses mNGS workflows must be capable of detecting emerging and re-emerging viruses, where there is limited or no prior knowledge of the infectious agent. This spectrum includes “unknown knowns” where reference sequences exist, but the virus has not been identified in a sample; “known unknowns” where a virus is known to be present, but it lacks a complete reference genome; and “unknown unknown”, for which neither prior detection nor reference sequences are available. In this study, secondary infections represented unknown knowns, PHV7 served as an example of known unknown, and the workflows were not explicitly challenged with unknown unknowns. Identification of Unknown Knowns (Co-Infections) Co-infections are representative of known unknowns because, although genetic sequences of these viruses are available, their presence in samples was previously unknown. 12 secondary infections were identified across 5 samples (Table 4 ) applying the criteria of at least 2 unique reads mapping to genetic references. Seven enteric viral pathogens were identified in Sample 3 with high read counts matching their respective sequences. Three of these enteric viruses were also observed in Sample 4, although Sapelovirus A was deemed a false-positive because all reads mapped to a single genomic locus. Molluscum contagiosum virus, Epstein Barr virus (EBV) and Herpes simplex virus 2 (HSV2) were identified as potential co-infections in Samples 5, 6 and 7, respectively, with Molluscum contagiosum virus supported by 1676 reads compared with 44 and 8 reads for HSV2 and EBV, respectively. Table 4 Viral co-infection. Metagenomic workflow details were obtained through analysis with the CZID metagenomics pipeline. Sample Virus Library Metagenomics Workflow Mapped Reads Reads Per Million Contigs 3 Porcine astrovius 4 cDNA 352,724 50,697.1 36 DNA 469 188.6 6 Sapporo virus cDNA 188,694 27,121.0 6 DNA 820 329.8 3 Sapelovirus A cDNA 31,774 4566.9 21 DNA 69 27.8 3 Aichivirus C cDNA 25,729 3698.0 2 DNA 877 352.7 9 Porcine torovirus cDNA 18,412 2646.4 6 DNA 76 30.6 3 Enterovirus G cDNA 1424 204.7 18 DNA 2 0.8 0 Teschovirus A cDNA 1158 166.4 11 DNA 6 2.4 0 4 Aichivirus C DNA 26 6.9 1 Sapporo virus DNA 2 0.5 0 5 Molluscum contagiosum virus DNA 1676 11.7 2 6 EBV DNA 8 0.4 0 7 HSV2 DNA 44 0.7 1 Identification of Known Unknowns (PHV7) Known unknowns had previously been identified in samples, but complete genome reference sequences were unavailable in the NT database when analysis was carried out. PHV7 exemplified this category because panherpesvirus PCR and Sanger sequencing had confirmed PHV7 presence, yet its complete genome sequence was unavailable. Viruses diverge more at the nucleotide level and share more similarity at the amino acid level. As such, an amino acid search was carried out to identify PHV7 by translating reads in all six open reading frames and searching translated sequences against the NR database. Hits to members of the Gammaherpesvirus genus in the NR database that did not match to the NT database, were interpreted as specific evidence for PHV7. PHV7 was not identified when mNGS reads were searched against the NT database, although 18 reads mapped to other species belonging to the Gammaherpesvirus genus. In contrast, BLASTX searches against the NR database identified 2,625 reads matching the Gammaherpesvirus genus proteins. A partial consensus genome sequence was assembled from the metatranscriptomic data, which covered approximately 4.17% of the PHV7 genome (Supplementary Figure S2). Conclusion These mNGS workflows are intended for deployment in scenarios where prior knowledge of circulating viruses and reference sequences may be incomplete or absent. Detection of 12 secondary infections demonstrates the capacity to identify unknown knowns, while recovery of Gammaherpesvirus genus sequences corresponding to PHV7 illustrate that the mNGS workflows can also detect and partially characterise known unknowns. Consensus Genome Assembly Compared to targeted approaches such as PCR and ELISA, mNGS generates rich sequence data, which can be used to assemble partial or complete consensus genomes. These assemblies support the design of targeted diagnostics and mRNA vaccines, enable phylogenetic analysis to track transmission and evolution, and allow inference of phenotypic features from genotype. Consensus genomes were also used to verify viruses identified by mNGS by assessing read distribution across reference genomes. Consensus genomes were assembled by mapping reads to reference sequences with Minimap2 [ 34 ], generating consensus genomes with samtools and bcftools [ 33 ] and measuring read depth with mosdepth [ 38 ] (Table 5 ). Typically more reads mapped to viral sequences during this targeted re-alignment step (Table 5 ) than were counted by CZID metagenomic pipeline (Table 4 ), reflecting inclusion of non-taxonomically informative reads and more efficient read alignment when the reference genome is provided. Genome Assembly for Verification of Virus Hits Genome-wide coverage profiles were used to distinguish true viral detections from false positive. For most non-segmented virus, coverage plots showed reads mapped to multiple regions of the genome consistent with genuine infections (Fig. 3 ). In contrast, all Sapelovirus A reads from sample 4, mapped to a single location along its reference genome (Fig. 3 ), failing the criterion of at least 2 unique reads mapping to distinct positions, and this signal was therefore classified as false positive. For the segmented genomes – SBV and Rotavirus – genomes were assembled using segment-specific reference sequences (Supplementary Table S6) and coverage plots demonstrated mapping of reads across multiple segments for each Rotavirus (Fig. 4 ), supporting their classification as true positives. Although reads only matched to the L segment of SBV, reads mapped to multiple loci of this segment (Fig. 4 ). Table 5 1X consensus genomes. Viruses detected with the metagenomic workflow were mapped against reference sequences using Minimap2. The total genome metrics are shown for segmented viruses with a detailed breakdown between segments in Supplementary Table S5. Sample Known Virus Library Consensus Genome Ref. Accession No. of Reads 1X Coverage (%) Mean Depth 1 SBV cDNA Table S5 36 1.7 0.44 2 Rotavirus A cDNA Table S5 18 3.4 0.09 3 Rotavirus A cDNA Table S5 24,916,386 85.3 104,903.8 DNA Table S5 133 14.59 0.65 Rotavirus B cDNA Table S5 362 4.1 4.99 Rotavirus C cDNA Table S5 22,446,004 80.5 95,253.5 DNA Table S5 341 16.7 2.19 Rotavirus C cDNA Table S5 5 1.5 0.02 Porcine astrovirus 4 cDNA KX060808.1 1,463,725 90.2 30,307.1 Sapporo virus cDNA MK962340.1 818,991 93.2 15,239.8 Sapelovirus A cDNA MN836683.1 230,101 92.4 4138.12 Aichivirus C cDNA LC210609.1 342,572 100 5861.0 Porcine torovirus cDNA LT900503.1 429,422 98.2 2161.81 Enterovirus G cDNA MF782664.1 9514 83.4 163.1 Teschovirus A cDNA JQ429405.1 6715 62.9 127.08 4 Rotavirus A DNA Table S5 341 16.7 2.19 Rotavirus C cDNA Table S5 5 1.5 0.02 Aichivirus C DNA LC210609.1 204 58.5 3.3 Sapelovirus A DNA MN836683.1 6 3.8 0.11 Sapporo virus DNA MK962340.1 22 12.6 0.38 5 Mpox IIb cDNA LC852831.1 2438 0.94 1.21 DNA LC852831.1 371,820 93.4 261.5 Molluscum contagiosum virus DNA MH320554.1 366,593 4.44 60.8 6 Mpox Ia cDNA LC852831.1 1036 18.19 0.66 DNA OZ254457.1 4234 52.9 1.8 EBV DNA NC_007605.1 243 1.54 0.066 7 Mpox IIb DNA LC852831.1 39,631 99.9 23.2 HSV2 DNA KY922721.1 3225 2.91 0.67 8 MDV DNA NC_075702.1 348,573 1.2 190.5 9 EEHV1A cDNA KC618527.1 6,666,011 77.5 312.3 10 OHV2 cDNA PV231823.1 869 6.8 0.46 12 IBV cDNA ON350837.1 27,549 21.8 31.1 13 PHV1 cDNA OK032545.1 103 4.4 0.095 Genome Assembly for Annotation High-quality genomes suitable for downstream analyses were those with at least 75% genome coverage at 1x and mean read depth of at least 10 (Table 5 ) [ 39 ]. For nine non-segmented viruses meeting these criteria, coverage plots showed uniformly high depth across the genome length, except Enterovirus G from Sample 3, which had several gaps and areas of low coverage (Fig. 5 ). All other consensus genomes, which had at least 60% coverage at ≥ 10X read depth (the reduced coverage from 75% to 60% was caused by increased read depth requirements), were annotated with VAPiD [ 40 ] and uploaded to GenBank (Supplementary Table 2). For segmented viruses, coverage plots revealed uneven depth and coverage among segments (Fig. 6 ). Consensus genomes were annotated with VAPiD [ 40 ] and submitted to GenBank for segments with at least 60% coverage at ≥ 10X read depth (Table 6). Discussion In preparation for future viral outbreaks of unknown aetiology, mNGS workflows were designed and evaluated for swab and tissue samples positive for a range of known DNA and RNA viruses, collected from multiple anatomical sites and host species and subjected to diverse handling and storage timelines. QC analyses confirmed that these clinical specimens provided nucleic acid extracts spanning a wide range of concentrations, purity and integrity, yet the workflow remained sufficiently robust to detect viruses from poor-quality extracts. Applying the threshold of at least 2 unique matching reads for nucleotide-level searches (NT) and at least 10 unique matching reads for amino acid-level searches (NR), 89.5% of the 19 known viruses were detected, demonstrating that the selected extraction, library preparation and sequencing protocols are suitable for viral mNGS. Detection of previously unrecognised or poorly characterised viruses is a key requirement of any mNGS-based pathogen surveillance system. The workflows established here generated data of sufficient quality to identify additional viruses in samples that had not previously tested positive for these agents, illustrating the capacity to detect previously unrecognised co-infections (“unknown knowns”). In addition, members of the Gammaherpesvirus genus were identified in Sample 14 using an amino acid search of the NR database, consistent with the presence of PHV7 – a virus lacking complete reference genome in the NT database. The logical next step is to apply these mNGS workflows to cases of suspected infectious disease of unknown aetiology, to assess performance in identifying truly novel or unanticipated viral infection, including unknown unknowns [ 42 ]. Assembly of viral consensus genomes was used to verify metagenomic hits by examining how reads distributed along reference genomes, enabling the identification of potential false positives. Identification of potential false positives in this study highlights the importance of complementing automated metagenomic classification with an independent verification step or other orthogonal methods. High-quality consensus genomes were generated for several of the primary and secondary viral infections identified. An important advantage of mNGS over targeted virus identification methods, such as PCR and ELISA, is the breadth of genetic information obtained, which can be leveraged for downstream applications. Previous studies have shown how consensus genomes assembled from mNGS data can support further analyses by both the originating investigators and the wider research community once deposited in public open access repositories [ 12 ]. The workflows presented here can generate data of sufficient quality for the assembly of genomes with at least 65% coverage at 10X depth or greater, providing a foundation for applications such as phylogenetic analysis, molecular epidemiology, and exploration of genotype-phenotype relationships [ 43 ]. Previous studies have described mNGS workflows for viral pathogen identification across multiple sample matrices and body sites [ 26 ]. The present study extends this by demonstrating for the first time, an mNGS workflow tested on clinical samples from multiple host species, while explicitly documenting its robustness across nucleic acid extracts of varying concentrations, purity and integrity. Collectively, the tissue and swab mNGS workflows developed here have proven effective at detecting known viruses, previously unrecognised infections (unknown knowns), and partially characterised agents (known unknowns) from a spectrum of clinically relevant samples. These workflows are now ready for integration into passive and syndromic surveillance networks to evaluate their performance in real-world investigations of suspected infectious disease cases with unknown aetiology. Conclusion Assembly of consensus genomes from mNGS data enabled verification of viral hits from the metagenomics pipeline based on the mapping of reads to multiple loci along reference genomes. High-quality consensus genomes were also assembled and deposited to GenBank. Declarations Ethics approval and consent to participate Ethical and regulatory oversight was secured through institutional review processes. The UCD Animal Research Ethics Committee granted exemptions for the use of samples collected as part of routine diagnostics or postmortem from DAFM (AREC-E-24-39-Gautier), Teagasc (AREC-E-25-25-Gautier) and UCD School of Veterinary Medicine (AREC-E-22-04-Jahns), and the Human Research Ethics Committee provided approval to work with human clinical samples provided by hospitals (306-LS-CSD-25-Mallon). The use of human samples complied with the Declaration of Helsinki. The participants provided their written informed consent for the collection of samples and clinical data for further research and publication. Consent for publication Not applicable Availability of data and materials The datasets generated during the current study are available in the Sequence Read Archive repository, https://www.ncbi.nlm.nih.gov/sra/PRJNA1371775 Competing interests The authors declare that they have no competing interests Funding Co-funded by the European Union EU4Health Programme 2021-2027 (grant agreement No. 101132970, EU4H-2022-DGA-MS-IBA3), supporting the "One Health – ALL Ireland for European Surveillance (OH-ALLIES)" project. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Health and Digital Executive Agency, the granting authority. Neither the European Union nor the granting authority can be held responsible for them. Authors' contributions TR designed experiments, performed laboratory experiments for data acquisition, analysed and interpreted data and wrote the manuscript. EF performed laboratory experiments for data acquisition and reviewed manuscript drafts. AM designed experiments. JH performed laboratory experiments for data acquisition. MM conceptualised the project and obtained porcine gastrointestinal samples for data acquisition. MC conceptualised the project. LGC obtained avian samples for data acquisition. JFM obtained aborted foetus samples for data acquisition. HJ obtained elephant, seal and deer samples for data acquisition. CK, JB and ERF obtained human samples for data acquisition. PWGM conceptualised the project. VWG conceptualised the project, designed the project, interpreted the data and wrote and reviewed the drafts. Acknowledgements The authors wish to thank all study participants and their families for their participation and support in the conduct of the All Ireland Infectious Diseases Cohort Study. References Samarasekera U. New EU health programme comes into force. Lancet. 2021;397:1252–3. https://doi.org/10.1016/S0140-6736(21)00772-8 . Shanmugaraj B, Kothalam R, Tharik MS, Azeeze A. A brief overview on the threat of zoonotic viruses. Microbes Infect Dis. 2024;0:0–0. https://doi.org/10.21608/MID.2024.294905.1975 . Finch A, Vora NM, Hassan L, Walzer C, Plowright RK, Alders R, et al. The promise and compromise of the WHO Pandemic Agreement for spillover prevention and One Health. Lancet. 2025;0. https://doi.org/10.1016/S0140-6736(25)00632-4 . Berezowski J, De Balogh K, Dórea FC, Ruegg S, Broglia A, Zancanaro G, et al. Coordinated surveillance system under the One Health approach for cross-border pathogens that threaten the Union – options for sustainable surveillance strategies for priority pathogens. EFSA J. 2023;21:e07882. https://doi.org/10.2903/J.EFSA.2023.7882 . WHO to identify pathogens that could cause future outbreaks. and pandemics. https://www.who.int/news/item/21-11-2022-who-to-identify-pathogens-that-could-cause-future-outbreaks-and-pandemics . Accessed 5 Nov 2025. Chatterjee P, Nair P, Chersich M, Terefe Y, Chauhan AS, Quesada F, et al. One Health, Disease X & the challenge of Unknown Unknowns. Indian J Med Res. 2021;153:264. https://doi.org/10.4103/IJMR.IJMR_601_21 . Chan JF-W, Kok K-H, Zhu Z, Chu H, To KK-W, Yuan S, et al. Genomic characterization of the 2019 novel human-pathogenic coronavirus isolated from a patient with atypical pneumonia after visiting Wuhan. Emerg Microbes Infect. 2020;9:221. https://doi.org/10.1080/22221751.2020.1719902 . Zhou P, Yang X-L, Wang X-G, Hu B, Zhang L, Zhang W, et al. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature. 2020;579:270. https://doi.org/10.1038/S41586-020-2012-7 . Wu F, Zhao S, Yu B, Chen YM, Wang W, Song ZG, et al. A new coronavirus associated with human respiratory disease in China. Nat 2020. 2020;579:7798. https://doi.org/10.1038/s41586-020-2008-3 . Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020;395:497. https://doi.org/10.1016/S0140-6736(20)30183-5 . Enrique O, Montaguth T, Buddle S, Morfopoulou S, Breuer J. Clinical metagenomics for diagnosis and surveillance of viral pathogens. Nat Reviews Microbiol 2025. 2025;1–15. https://doi.org/10.1038/s41579-025-01223-5 . Russell T, Formiconi E, Casey M, McElroy M, Mallon PWG, Gautier VW. Viral Metagenomic Next-Generation Sequencing for One Health Discovery and Surveillance of (Re)Emerging Viruses: A Deep Review. Int J Mol Sci 2025. 2025;26(9831):26:9831. https://doi.org/10.3390/IJMS26199831 . Hoffmann B, Scheuch M, Höper D, Jungblut R, Holsteg M, Schirrmeier H, et al. Novel Orthobunyavirus in Cattle, Europe, 2011. Emerg Infect Dis. 2012;18:469. https://doi.org/10.3201/EID1803.111905 . Corman VM, Landt O, Kaiser M, Molenkamp R, Meijer A, Chu DKW, et al. Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR. Eurosurveillance. 2020;25:2000045. https://doi.org/10.2807/1560-7917.ES.2020.25.3.2000045/CITE/REFWORKS . team E editorial. Erratum for Euro Surveill. 2020;25(3). Eurosurveillance. 2021;26:210204e. https://doi.org/10.2807/1560-7917.ES.2021.26.5.210204E Sharma O, Sultan AA, Ding H, Triggle CR. A Review of the Progress and Challenges of Developing a Vaccine for COVID-19. Front Immunol. 2020;11:585354. https://doi.org/10.3389/FIMMU.2020.585354/BIBTEX . Mahase E. Covid-19: Moderna applies for US and EU approval as vaccine trial reports 94.1% efficacy. BMJ. 2020;371. https://doi.org/10.1136/BMJ.M4709 . Mahase E. Covid-19: Vaccine candidate may be more than 90% effective, interim results indicate. BMJ. 2020;371:m4347. https://doi.org/10.1136/BMJ.M4347 . Bogner P, Capua I, Cox NJ, Lipman DJ. A global initiative on sharing avian flu data. Nat 2006. 2006;442:7106. https://doi.org/10.1038/442981a . Hadfield J, Megill C, Bell SM, Huddleston J, Potter B, Callender C, et al. NextStrain: Real-time tracking of pathogen evolution. Bioinformatics. 2018;34:4121–3. https://doi.org/10.1093/BIOINFORMATICS/BTY407 . Sardi SI, Somasekar S, Naccache SN, Bandeira AC, Tauro LB, Campos GS, et al. Coinfections of zika and chikungunya viruses in bahia, Brazil, identified by metagenomic next-generation sequencing. J Clin Microbiol. 2016;54:2348–53. https://doi.org/10.1128/JCM.00877-16/ASSET/85E2DAE7-50FD-4543-A324-B4E04CAD76DF/ASSETS/GRAPHIC/ZJM9990951420002.JPEG . Pronyk PM, de Alwis R, Rockett R, Basile K, Boucher YF, Pang V, et al. Advancing pathogen genomics in resource-limited settings. Cell Genomics. 2023;3:100443. https://doi.org/10.1016/J.XGEN.2023.100443 . Hong NTT, Anh NT, Mai NTH, Nghia HDT, Nhu LNT, Thanh TT, et al. Performance of Metagenomic Next-Generation Sequencing for the Diagnosis of Viral Meningoencephalitis in a Resource-Limited Setting. Open Forum Infect Dis. 2020;7. https://doi.org/10.1093/OFID/OFAA046 . Yek C, Pacheco AR, Vanaerschot M, Bohl JA, Fahsbender E, Aranda-Díaz A, et al. Metagenomic pathogen sequencing in resource-scarce settings: Lessons learned and the road ahead. Front Epidemiol. 2022;2:926695. https://doi.org/10.3389/FEPID.2022.926695/BIBTEX . Greninger AL, Chen EC, Sittler T, Scheinerman A, Roubinian N, Yu G, et al. A Metagenomic Analysis of Pandemic Influenza A (2009 H1N1) Infection in Patients from North America. PLoS ONE. 2010;5:e13381. https://doi.org/10.1371/JOURNAL.PONE.0013381 . Fourgeaud J, Regnault B, Ok V, Da Rocha N, Sitterlé É, Mekouar M, et al. Performance of clinical metagenomics in France: a prospective observational study. Lancet Microbe. 2024;5:e52–61. https://doi.org/10.1016/S2666-5247(23)00244-6 . Ogunbayo AE, Sabiu S, Nyaga MM. Evaluation of extraction and enrichment methods for recovery of respiratory RNA viruses in a metagenomics approach. J Virol Methods. 2023;314:114677. https://doi.org/10.1016/J.JVIROMET.2023.114677 . Mao W, Wang J, Li T, Wu J, Wang J, Wen S, et al. Pathogens 2025. Page 264. 2025;14:14:264. https://doi.org/10.3390/PATHOGENS14030264 . Hybrid Capture-Based Sequencing Enables Highly Sensitive Zoonotic Virus Detection Within the One Health Framework. Mourik K, Sidorov I, Carbo EC, van der Meer D, Boot A, Kroes ACM, et al. Comparison of the performance of two targeted metagenomic virus capture probe-based methods using reference control materials and clinical samples. J Clin Microbiol. 2024;62. https://doi.org/10.1128/JCM.00345-24/SUPPL_FILE/JCM.00345-24-S0002.XLSX . Kalantar KL, Carvalho T, De Bourcy CFA, Dimitrov B, Dingle G, Egger R, et al. IDseq—An open source cloud-based pipeline and analysis service for metagenomic pathogen detection and monitoring. Gigascience. 2020;9:1–14. https://doi.org/10.1093/GIGASCIENCE/GIAA111 . Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114. https://doi.org/10.1093/BIOINFORMATICS/BTU170 . Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357. https://doi.org/10.1038/NMETH.1923 . Danecek P, Bonfield JK, Liddle J, Marshall J, Ohan V, Pollard MO, et al. Twelve years of SAMtools and BCFtools. Gigascience. 2021;10:1–4. https://doi.org/10.1093/GIGASCIENCE/GIAB008 . Li H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics. 2018;34:3094. https://doi.org/10.1093/BIOINFORMATICS/BTY191 . Buchfink B, Reuter K, Drost HG. Sensitive protein alignments at tree-of-life scale using DIAMOND. Nat Methods 2021. 2021;18:4. https://doi.org/10.1038/s41592-021-01101-x . Prjibelski A, Antipov D, Meleshko D, Lapidus A, Korobeynikov A. Using SPAdes De Novo Assembler. Curr Protoc Bioinf. 2020;70:e102. https://doi.org/10.1002/CPBI.102 . Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, et al. BLAST+: architecture and applications. BMC Bioinformatics. 2009;10:421. https://doi.org/10.1186/1471-2105-10-421 . Pedersen BS, Quinlan AR. Mosdepth: quick coverage calculation for genomes and exomes. Bioinformatics. 2018;34:867–8. https://doi.org/10.1093/BIOINFORMATICS/BTX699 . ECDC. Sequencing of SARS-CoV-2: first update. 2021. Shean RC, Makhsous N, Stoddard GD, Lin MJ, Greninger AL. VAPiD: A lightweight cross-platform viral annotation pipeline and identification tool to facilitate virus genome submissions to NCBI GenBank. BMC Bioinformatics. 2019;20:48. https://doi.org/10.1186/S12859-019-2606-Y/TABLES/1 . Liu B, Shao N, Wang J, Zhou SY, Su HX, Dong J, et al. An Optimized Metagenomic Approach for Virome Detection of Clinical Pharyngeal Samples With Respiratory Infection. Front Microbiol. 2020;11:1552. https://doi.org/10.3389/FMICB.2020.01552/FULL . Ashraf S, Jerome H, Bugembe DL, Ssemwanga D, Byaruhanga T, Kayiwa JT, et al. Uncovering the viral aetiology of undiagnosed acute febrile illness in Uganda using metagenomic sequencing. Nat Commun 2025. 2025;16:1. https://doi.org/10.1038/s41467-025-57696-8 . Morfopoulou S, Buddle S, Torres Montaguth OE, Atkinson L, Guerra-Assunção JA, Moradi Marjaneh M, et al. Genomic investigations of unexplained acute hepatitis in children. Nat 2023. 2023;617:7961. https://doi.org/10.1038/s41586-023-06003-w . Additional Declarations No competing interests reported. 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19:08:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8563816/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8563816/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100408549,"identity":"03844ea8-ddbe-4b83-a82b-03ce3119617f","added_by":"auto","created_at":"2026-01-16 13:06:20","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7782130,"visible":true,"origin":"","legend":"","description":"","filename":"PathogenYManuscriptREVISED.docx","url":"https://assets-eu.researchsquare.com/files/rs-8563816/v1/bf438cc8270d0b53d8b82656.docx"},{"id":100408390,"identity":"3fe83e74-0108-4cf4-82e0-cce661409779","added_by":"auto","created_at":"2026-01-16 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13:06:25","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":534212,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8563816/v1/8420c9eb89410f00cbed846d.png"},{"id":100408762,"identity":"00a4a28a-25ee-446c-9a48-a10864ffe1d5","added_by":"auto","created_at":"2026-01-16 13:06:31","extension":"png","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":768678,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8563816/v1/19d30469006f3ad09a25365f.png"},{"id":100408770,"identity":"e0fcc136-acd8-4c1e-ab66-389bf7033af7","added_by":"auto","created_at":"2026-01-16 13:06:31","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":456909,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8563816/v1/954035a25c48be9c2ab22451.png"},{"id":100408262,"identity":"0788714d-dc67-42c7-936d-c68a4837ec80","added_by":"auto","created_at":"2026-01-16 13:05:51","extension":"xml","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":195394,"visible":true,"origin":"","legend":"","description":"","filename":"916324b996854cfea267ffea73aff9561structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8563816/v1/408a48a70f1a4491cd1f8e04.xml"},{"id":100408325,"identity":"65a3d3c2-f916-45fd-8f77-26eb84d30df0","added_by":"auto","created_at":"2026-01-16 13:05:59","extension":"html","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":212046,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8563816/v1/8eb8099008ced0835237a4a5.html"},{"id":100408757,"identity":"62b0454e-b245-4f8c-9b4c-6f185a84112f","added_by":"auto","created_at":"2026-01-16 13:06:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2558928,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eWorkflows for mNGS of clinical samples. The approaches for swab and tissue samples are shown [26]. The swab workflow has been designed to detect whole virus particles, so DNA and RNA were analysed by mNGS. A transcriptomics approach has been applied for tissue samples because actively replicating virus that generates transcripts would be expected. The schematic indicates the critical QC metrics during the workflow, included knowledge of freeze-thaw cycles and storage temperatures and when nucleic acid concentration, purity and integrity is assessed using the Qubit, Nanodrop and Bioanalyzer, respectively. Approximate time to run each step and overall costs per sample are provided. Created with BioRender.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8563816/v1/b1642ff7ed32aa27fb374c44.png"},{"id":100408392,"identity":"f41b9c14-c2f1-4770-8cea-390f2578d101","added_by":"auto","created_at":"2026-01-16 13:06:04","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":245027,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDetection of known viruses by mNGS relative to extract and library measurements. A) Detection of known viruses relative to extract concentration (y-axis) and purity (x-axis). The 260/280 range of pure nucleic acid (1.8-2.2) is indicated by the dashed vertical lines. B) Detection of known viruses relative to library concentration (y-axis) and fragment length (x-axis). The recommended fragment length ranges for cDNA and DNA libraries are indicated with dashed vertical lines.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8563816/v1/5ed8fe4d26b3af8926b3b4c4.jpeg"},{"id":100408230,"identity":"fa8a6a99-fc59-464c-8bf3-6e0e7c1479d2","added_by":"auto","created_at":"2026-01-16 13:05:48","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2016548,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eCoverage plots of non-segmented viruses detected by mNGS. Reads were aligned to references using Minimap2 and depth of coverage was determined using mosdepth.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8563816/v1/6b85ee2a9c86389cb6cc872f.jpeg"},{"id":100408414,"identity":"b073d710-0934-40de-b514-15552af86015","added_by":"auto","created_at":"2026-01-16 13:06:13","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2861988,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eCoverage plots of segmented viruses detected by mNGS. Reads were aligned to references of each segment using Minimap2 and depth of coverage was determined using mosdepth.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8563816/v1/7f6dcee69d19408453406ca9.jpeg"},{"id":100408343,"identity":"d445e8d2-745e-4bdc-a85e-00dce841b113","added_by":"auto","created_at":"2026-01-16 13:06:01","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2161342,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eCoverage plots of non-segmented viruses detected by mNGS and with ≥75% 1X genome coverage at a mean read depth ≥10.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8563816/v1/66b7d6a0f3a4c57f29db6cc0.jpeg"},{"id":100408689,"identity":"a4cbdcf9-0a26-45e2-ba8f-beaec3bd42f2","added_by":"auto","created_at":"2026-01-16 13:06:24","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1822415,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eCoverage plots of segmented viruses detected by mNGS and with ≥75% 1X genome coverage at a mean read depth ≥10.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8563816/v1/bb022bcb281218c707b8d4bc.jpeg"},{"id":100414836,"identity":"b74ec61f-9dbf-4490-9df5-d26ac9101644","added_by":"auto","created_at":"2026-01-16 13:20:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":12228450,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8563816/v1/d76c8ba5-2327-43ac-8ce4-54e87ebb16fb.pdf"},{"id":100408393,"identity":"3830c757-6f10-4904-89df-1b4a46ab02ed","added_by":"auto","created_at":"2026-01-16 13:06:04","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1267694,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary161225.docx","url":"https://assets-eu.researchsquare.com/files/rs-8563816/v1/e242c310525dd75caf019472.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"One Health Viral Metagenomics for Pandemic Preparedness: Validated mNGS Workflows for Viral Detection and Genome Recovery from Swab and Tissue Specimens","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePandemic preparedness is a strategic priority for the European Union driven by the substantial morbidity and mortality associated with recent viral emergence events such as SARS-CoV-2 in humans and Avian influenza virus H5N1 in birds and cattle [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Zoonotic spillover between host species has underpinned the major pandemics of the last 50 years including those caused by SARS-CoV-2, HIV and Influenza H1N1 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn response, the EU and World Health Organisation have adopted One Health strategies to pandemic preparedness and response frameworks, [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Surveillance initiatives, such as One Health \u0026ndash; ALL Ireland for European Surveillance (OH-ALLIES), are being established to build capacity for detection of high-risk viral families circulating in animal populations while also enhancing the ability to discover previously unknown viruses in Ireland [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Within this context, \u0026ldquo;Pathogen X\u0026rdquo; and \u0026ldquo;Pathogen Y\u0026rdquo; refer to hypothetical, unknown human and veterinary agents, respectively, with pandemic potential and potential to cause significant health and socio-economic disruption [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Viruses are considered the most plausible Pathogen X or Pathogen Y agents due to their rapid evolution, host switching capacity and ability to alter their virulence before recognition [\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eViral metagenomic next-generation sequencing (mNGS) enables unbiased detection of both known and novel viruses directly from clinical samples addressing gaps in targeted molecular diagnostics, which rely on prior knowledge of pathogen sequences and are not optimum when viral genomes are highly divergent or co-infections and unexpected pathogens are present [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003emNGS has already made important contributions to the detection and characterisation of emerging viral pathogens, exemplified by the identification of Schmallenberg virus as the aetiological agent of large bovine and ovine abortion storms in Europe in 2011 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and the rapid characterisation of SARS-CoV-2 from the first COVID-19 cases in China [\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], where early sequences facilitated PCR assays[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] and vaccine design [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], phylogenetic analyses to track viral spread [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and inference of phenotype from genotype [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Co-infections can also be identified using mNGS [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. These strengths distinguish mNGS from targeted methods, yet there are several challenges to mNGS-based pathogen discovery and surveillance. Practical concerns, especially in resource-limited settings, include its cost and requirements for specialist equipment, computing and personnel [\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The issue of reduced analytical sensitivity relative to PCR due to low viral fraction in many samples and the need for careful interpretation to ensure specificity. Recently, technical advances in mNGS have made it more accessible[\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] and various approaches, including methods of host depletion[\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] and viral enrichment [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], have been developed to improve the sensitivity of viral mNGS.\u003c/p\u003e \u003cp\u003eThere is a particular need for rigorously validated, practical mNGS workflows that can operate reliably on the heterogeneous specimens encountered at the animal\u0026ndash;human interface spanning both simple swab matrices and complex tissues. This study develops and validates two optimised mNGS workflows, one for swab samples and the other for complex tissue matrices (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), each integrating comprehensive quality control (QC) measures, from nucleic acid extraction through library preparation and sequencing to bioinformatic analysis, to support robust virus discovery and characterisation. The methods detailed here cover the quality metrics, performance for detection of known and unknown viruses, and application of these workflows for consensus genome assembly, providing a framework for deployment in One Health surveillance and outbreak investigation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e \u003cb\u003eReference Biospecimen Repository\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA biospecimen repository of clinical samples positive for known viruses was assembled from five independent sources: the Department of Agriculture, Food and the Marine (DAFM), the Agriculture and Food Development Authority (Teagasc), UCD School of Veterinary Medicine, St Vincent\u0026rsquo;s University Hospital (SVUH) and Mater Misericordiae University Hospital (MMUH). Eight swab and six tissue samples representing nine anatomical sites, 12 viral species and nine host species were included for method development and evaluation (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). All samples from non-human animals were obtained from naturally deceased animals during routine postmortem examinations without the use of anaesthesia or euthanasia.\u003c/p\u003e \u003cp\u003eSwab specimens were collected using flocked swabs and a range of transport and storage conditions. DAFM collected swabs into universal transport medium (COPAN, 330C), Teagasc collected swabs (COPAN, 552C) into PrimeStore molecular transport media containing guanidine thiocyanate lysis buffer (Thermo Fisher Scientific, R13905), while SVUH and MMUH collected dry swabs (COPAN, 552C) without transport medium. SVUH and MMUH samples were extracted within 2 days of collection to avoid freeze-thaw cycles. DAFM and Teagasc samples were stored at -80\u0026deg;C and \u0026minus;\u0026thinsp;20\u0026deg;C, respectively, until extraction (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTissue biopsies were collected during routine postmortem examinations. Grey seal (\u003cem\u003eHalichoerus grypus\u003c/em\u003e) carcasses processed by UCD School of Veterinary Medicine were stored at -20\u0026deg;C prior to necropsies after which tissue biopsies were stored at -80\u0026deg;C before processing. Sika deer (\u003cem\u003eCervus nippon\u003c/em\u003e) and Asian elephant (\u003cem\u003eElephas maximus\u003c/em\u003e) tissues collected from fresh animals by UCD Veterinary Medicine were similarly stored at -80\u0026deg;C before processing. DAFM tissue samples were stored at -80\u0026deg;C and had undergone at least one freeze-thaw cycle before extraction (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e Ethical and regulatory oversight was secured through institutional review processes. The UCD Animal Research Ethics Committee granted exemptions for the use of samples collected as part of routine diagnostics or postmortem from DAFM (AREC-E-24-39-Gautier), Teagasc (AREC-E-25-25-Gautier) and UCD School of Veterinary Medicine (AREC-E-22-04-Jahns), and the Human Research Ethics Committee provided approval to work with human clinical samples provided by hospitals (306-LS-CSD-25-Mallon).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eSwab and tissue biospecimens used for viral mNGS workflow validation. Abbreviations: EEHV1A, Elephant endotheliotropic herpesvirus; IBV, Infectious bronchitis virus; MDV, Marek\u0026rsquo;s Disease virus; OHV2, Ovine herpesvirus 2; MTM, Molecular Transport Media; PHV1/7, Phocine herpesvirus 1/7; PMV1, Pigeon paramyxovirus 1; SBV, Schmallenberg virus; and UTM, Universal Transport Media.\u003c/em\u003e \u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eFetal abomasal fluid comprises swallowed amniotic fluid and gastric secretions \u0026ndash; the abomasum is the fourth stomach of a ruminant.\u003c/em\u003e \u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e \u003cem\u003eSample storage prior to extraction, and in some cases, storage of carcasses before postmortem.\u003c/em\u003e \u003csup\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sup\u003e \u003cem\u003eStored at -20\u0026deg;C when collected and transferred to -80\u0026deg;C on receipt.\u003c/em\u003e \u003csup\u003e\u003cem\u003e4\u003c/em\u003e\u003c/sup\u003e \u003cem\u003eDetected by end-point PCR and confirmed by Sanger sequencing.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMatrix\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHost\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnatomical Site\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDate of Sampling\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAvailable Details on Animal Condition \u0026amp; Pathology\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eStorage (\u0026deg;C)\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eFreeze-thaw cycles\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eKnown Virus (Genome)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eDetection\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSwab (MTM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eBos taurus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAbomasal fluid\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTeagasc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e02/03/2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAutolysed Aborted Foetus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-20 \u0026amp; -80\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSBV (RNA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eqPCR (CT\u0026thinsp;=\u0026thinsp;32)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSwab (UTM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eSus scrofa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLarge Intestine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDAFM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDead, Diarrhoea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eRotavirus A (RNA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eqPCR (CT\u0026thinsp;=\u0026thinsp;27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eRotavirus B (RNA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eqPCR (CT\u0026thinsp;=\u0026thinsp;31)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSwab (UTM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003eSus scrofa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSmall Intestine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eDAFM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eDead, Diarrhoea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e-80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eRotavirus A (RNA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eqPCR (CT\u0026thinsp;=\u0026thinsp;7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eRotavirus B (RNA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eqPCR (CT\u0026thinsp;=\u0026thinsp;17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eRotavirus C (RNA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eqPCR (CT\u0026thinsp;=\u0026thinsp;15)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSwab (UTM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eSus scrofa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFaecal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDAFM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDead, Diarrhoea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eRotavirus A (RNA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eqPCR (CT\u0026thinsp;=\u0026thinsp;23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eRotavirus C (RNA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eqPCR (CT\u0026thinsp;=\u0026thinsp;22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSwab (Dry)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eHomo sapiens\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSkin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSVUH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e03/2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLive, Skin Poxes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eProcessed on arrival\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eMpox IIb (DNA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eqPCR (CT\u0026thinsp;=\u0026thinsp;27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSwab (Dry)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eHomo sapiens\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSkin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMMUH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e07/02/2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLive, Skin Poxes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eProcessed on arrival\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eMpox Ia (DNA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eqPCR (CT\u0026thinsp;=\u0026thinsp;24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSwab (Dry)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eHomo sapiens\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSkin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSVUH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e09/2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLive, Skin Poxes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eProcessed on arrival\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eMpox IIb (DNA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eqPCR (CT=)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSwab (UTM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eGallus gallus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTrachea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDAFM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22/04/2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eMDV (DNA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eqPCR (CT\u0026thinsp;=\u0026thinsp;33)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTissue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eElephas maximus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHeart\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUCD Vet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24/07/2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDead, systemic haemorrhages, Intestinal ulcers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-20 \u0026amp; -80\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eEEHV1A (DNA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003ePCR\u0026thinsp;+\u0026thinsp;\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTissue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCervus nippon\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLiver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUCD Vet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19/09/2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDead, systemic vasculitis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-20 \u0026amp; -80\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOHV2 (DNA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003ePCR\u0026thinsp;+\u0026thinsp;\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTissue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eColumba livia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBrain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDAFM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e01/07/2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePMV1 (RNA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eqPCR (CT\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTissue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eGallus gallus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIntestine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDAFM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25/01/2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eIBV (RNA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eqPCR (CT\u0026thinsp;=\u0026thinsp;26)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTissue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eHalichoerus grypus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBrain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUCD Vet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e08/11/2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDead, stranded, mouth ulcers, septicaemia, umbilical abscess\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-20 \u0026amp; -80\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePHV1 (DNA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003ePCR\u0026thinsp;+\u0026thinsp;\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTissue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eHalichoerus grypus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGingiva\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUCD Vet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17/03/2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDead, Stranded, Pneumonia, Septicaemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-20 \u0026amp; -80\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePHV7 (DNA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003ePCR\u0026thinsp;+\u0026thinsp;\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eBiosafety and Risk Assessment\u003c/h2\u003e \u003cp\u003eAll work with infectious material was conducted under appropriate biocontainment with risk assessment determining assignment to Biosafety Level 2 or 3 facilities according to national guidelines. Mpox-positive clinical swabs were handled and processed within a dedicated Biosafety Level 3 laboratory while all remaining animal and human diagnostic or postmortem specimens were manipulated under Biosafety Level 2 conditions using standard operating procedures and engineering controls to minimise exposure risk and prevent environmental release\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTotal Nucleic Acid (TNA) Extraction from Swabs\u003c/h3\u003e\n\u003cp\u003eDry Mpox-positive swabs were processed by adding 850 \u0026micro;l Buffer AVL, incubating for 10 minutes, and using 700 \u0026micro;l lysate for TNA extraction with the QIAamp Viral RNA Mini Kit (Qiagen, 52906) following the manufacturer\u0026rsquo;s instructions with the sole modification of replacing 6 \u0026micro;l carrier RNA with 6 \u0026micro;l linear acrylamide (5 mg/ml, Thermo Fisher Scientific; AM9520). Linear acrylamide was used instead of carrier RNA to prevent sequencing of carrier RNA. All other swabs were processed by vortexing for 5 seconds then 300 \u0026micro;l transport media was used for TNA extraction with the Liferiver Viral RNA Isolation Kit (P20211009) on the Liferiver automated extractor platform, following the manufacturer\u0026rsquo;s instructions except that 6 \u0026micro;l linear acrylamide (5 mg/ml) was substituted for the carrier RNA.\u003c/p\u003e\n\u003ch3\u003eRNA Extraction from Tissues\u003c/h3\u003e\n\u003cp\u003eRNA was extracted from tissue samples using the RNeasy Mini Kit (Qiagen, 74106). Tissue was cut on dry-ice into 10\u0026ndash;20 mg pieces and transferred into 600 \u0026micro;l Buffer RLT supplemented with 10% β-mercaptoethanol. Samples were sonicated on the high setting of the Biorupter NextGen sonicator (diagenode) for three cycles of 30 second ON and 30 second OFF at 4\u0026deg;C followed by column-based homogenisation (BioTech, HCR003) at 14,000 x g for 120 seconds. RNA was purified using the Qiagen RNeasy Mini Kit and residual DNA was digested with the DNA-free DNA Removal Kit (Thermo Fisher Scientific, AM1906) according to manufacturer\u0026rsquo;s instructions.\u003c/p\u003e\n\u003ch3\u003eQuality Control for Nucleic Acid Extracts\u003c/h3\u003e\n\u003cp\u003eNucleic acid extract purity and quantity was assessed using the ND-1000 NanoDrop spectrometer (Labtech International). RNA concentrations were determined using the Qubit RNA High Sensitivity RNA Kit (Thermo Fisher Scientific, Q32852) and DNA concentrations using the Qubit DNA High Sensitivity Kits (Thermo Fisher Scientific, Q33230). RNA from tissue extracts was assessed using the Bioanalyzer RNA Nano chip (Agilent, 5067\u0026thinsp;\u0026minus;\u0026thinsp;1512) or the Tapestation RNA ScreenTape (Agilent, 5067\u0026ndash;5579). Swab extracts with concentrations below the limit of detection of these platforms were not subjected to integrity assessment.\u003c/p\u003e\n\u003ch3\u003eDouble-stranded cDNA Synthesis\u003c/h3\u003e\n\u003cp\u003eFirst-strand cDNA synthesis was performed using SuperScript IV (Thermo Fisher Scientific, 18090050) by combining 11 \u0026micro;l TNA extract with 1 \u0026micro;l 10 mM deoxynucleotides and 1 \u0026micro;l 50 ng/\u0026micro;l random hexamers (Thermo Fisher Scientific, 51709), then incubating at 65\u0026deg;C for 5 minutes. Then, 4 \u0026micro;l 5X SSIV Buffer, 1 \u0026micro;l 100 mM DTT, 1 \u0026micro;l RNase OUT (Thermo Fisher Scientific, 100000840) and 1 \u0026micro;l SuperScript IV enzyme were added to reaction mixes, which were incubated at 23\u0026deg;C for 10 minutes, 50\u0026deg;C for 30 minutes and 80\u0026deg;C for 10 minutes.\u003c/p\u003e \u003cp\u003eSecond-strand cDNA synthesis was carried out using the Klenow Fragment (Thermo Fisher Scientific, EP0051) by addition of 0.5 \u0026micro;l 10 U per 1 \u0026micro;l Klenow Fragment, 1 \u0026micro;l 10 mM deoxynucleotides, 5 \u0026micro;l Klenow Fragment Buffer and 33.5 \u0026micro;l nuclease-free water to first-strand cDNA synthesis reaction mixes. The reaction was incubated at 37\u0026deg;C for 30 minutes, then heat inactivated at 80\u0026deg;C for 5 minutes. The final product contained genomic DNA and ds-cDNA.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ecDNA Library Preparation\u003c/h2\u003e \u003cp\u003ecDNA libraries were generated from 500 pg to 100 ng of RNA using the SMART-Seq Total RNA Pico Input with ZapR (Mammalian) rRNA Depletion Kit (Takara Biosciences, 634357) following the manufacturer\u0026rsquo;s instructions. Fragmentation times were adapted to RNA integrity metrics: 4 minutes for RIN\u0026thinsp;\u0026gt;\u0026thinsp;7, 3 minutes for RIN 5\u0026ndash;7, 2 minutes for RIN 4\u0026ndash;5 with DV200\u0026thinsp;\u0026gt;\u0026thinsp;50%, and no fragmentation for RIN\u0026thinsp;\u0026lt;\u0026thinsp;4 with DV200 30\u0026ndash;50%. RNA extracts below the limit of detection of the Bioanalyzer and Tapestation were fragmented for 2 minutes. Following first-strand cDNA synthesis, five PCR cycles were used in first amplification to incorporate adapters and unique dual indexes (Takara Biosciences, 634756), and all purification steps employed NucleoMag NGS Cleanup and Size Select beads (Macherey-Nagel, 744970.50) at a bead:sample ratio of 0.8. Ribosomal RNA was depleted using the ZapR probes provided with the SMART-Seq Kit. A second-round PCR amplification was run for 14 cycles then a final library purification with a bead:sample ratio of 1.0.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDNA Library Preparation\u003c/h3\u003e\n\u003cp\u003eDNA libraries were generated from 50 pg to 50 ng of DNA (genomic DNA plus ds-cDNA) using the ThruPLEX DNA-Seq Kit (Takara Biosciences, R400674) according to the manufacturer\u0026rsquo;s instructions. Sequencing adapters and unique dual indexes were incorporated using PCR with tailed primers. Cycle numbers were adjusted based on DNA input: 8 cycles for 30\u0026ndash;50 ng, 9 cycles for 10\u0026ndash;30 ng, 10 cycles for 3\u0026ndash;10 ng, 11 cycles for 1.5-3 ng, 12 cycles for 0.5\u0026ndash;1.5 ng, 14 cycles for 0.2\u0026ndash;0.5 ng and 16 cycles for 0.05\u0026ndash;0.2 ng. Final DNA libraries were purified using bead:sample ratio of 1.0.\u003c/p\u003e\n\u003ch3\u003eLibraries Quality Control\u003c/h3\u003e\n\u003cp\u003eLibrary fragment size distribution was assessed using either the Bioanalyzer DNA High Sensitivity Chip (Agilent, 5067\u0026thinsp;\u0026minus;\u0026thinsp;4626) or the DNA D1000 TapeStation (Agilent, 5067\u0026ndash;5584). Libraries were quantified with the Qubit DNA Broad Range Kit (Thermo Fisher Scientific, Q33260) or Qubit DNA High Sensitivity Kits (Thermo Fisher Scientific, Q33230).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eIllumina Next-Generation Sequencing\u003c/h2\u003e \u003cp\u003eNext-Generation Sequencing was performed on the Illumina NextSeq 2000 platform using P2 Cartridges with SBS-XLEAP chemistry and 2X 150 bp paired-end reads (Illumina, 20100985). Libraries were loaded at 600 pM and spiked with 8% PhiX reference genome (Illumina; FC-110-3002). Resulting FASTQ files were deposited on the Sequence Read Archive (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) under BioProject PRJNA1371775, except for datasets derived from human clinical samples, which were excluded in accordance with ethical approval conditions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eTaxonomic Identification with CZID\u003c/h2\u003e \u003cp\u003eMetagenomic reads were processed using the Chan Zuckerberg ID (CZID) web-based pipeline (version 8.3) for quality control (QC) and taxonomic assignment [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Initial QC steps included trimming low-quality base calls (\u0026lt;\u0026thinsp;Q20) and removing reads with poor quality or low sequence complexity using Trimmomatic [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] and Price [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Where appropriate host genomes were available, host-derived reads were filtered by mapping against the host genome using Bowtie2 [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] and samtools [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Minimap2 [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] and Diamond [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] were used to perform nucleotide and protein searches against NCBI NT and NR databases (databases from 06/02/2024), respectively, followed by de novo assembly of classified reads into continuous sequences (contigs) with SPAdes [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Bowtie2 [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] was then used to map reads back to contigs to determine depth. Contigs were then searched against the NCBI NT and NR databases using BLASTN and BLASTX [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], respectively to refine taxonomic assignements. The resulting output was a table containing a list of taxonomic matches with associated read and contig counts for the NT and NR databases.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eGenome Assembly\u003c/h2\u003e \u003cp\u003eFor viruses with suitable reference genomes, assemblies were generated by mapping reads to the closest reference sequence using Minimap2 [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Read depth was determined using mosdepth [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Consensus genomes were generated using samtools and bcftools [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Consensus genomes meeting predefined quality threshold (\u0026ge;\u0026thinsp;60% genome coverage and mean read depth\u0026thinsp;\u0026ge;\u0026thinsp;10X [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]) were annotated using VAPiD[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] and deposited on GenBank (Supplementary Table S2). For viruses lacking a close reference genome, an alternative workflow was applied: overlapping reads were first assembled \u003cem\u003ede novo\u003c/em\u003e into contigs using SPAdes[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] passing the \u0026ldquo;\u0026mdash;meta\u0026rdquo; flag. Diamond[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] was then used to translate reads into the six open reading frames and perform BLASTX[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] searches of contigs against the NCBI NR database. A reference sequence with full-genome assembly and consistently one of the best matches from the BLASTX search was selected for carrying out a reference-based TBLASTX[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] search to identify the coordinates along the genome where contigs mapped. Contig nucleotide sequences were then mapped back to these coordinates to generate a consensus sequence. \u0026ldquo;N\u0026rdquo; was assigned to positions with no coverage.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eQuality Control Overview\u003c/h2\u003e \u003cp\u003emNGS workflows deployable for passive or active surveillance of DNA and RNA viruses derived from a range of sample matrices, host species and anatomical sites were designed and tested. The mNGS workflows developed within OH-ALLIES were systematically quality-controlled from sample acquisition through nucleic acid extraction, library preparation and sequencing to ensure robust viral detection across diverse relevant specimens (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAt each step of the mNGS workflow, quantitative and qualitative QC metrics were recorded to monitor process performance, support troubleshooting and document assay robustness for pathogen discovery applications.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSample Acquisition\u003c/h2\u003e \u003cp\u003eTo reflect realistic conditions of an outbreak scenario, where the source of a novel pathogen is uncertain and sample quality, handling and storage are often suboptimal, mNGS workflows were tested with 14 clinical specimens from the OH-ALLIES Reference Biospecimen Repository including eight swabs and six tissue biopsies collected from nine host species and nine anatomical sites (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Across this panel, targeted molecular testing had identified 12 viral species belonging to four RNA virus families (\u003cem\u003eCoronaviridae\u003c/em\u003e, \u003cem\u003eParamyxoviridae\u003c/em\u003e, \u003cem\u003ePeribunyaviridae\u003c/em\u003e and \u003cem\u003eSedoreoviridae\u003c/em\u003e) and two DNA virus families (\u003cem\u003eOrthoherpesviridae\u003c/em\u003e and \u003cem\u003ePoxviridae\u003c/em\u003e), providing a diverse benchmark for mNGS performance (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSample handling and storage conditions were deliberately heterogenous, reflecting limited control over logistics during an emerging infectious event (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Samples had been stored at -20\u0026deg;C and \u0026minus;\u0026thinsp;80\u0026deg;C, in some cases for up to 10 years, and experienced between zero and at least two pre-extraction freeze-thaw events (Supplementary Table S3). Despite these constraints, known viruses remained detectable by mNGS from Samples 13 and 14 following repeated freeze-thaw cycles and prolonged periods of storage at -20\u0026deg;C, indicating that the workflows retained sensitivity under suboptimal pre-analytical conditions (Supplementary Table S3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eNucleic Acid Extraction\u003c/h2\u003e \u003cp\u003eExtracts from swabs and tissues encompassed a wide range of DNA and RNA concentrations (below the limit of detection to high nanogram per microliter levels), purity ratios (260/280 and 260/230) and RNA integrity metrics (RIN and DV200) (Supplementary Table S4). These measurements were used to adjust library preparation parameters, with nucleic acid concentrations informing the number of PCR cycles and RIN/DV200 guiding fragmentation times. Known viral targets, including SBV (sample1), Mpox IIb (Sample 5) and MDV (Sample 8), were consistently detected in extracts with low nucleic acid concentration, suboptimal purity and reduced RNA integrity (Supplementary Table S4). Overall, there was no apparent association between nucleic acid extract concentration or purity and qualitative viral detection by mNGS, demonstrating the robustness of workflows and their tolerance to degraded or impure input material (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eLibrary Preparation\u003c/h2\u003e \u003cp\u003eLibrary QC focused on concentration and fragment length distribution as both parameters impact data quality and cluster generation on Illumina NGS instruments. All libraries fell within the expected range of fragment length window, (150\u0026ndash;500 bp for cDNA libraries and 300\u0026ndash;600 bp for DNA libraries) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), indicating that integrity-guided fragmentation settings were appropriate for cDNA libraries across the tested extract qualities.\u003c/p\u003e \u003cp\u003eLibraries meeting or exceeding the minimum loading concentration (\u0026gt;\u0026thinsp;600 pM) were generated even from extracts with DNA and RNA concentrations below the limits of detection of high sensitivity fluorometric assays, validating the selected library preparation technologies for low input application. In some instances, library concentrations remained below the detection limit of detection, yet the corresponding datasets still yielded qualitative detection of the expected viruses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eNGS Run Performance\u003c/h2\u003e \u003cp\u003eSequencing was carried out on an Illumina NextSeq 2000 instrument using the SBS-XLEAP chemistry, selected for their combined capacity to deliver high throughput and high-quality short read data suited for pathogen discovery applications. Across runs, core instrument metrics such as yield, cluster occupancy and percentage of bases above Q30 exceeded manufacturer specifications (Supplementary Table S5), consistent with high-quality library preparation and optimal run set up.\u003c/p\u003e \u003cp\u003eA target depth of 50\u0026nbsp;million reads per sample was applied to accommodate the typically low proportion of viral nucleic acid within total sequence output. For swab samples, this target was distributed equally between corresponding DNA cDNA libraries, with six of eight DNA libraries and five of six cDNA libraries exceeding these targets (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For tissue samples, 50\u0026nbsp;million reads per cDNA library was the target and this was achieved for five of six libraries (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Reads passing filters (post-run QC) and host filtering varied widely between libraries reflecting differences in sample composition and host background (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Both samples in which the known viruses were not detected by mNGS exceeded 50\u0026nbsp;million reads with relatively high proportions of passing-filter rates indicating that these metrics alone did not explain occasional false negatives.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eLibrary-specific metrics for mNGS analysis obtained using CZID and qualitative detection by mNGS. Reads passing filters are those retained following QC and host filtering.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRun\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLibrary Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal Reads\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReads Passing Filters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDetection by mNGS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e150,000,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9,968,190 (6.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e141,458,898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11,233,174 (35.76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88,911,054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4,896,870 (3.46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e150,000,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,475,474 (2.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e111,705,698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4,357,062 (4.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31,411,964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,007,886 (2.01%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e139,326,058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,290,160 (1.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e150,000,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,881,426 (2.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60,006,358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e111,800 (0.19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e150,000,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,367,062 (1.58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4,969,268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,987,534 (80.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8,153,070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,877,596 (47.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88,164,752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15,326,808 (17.38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 of 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46,143,002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,231,588 (4.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e150,000,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6,399,876 (4.27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41,962,422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,252,322 (5.37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 of 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e110,879,828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10,615,516 (9.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 of 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71,164,868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,439,184 (4.83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 of 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e142,868,726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,246,670 (66.97%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,861,622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9,592,780 (6.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eOverall Performance\u003c/h2\u003e \u003cp\u003eComprehensive metadata records captured sample matrix, host, anatomical site, pre-analytical handling extracts characteristics, library QC steps and run performance, confirming the workflows were stress tested across a broad spectrum of realistic conditions. Across this range qualitative detection of known viruses was achieved from low- and high-quality extracts and libraries providing evidence that mNGS workflows are robust for viral pathogen identification in heterogenous outbreak scenarios.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of Known Viruses\u003c/h2\u003e \u003cp\u003eTo develop an NGS-based tool capable of detecting viral causes of infectious disease with unknown aetiology, swab and tissue-specific workflows were evaluated using clinical specimens known to contain diverse viruses. Potential targets include re-emerging viruses with available reference sequences that may escape targeted molecular assays due to phenotypic shifts or sequence variation in regions targeted by PCR, and truly emerging viruses for which reference sequences might not be available. The latter is covered in the \u0026ldquo;Identification of Unknown Viruses\u0026rdquo; section and includes PHV7, for which no complete reference genome was initially available.\u003c/p\u003e \u003cp\u003eFor viruses with reference sequences, untargeted shotgun NGS data were generated and analysed using the CZID metagenomics pipeline which aligns non-host reads against the GenBank nucleotide (NT) database for taxonomic identification (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Only reads specific to the virus taxa were considered and a minimum of two unique reads mapping to distinct genomic loci was required to call a detected virus a true hit consistent with pathogen detection practices [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Virus identification was subsequently verified by mapping reads to appropriate references to assemble consensus genomes as outlined in the \u0026ldquo;Consensus Genome Assembly\u0026rdquo; section, providing sequence-level confirmation and enabling downstream characterisation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eDetection of known viruses. Output from the CZID Metagenomics workflow including number of reads matching known virus, proportion of reads matching known virus (reads per million) and de novo assembled contigs.\u003c/em\u003e \u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e \u003cem\u003eThe PHV7 reference sequence was unavailable in the GenBank NT database (06/02/2024).\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eKnown Virus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLibrary\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eCZID Metagenomics Workflow\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnique Reads\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReads Per Million\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eContigs\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSBV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRotavirus A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRotavirus B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRotavirus A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,735,102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e249,386.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRotavirus B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRotavirus C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44,690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6423.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRotavirus A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRotavirus C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMpox IIb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e715.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e333,126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMpox Ia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMpox IIb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35,295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e588.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMDV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEEHV1A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11,394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e415.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOHV2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePMV1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIBV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePHV1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePHV7\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eKnown Virus Detection from Swab Specimens\u003c/h2\u003e \u003cp\u003eFor swab specimens, the strategy targeted total nucleic acid to capture both DNA and RNA virus present as whole particles, and therefore both DNA and cDNA libraries were analysed. Across eight swab samples tested containing 12 known viruses, 11 were detected by mNGS with increased qualitative and quantitative detection of RNA and DNA viruses from cDNA and DNA libraries, respectively. Six RNA viruses were detected in cDNA libraries compared to two in DNA libraries and, of the two DNA viruses for which both DNA and cDNA libraries were analysed, there were 333,327 matching reads in DNA libraries compared to 1352 reads in cDNA libraries. One RNA virus (Rotavirus A in Sample 5) was only detected by mNGS analysis of the DNA library. Overall, the increased detection of RNA and DNA viruses from cDNA and DNA libraries, respectively, supports the strategy of dual DNA/RNA analysis for swab-based viral surveillance.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eKnown Virus Detection from Tissue Specimens\u003c/h2\u003e \u003cp\u003eFor tissue samples, a transcriptomics approach was prioritised on the premise that actively replicating viruses would generate viral transcripts in affected organs. Reference sequences for five known tissue-associated viruses were present in the GenBank NT database and four were detected using mNGS, including three herpesviruses and one coronavirus, demonstrating the detection of viral transcripts from DNA and RNA viruses using this workflow (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Successful detection of EEHV1A in heart tissue from an elephant with systemic haemorrhages illustrates how pathological examinations can guide optimal samples selection by targeting organs with macroscopically evident lesions for mNGS analysis (Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOverall, the 14 specimens used in this evaluation contained 17 known viruses of which, 15 (88.2%) were identified including 11 of 12 in swab samples and 4 of 5 in tissue samples (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The detection rates support the distinct strategies of analysing DNA and RNA for detection of whole virus particles from swab samples and the transcriptomics approach for detecting actively replicating viruses from tissues.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of Unknown Viruses\u003c/h2\u003e \u003cp\u003emNGS workflows must be capable of detecting emerging and re-emerging viruses, where there is limited or no prior knowledge of the infectious agent. This spectrum includes \u0026ldquo;unknown knowns\u0026rdquo; where reference sequences exist, but the virus has not been identified in a sample; \u0026ldquo;known unknowns\u0026rdquo; where a virus is known to be present, but it lacks a complete reference genome; and \u0026ldquo;unknown unknown\u0026rdquo;, for which neither prior detection nor reference sequences are available. In this study, secondary infections represented unknown knowns, PHV7 served as an example of known unknown, and the workflows were not explicitly challenged with unknown unknowns.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eIdentification of Unknown Knowns (Co-Infections)\u003c/h2\u003e \u003cp\u003eCo-infections are representative of known unknowns because, although genetic sequences of these viruses are available, their presence in samples was previously unknown. 12 secondary infections were identified across 5 samples (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) applying the criteria of at least 2 unique reads mapping to genetic references. Seven enteric viral pathogens were identified in Sample 3 with high read counts matching their respective sequences. Three of these enteric viruses were also observed in Sample 4, although Sapelovirus A was deemed a false-positive because all reads mapped to a single genomic locus. Molluscum contagiosum virus, Epstein Barr virus (EBV) and Herpes simplex virus 2 (HSV2) were identified as potential co-infections in Samples 5, 6 and 7, respectively, with Molluscum contagiosum virus supported by 1676 reads compared with 44 and 8 reads for HSV2 and EBV, respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eViral co-infection. Metagenomic workflow details were obtained through analysis with the CZID metagenomics pipeline.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVirus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLibrary\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eMetagenomics Workflow\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMapped Reads\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReads Per Million\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eContigs\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"13\" rowspan=\"14\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePorcine astrovius 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e352,724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50,697.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e188.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSapporo virus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e188,694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27,121.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e329.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSapelovirus A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31,774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4566.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAichivirus C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25,729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3698.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e352.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePorcine torovirus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18,412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2646.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEnterovirus G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e204.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTeschovirus A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e166.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAichivirus C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSapporo virus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMolluscum contagiosum virus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEBV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHSV2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eIdentification of Known Unknowns (PHV7)\u003c/h2\u003e \u003cp\u003eKnown unknowns had previously been identified in samples, but complete genome reference sequences were unavailable in the NT database when analysis was carried out. PHV7 exemplified this category because panherpesvirus PCR and Sanger sequencing had confirmed PHV7 presence, yet its complete genome sequence was unavailable. Viruses diverge more at the nucleotide level and share more similarity at the amino acid level. As such, an amino acid search was carried out to identify PHV7 by translating reads in all six open reading frames and searching translated sequences against the NR database. Hits to members of the \u003cem\u003eGammaherpesvirus\u003c/em\u003e genus in the NR database that did not match to the NT database, were interpreted as specific evidence for PHV7.\u003c/p\u003e \u003cp\u003ePHV7 was not identified when mNGS reads were searched against the NT database, although 18 reads mapped to other species belonging to the \u003cem\u003eGammaherpesvirus\u003c/em\u003e genus. In contrast, BLASTX searches against the NR database identified 2,625 reads matching the \u003cem\u003eGammaherpesvirus\u003c/em\u003e genus proteins. A partial consensus genome sequence was assembled from the metatranscriptomic data, which covered approximately 4.17% of the PHV7 genome (Supplementary Figure S2).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eConclusion\u003c/h3\u003e\n\u003cp\u003eThese mNGS workflows are intended for deployment in scenarios where prior knowledge of circulating viruses and reference sequences may be incomplete or absent. Detection of 12 secondary infections demonstrates the capacity to identify unknown knowns, while recovery of \u003cem\u003eGammaherpesvirus\u003c/em\u003e genus sequences corresponding to PHV7 illustrate that the mNGS workflows can also detect and partially characterise known unknowns.\u003c/p\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eConsensus Genome Assembly\u003c/h2\u003e \u003cp\u003eCompared to targeted approaches such as PCR and ELISA, mNGS generates rich sequence data, which can be used to assemble partial or complete consensus genomes. These assemblies support the design of targeted diagnostics and mRNA vaccines, enable phylogenetic analysis to track transmission and evolution, and allow inference of phenotypic features from genotype. Consensus genomes were also used to verify viruses identified by mNGS by assessing read distribution across reference genomes. Consensus genomes were assembled by mapping reads to reference sequences with Minimap2 [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], generating consensus genomes with samtools and bcftools [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] and measuring read depth with mosdepth [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Typically more reads mapped to viral sequences during this targeted re-alignment step (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) than were counted by CZID metagenomic pipeline (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), reflecting inclusion of non-taxonomically informative reads and more efficient read alignment when the reference genome is provided.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003eGenome Assembly for Verification of Virus Hits\u003c/h2\u003e \u003cp\u003eGenome-wide coverage profiles were used to distinguish true viral detections from false positive. For most non-segmented virus, coverage plots showed reads mapped to multiple regions of the genome consistent with genuine infections (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In contrast, all Sapelovirus A reads from sample 4, mapped to a single location along its reference genome (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), failing the criterion of at least 2 unique reads mapping to distinct positions, and this signal was therefore classified as false positive.\u003c/p\u003e \u003cp\u003eFor the segmented genomes \u0026ndash; SBV and Rotavirus \u0026ndash; genomes were assembled using segment-specific reference sequences (Supplementary Table S6) and coverage plots demonstrated mapping of reads across multiple segments for each Rotavirus (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), supporting their classification as true positives. Although reads only matched to the L segment of SBV, reads mapped to multiple loci of this segment (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e \u003cem\u003e1X consensus genomes. Viruses detected with the metagenomic workflow were mapped against reference sequences using Minimap2. The total genome metrics are shown for segmented viruses with a detailed breakdown between segments in Supplementary Table S5.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eKnown Virus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLibrary\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e \u003cp\u003eConsensus Genome\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef. Accession\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo. of Reads\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1X Coverage (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMean Depth\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSBV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTable S5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRotavirus A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTable S5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"12\" rowspan=\"13\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRotavirus A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTable S5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24,916,386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e85.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e104,903.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTable S5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRotavirus B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTable S5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRotavirus C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTable S5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22,446,004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e80.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e95,253.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTable S5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRotavirus C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTable S5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePorcine astrovirus 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKX060808.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,463,725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e90.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e30,307.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSapporo virus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMK962340.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e818,991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e93.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15,239.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSapelovirus A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMN836683.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e230,101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e92.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4138.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAichivirus C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC210609.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e342,572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5861.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePorcine torovirus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLT900503.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e429,422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e98.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2161.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnterovirus G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMF782664.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e83.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e163.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTeschovirus A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eJQ429405.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e62.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e127.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRotavirus A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTable S5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRotavirus C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTable S5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAichivirus C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC210609.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e58.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSapelovirus A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMN836683.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSapporo virus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMK962340.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMpox IIb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC852831.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC852831.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e371,820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e93.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e261.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMolluscum contagiosum virus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMH320554.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e366,593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e60.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMpox Ia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC852831.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOZ254457.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e52.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEBV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNC_007605.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMpox IIb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC852831.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e39,631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e99.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e23.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHSV2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKY922721.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMDV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNC_075702.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e348,573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e190.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEEHV1A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKC618527.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6,666,011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e77.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e312.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOHV2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePV231823.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIBV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eON350837.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27,549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e31.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePHV1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOK032545.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGenome Assembly for Annotation\u003c/h3\u003e\n\u003cp\u003eHigh-quality genomes suitable for downstream analyses were those with at least 75% genome coverage at 1x and mean read depth of at least 10 (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. For nine non-segmented viruses meeting these criteria, coverage plots showed uniformly high depth across the genome length, except Enterovirus G from Sample 3, which had several gaps and areas of low coverage (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). All other consensus genomes, which had at least 60% coverage at \u0026ge;\u0026thinsp;10X read depth (the reduced coverage from 75% to 60% was caused by increased read depth requirements), were annotated with VAPiD [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] and uploaded to GenBank (Supplementary Table\u0026nbsp;2).\u003c/p\u003e \u003cp\u003eFor segmented viruses, coverage plots revealed uneven depth and coverage among segments (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Consensus genomes were annotated with VAPiD [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] and submitted to GenBank for segments with at least 60% coverage at \u0026ge;\u0026thinsp;10X read depth (Table\u0026nbsp;6).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn preparation for future viral outbreaks of unknown aetiology, mNGS workflows were designed and evaluated for swab and tissue samples positive for a range of known DNA and RNA viruses, collected from multiple anatomical sites and host species and subjected to diverse handling and storage timelines. QC analyses confirmed that these clinical specimens provided nucleic acid extracts spanning a wide range of concentrations, purity and integrity, yet the workflow remained sufficiently robust to detect viruses from poor-quality extracts. Applying the threshold of at least 2 unique matching reads for nucleotide-level searches (NT) and at least 10 unique matching reads for amino acid-level searches (NR), 89.5% of the 19 known viruses were detected, demonstrating that the selected extraction, library preparation and sequencing protocols are suitable for viral mNGS.\u003c/p\u003e \u003cp\u003eDetection of previously unrecognised or poorly characterised viruses is a key requirement of any mNGS-based pathogen surveillance system. The workflows established here generated data of sufficient quality to identify additional viruses in samples that had not previously tested positive for these agents, illustrating the capacity to detect previously unrecognised co-infections (\u0026ldquo;unknown knowns\u0026rdquo;). In addition, members of the \u003cem\u003eGammaherpesvirus\u003c/em\u003e genus were identified in Sample 14 using an amino acid search of the NR database, consistent with the presence of PHV7 \u0026ndash; a virus lacking complete reference genome in the NT database. The logical next step is to apply these mNGS workflows to cases of suspected infectious disease of unknown aetiology, to assess performance in identifying truly novel or unanticipated viral infection, including unknown unknowns [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAssembly of viral consensus genomes was used to verify metagenomic hits by examining how reads distributed along reference genomes, enabling the identification of potential false positives. Identification of potential false positives in this study highlights the importance of complementing automated metagenomic classification with an independent verification step or other orthogonal methods.\u003c/p\u003e \u003cp\u003eHigh-quality consensus genomes were generated for several of the primary and secondary viral infections identified. An important advantage of mNGS over targeted virus identification methods, such as PCR and ELISA, is the breadth of genetic information obtained, which can be leveraged for downstream applications. Previous studies have shown how consensus genomes assembled from mNGS data can support further analyses by both the originating investigators and the wider research community once deposited in public open access repositories [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The workflows presented here can generate data of sufficient quality for the assembly of genomes with at least 65% coverage at 10X depth or greater, providing a foundation for applications such as phylogenetic analysis, molecular epidemiology, and exploration of genotype-phenotype relationships [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrevious studies have described mNGS workflows for viral pathogen identification across multiple sample matrices and body sites [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The present study extends this by demonstrating for the first time, an mNGS workflow tested on clinical samples from multiple host species, while explicitly documenting its robustness across nucleic acid extracts of varying concentrations, purity and integrity. Collectively, the tissue and swab mNGS workflows developed here have proven effective at detecting known viruses, previously unrecognised infections (unknown knowns), and partially characterised agents (known unknowns) from a spectrum of clinically relevant samples. These workflows are now ready for integration into passive and syndromic surveillance networks to evaluate their performance in real-world investigations of suspected infectious disease cases with unknown aetiology.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAssembly of consensus genomes from mNGS data enabled verification of viral hits from the metagenomics pipeline based on the mapping of reads to multiple loci along reference genomes. High-quality consensus genomes were also assembled and deposited to GenBank.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eEthical and regulatory oversight was secured through institutional review processes. The UCD Animal Research Ethics Committee granted exemptions for the use of samples collected as part of routine diagnostics or postmortem from DAFM (AREC-E-24-39-Gautier), Teagasc (AREC-E-25-25-Gautier) and UCD School of Veterinary Medicine (AREC-E-22-04-Jahns), and the Human Research Ethics Committee provided approval to work with human clinical samples provided by hospitals (306-LS-CSD-25-Mallon). The use of human samples complied with the Declaration of Helsinki. The participants provided their written informed consent for the collection of samples and clinical data for further research and publication.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eThe datasets generated during the current study are available in the Sequence Read Archive repository, https://www.ncbi.nlm.nih.gov/sra/PRJNA1371775\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eCo-funded by the European Union EU4Health Programme 2021-2027 (grant agreement No. 101132970, EU4H-2022-DGA-MS-IBA3), supporting the \u0026quot;One Health \u0026ndash; ALL Ireland for European Surveillance (OH-ALLIES)\u0026quot; project. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Health and Digital Executive Agency, the granting authority. Neither the European Union nor the granting authority can be held responsible for them.\u003c/p\u003e\n\u003ch2 id=\"_Toc217310711\"\u003eAuthors\u0026apos; contributions\u003c/h2\u003e\n\u003cp\u003eTR designed experiments, performed laboratory experiments for data acquisition, analysed and interpreted data and wrote the manuscript. EF performed laboratory experiments for data acquisition and reviewed manuscript drafts. AM designed experiments. JH performed laboratory experiments for data acquisition. MM conceptualised the project and obtained porcine gastrointestinal samples for data acquisition. MC conceptualised the project. LGC obtained avian samples for data acquisition. JFM obtained aborted foetus samples for data acquisition. HJ obtained elephant, seal and deer samples for data acquisition. CK, JB and ERF obtained human samples for data acquisition. PWGM conceptualised the project. VWG conceptualised the project, designed the project, interpreted the data and wrote and reviewed the drafts.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThe authors wish to thank all study participants and their families for their participation and support in the conduct of the All Ireland Infectious Diseases Cohort Study.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSamarasekera U. New EU health programme comes into force. Lancet. 2021;397:1252\u0026ndash;3. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0140-6736(21)00772-8\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(21)00772-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShanmugaraj B, Kothalam R, Tharik MS, Azeeze A. A brief overview on the threat of zoonotic viruses. Microbes Infect Dis. 2024;0:0\u0026ndash;0. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.21608/MID.2024.294905.1975\u003c/span\u003e\u003cspan address=\"10.21608/MID.2024.294905.1975\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFinch A, Vora NM, Hassan L, Walzer C, Plowright RK, Alders R, et al. The promise and compromise of the WHO Pandemic Agreement for spillover prevention and One Health. Lancet. 2025;0. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0140-6736(25)00632-4\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(25)00632-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerezowski J, De Balogh K, D\u0026oacute;rea FC, Ruegg S, Broglia A, Zancanaro G, et al. Coordinated surveillance system under the One Health approach for cross-border pathogens that threaten the Union \u0026ndash; options for sustainable surveillance strategies for priority pathogens. EFSA J. 2023;21:e07882. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2903/J.EFSA.2023.7882\u003c/span\u003e\u003cspan address=\"10.2903/J.EFSA.2023.7882\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO to identify pathogens that could cause future outbreaks. and pandemics. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/news/item/21-11-2022-who-to-identify-pathogens-that-could-cause-future-outbreaks-and-pandemics\u003c/span\u003e\u003cspan address=\"https://www.who.int/news/item/21-11-2022-who-to-identify-pathogens-that-could-cause-future-outbreaks-and-pandemics\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 5 Nov 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChatterjee P, Nair P, Chersich M, Terefe Y, Chauhan AS, Quesada F, et al. One Health, Disease X \u0026amp; the challenge of Unknown Unknowns. Indian J Med Res. 2021;153:264. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4103/IJMR.IJMR_601_21\u003c/span\u003e\u003cspan address=\"10.4103/IJMR.IJMR_601_21\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChan JF-W, Kok K-H, Zhu Z, Chu H, To KK-W, Yuan S, et al. Genomic characterization of the 2019 novel human-pathogenic coronavirus isolated from a patient with atypical pneumonia after visiting Wuhan. Emerg Microbes Infect. 2020;9:221. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/22221751.2020.1719902\u003c/span\u003e\u003cspan address=\"10.1080/22221751.2020.1719902\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou P, Yang X-L, Wang X-G, Hu B, Zhang L, Zhang W, et al. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature. 2020;579:270. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/S41586-020-2012-7\u003c/span\u003e\u003cspan address=\"10.1038/S41586-020-2012-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu F, Zhao S, Yu B, Chen YM, Wang W, Song ZG, et al. A new coronavirus associated with human respiratory disease in China. Nat 2020. 2020;579:7798. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41586-020-2008-3\u003c/span\u003e\u003cspan address=\"10.1038/s41586-020-2008-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020;395:497. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0140-6736(20)30183-5\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(20)30183-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEnrique O, Montaguth T, Buddle S, Morfopoulou S, Breuer J. Clinical metagenomics for diagnosis and surveillance of viral pathogens. Nat Reviews Microbiol 2025. 2025;1\u0026ndash;15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41579-025-01223-5\u003c/span\u003e\u003cspan address=\"10.1038/s41579-025-01223-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRussell T, Formiconi E, Casey M, McElroy M, Mallon PWG, Gautier VW. Viral Metagenomic Next-Generation Sequencing for One Health Discovery and Surveillance of (Re)Emerging Viruses: A Deep Review. Int J Mol Sci 2025. 2025;26(9831):26:9831. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/IJMS26199831\u003c/span\u003e\u003cspan address=\"10.3390/IJMS26199831\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoffmann B, Scheuch M, H\u0026ouml;per D, Jungblut R, Holsteg M, Schirrmeier H, et al. Novel Orthobunyavirus in Cattle, Europe, 2011. Emerg Infect Dis. 2012;18:469. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3201/EID1803.111905\u003c/span\u003e\u003cspan address=\"10.3201/EID1803.111905\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCorman VM, Landt O, Kaiser M, Molenkamp R, Meijer A, Chu DKW, et al. Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR. Eurosurveillance. 2020;25:2000045. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2807/1560-7917.ES.2020.25.3.2000045/CITE/REFWORKS\u003c/span\u003e\u003cspan address=\"10.2807/1560-7917.ES.2020.25.3.2000045/CITE/REFWORKS\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eteam E editorial. Erratum for Euro Surveill. 2020;25(3). Eurosurveillance. 2021;26:210204e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2807/1560-7917.ES.2021.26.5.210204E\u003c/span\u003e\u003cspan address=\"10.2807/1560-7917.ES.2021.26.5.210204E\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharma O, Sultan AA, Ding H, Triggle CR. A Review of the Progress and Challenges of Developing a Vaccine for COVID-19. Front Immunol. 2020;11:585354. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/FIMMU.2020.585354/BIBTEX\u003c/span\u003e\u003cspan address=\"10.3389/FIMMU.2020.585354/BIBTEX\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMahase E. Covid-19: Moderna applies for US and EU approval as vaccine trial reports 94.1% efficacy. BMJ. 2020;371. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/BMJ.M4709\u003c/span\u003e\u003cspan address=\"10.1136/BMJ.M4709\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMahase E. Covid-19: Vaccine candidate may be more than 90% effective, interim results indicate. BMJ. 2020;371:m4347. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/BMJ.M4347\u003c/span\u003e\u003cspan address=\"10.1136/BMJ.M4347\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBogner P, Capua I, Cox NJ, Lipman DJ. A global initiative on sharing avian flu data. Nat 2006. 2006;442:7106. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/442981a\u003c/span\u003e\u003cspan address=\"10.1038/442981a\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHadfield J, Megill C, Bell SM, Huddleston J, Potter B, Callender C, et al. NextStrain: Real-time tracking of pathogen evolution. Bioinformatics. 2018;34:4121\u0026ndash;3. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/BIOINFORMATICS/BTY407\u003c/span\u003e\u003cspan address=\"10.1093/BIOINFORMATICS/BTY407\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSardi SI, Somasekar S, Naccache SN, Bandeira AC, Tauro LB, Campos GS, et al. Coinfections of zika and chikungunya viruses in bahia, Brazil, identified by metagenomic next-generation sequencing. J Clin Microbiol. 2016;54:2348\u0026ndash;53. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1128/JCM.00877-16/ASSET/85E2DAE7-50FD-4543-A324-B4E04CAD76DF/ASSETS/GRAPHIC/ZJM9990951420002.JPEG\u003c/span\u003e\u003cspan address=\"10.1128/JCM.00877-16/ASSET/85E2DAE7-50FD-4543-A324-B4E04CAD76DF/ASSETS/GRAPHIC/ZJM9990951420002.JPEG\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePronyk PM, de Alwis R, Rockett R, Basile K, Boucher YF, Pang V, et al. Advancing pathogen genomics in resource-limited settings. Cell Genomics. 2023;3:100443. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/J.XGEN.2023.100443\u003c/span\u003e\u003cspan address=\"10.1016/J.XGEN.2023.100443\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHong NTT, Anh NT, Mai NTH, Nghia HDT, Nhu LNT, Thanh TT, et al. Performance of Metagenomic Next-Generation Sequencing for the Diagnosis of Viral Meningoencephalitis in a Resource-Limited Setting. Open Forum Infect Dis. 2020;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/OFID/OFAA046\u003c/span\u003e\u003cspan address=\"10.1093/OFID/OFAA046\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYek C, Pacheco AR, Vanaerschot M, Bohl JA, Fahsbender E, Aranda-D\u0026iacute;az A, et al. Metagenomic pathogen sequencing in resource-scarce settings: Lessons learned and the road ahead. Front Epidemiol. 2022;2:926695. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/FEPID.2022.926695/BIBTEX\u003c/span\u003e\u003cspan address=\"10.3389/FEPID.2022.926695/BIBTEX\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGreninger AL, Chen EC, Sittler T, Scheinerman A, Roubinian N, Yu G, et al. A Metagenomic Analysis of Pandemic Influenza A (2009 H1N1) Infection in Patients from North America. PLoS ONE. 2010;5:e13381. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/JOURNAL.PONE.0013381\u003c/span\u003e\u003cspan address=\"10.1371/JOURNAL.PONE.0013381\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFourgeaud J, Regnault B, Ok V, Da Rocha N, Sitterl\u0026eacute; \u0026Eacute;, Mekouar M, et al. Performance of clinical metagenomics in France: a prospective observational study. Lancet Microbe. 2024;5:e52\u0026ndash;61. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S2666-5247(23)00244-6\u003c/span\u003e\u003cspan address=\"10.1016/S2666-5247(23)00244-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOgunbayo AE, Sabiu S, Nyaga MM. Evaluation of extraction and enrichment methods for recovery of respiratory RNA viruses in a metagenomics approach. J Virol Methods. 2023;314:114677. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/J.JVIROMET.2023.114677\u003c/span\u003e\u003cspan address=\"10.1016/J.JVIROMET.2023.114677\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMao W, Wang J, Li T, Wu J, Wang J, Wen S, et al. Pathogens 2025. Page 264. 2025;14:14:264. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/PATHOGENS14030264\u003c/span\u003e\u003cspan address=\"10.3390/PATHOGENS14030264\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Hybrid Capture-Based Sequencing Enables Highly Sensitive Zoonotic Virus Detection Within the One Health Framework.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMourik K, Sidorov I, Carbo EC, van der Meer D, Boot A, Kroes ACM, et al. Comparison of the performance of two targeted metagenomic virus capture probe-based methods using reference control materials and clinical samples. J Clin Microbiol. 2024;62. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1128/JCM.00345-24/SUPPL_FILE/JCM.00345-24-S0002.XLSX\u003c/span\u003e\u003cspan address=\"10.1128/JCM.00345-24/SUPPL_FILE/JCM.00345-24-S0002.XLSX\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKalantar KL, Carvalho T, De Bourcy CFA, Dimitrov B, Dingle G, Egger R, et al. IDseq\u0026mdash;An open source cloud-based pipeline and analysis service for metagenomic pathogen detection and monitoring. Gigascience. 2020;9:1\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/GIGASCIENCE/GIAA111\u003c/span\u003e\u003cspan address=\"10.1093/GIGASCIENCE/GIAA111\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/BIOINFORMATICS/BTU170\u003c/span\u003e\u003cspan address=\"10.1093/BIOINFORMATICS/BTU170\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLangmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/NMETH.1923\u003c/span\u003e\u003cspan address=\"10.1038/NMETH.1923\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDanecek P, Bonfield JK, Liddle J, Marshall J, Ohan V, Pollard MO, et al. Twelve years of SAMtools and BCFtools. Gigascience. 2021;10:1\u0026ndash;4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/GIGASCIENCE/GIAB008\u003c/span\u003e\u003cspan address=\"10.1093/GIGASCIENCE/GIAB008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics. 2018;34:3094. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/BIOINFORMATICS/BTY191\u003c/span\u003e\u003cspan address=\"10.1093/BIOINFORMATICS/BTY191\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBuchfink B, Reuter K, Drost HG. Sensitive protein alignments at tree-of-life scale using DIAMOND. Nat Methods 2021. 2021;18:4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41592-021-01101-x\u003c/span\u003e\u003cspan address=\"10.1038/s41592-021-01101-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrjibelski A, Antipov D, Meleshko D, Lapidus A, Korobeynikov A. Using SPAdes De Novo Assembler. Curr Protoc Bioinf. 2020;70:e102. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/CPBI.102\u003c/span\u003e\u003cspan address=\"10.1002/CPBI.102\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCamacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, et al. BLAST+: architecture and applications. BMC Bioinformatics. 2009;10:421. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1471-2105-10-421\u003c/span\u003e\u003cspan address=\"10.1186/1471-2105-10-421\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePedersen BS, Quinlan AR. Mosdepth: quick coverage calculation for genomes and exomes. Bioinformatics. 2018;34:867\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/BIOINFORMATICS/BTX699\u003c/span\u003e\u003cspan address=\"10.1093/BIOINFORMATICS/BTX699\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eECDC. Sequencing of SARS-CoV-2: first update. 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShean RC, Makhsous N, Stoddard GD, Lin MJ, Greninger AL. VAPiD: A lightweight cross-platform viral annotation pipeline and identification tool to facilitate virus genome submissions to NCBI GenBank. BMC Bioinformatics. 2019;20:48. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/S12859-019-2606-Y/TABLES/1\u003c/span\u003e\u003cspan address=\"10.1186/S12859-019-2606-Y/TABLES/1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu B, Shao N, Wang J, Zhou SY, Su HX, Dong J, et al. An Optimized Metagenomic Approach for Virome Detection of Clinical Pharyngeal Samples With Respiratory Infection. Front Microbiol. 2020;11:1552. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/FMICB.2020.01552/FULL\u003c/span\u003e\u003cspan address=\"10.3389/FMICB.2020.01552/FULL\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAshraf S, Jerome H, Bugembe DL, Ssemwanga D, Byaruhanga T, Kayiwa JT, et al. Uncovering the viral aetiology of undiagnosed acute febrile illness in Uganda using metagenomic sequencing. Nat Commun 2025. 2025;16:1. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41467-025-57696-8\u003c/span\u003e\u003cspan address=\"10.1038/s41467-025-57696-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorfopoulou S, Buddle S, Torres Montaguth OE, Atkinson L, Guerra-Assun\u0026ccedil;\u0026atilde;o JA, Moradi Marjaneh M, et al. Genomic investigations of unexplained acute hepatitis in children. Nat 2023. 2023;617:7961. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41586-023-06003-w\u003c/span\u003e\u003cspan address=\"10.1038/s41586-023-06003-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Metagenomic NGS, Virus Detection, Pandemic Preparedness","lastPublishedDoi":"10.21203/rs.3.rs-8563816/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8563816/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMetagenomic next-generation sequencing (mNGS) is an untargeted approach that enables detection of pathogens directly from samples without prior knowledge of their genetic sequences. In the context of pandemic preparedness and One Health surveillance, there is a pressing need for validated viral mNGS workflows that perform reliably across diverse hosts sample types and pre-analytical conditions.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe study designed and evaluated two mNGS workflows, one for swabs and one for complex tissue matrices, using a reference repository of clinical and post-mortem samples. The panel comprised swabs and tissue samples positive for 19 DNA and RNA viruses (including 12 species) from nine host species and nine anatomical sites, encompassing a range of transport media, storage temperatures and processing timelines. Quality control metrics were embedded throughout nucleic acid extraction, library preparation and sequencing to monitor performance and support interpretation. Overall, 89.5% of 19 known DNA and RNA viruses were detected, including from samples with low nucleic acid concentrations (\u0026lt;\u0026thinsp;1 ng/\u0026micro;l) and variable integrity and purity. The workflows identified viral co-infections that had not been detected by prior targeted testing, as well as Phocid herpesvirus 7 (PHV7) for which no complete reference genome was initially available.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThese results demonstrate that the validated swab and tissue mNGS workflows are sufficiently robust and sensitive for deployment in investigations of suspected viral disease of unknown aetiology and for early detection of emerging viral threats at the animal\u0026ndash;human interface.\u003c/p\u003e","manuscriptTitle":"One Health Viral Metagenomics for Pandemic Preparedness: Validated mNGS Workflows for Viral Detection and Genome Recovery from Swab and Tissue Specimens","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-16 11:03:37","doi":"10.21203/rs.3.rs-8563816/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-04T15:41:45+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-19T21:18:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-17T21:17:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"210714810231464324353638440843849378627","date":"2026-02-02T18:44:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"40131232191630700214970658846790260095","date":"2026-01-30T12:44:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"130166748973886212759058047918155083786","date":"2026-01-27T17:06:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"103661085309725885749813461722012839752","date":"2026-01-22T16:38:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-14T09:04:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-14T08:55:18+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-13T08:12:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-13T07:30:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Microbiology","date":"2026-01-13T07:21:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cf9cbb9a-9c63-4803-a127-422724346a8d","owner":[],"postedDate":"January 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T09:55:29+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-16 11:03:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8563816","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8563816","identity":"rs-8563816","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}