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PubMed2026

Library strategies differentially shape microbial, functional, and host signals in clinical metagenomic sequencing.

Metagenomic next-generation sequencing (mNGS) is increasingly used in infectious disease diagnostics, yet how library preparation shapes the microbial, functional, and host signals recovered from clinical samples remains poorly defined. Here, we performed a within-sample parallel comparison of three mNGS library preparation strategies-DNA-based libraries (DNAlib), RNA-based libraries (RNAlib), and total nucleic acid-based libraries (TNAlib)-across a diverse range of clinical specimens spanning five sample types. Using a curated clinical infectome as a benchmark, we show that library strategies are not interchangeable but capture distinct biological dimensions of the same specimen. RNAlib provided the most comprehensive standalone recovery of the clinical infectome, with improved detection of RNA viruses and cellular pathogens, enhanced resolution of resistance and virulence signals, and preservation of infection-associated host immune signatures. DNAlib showed stronger baseline recovery of DNA viruses and broader host genome coverage, whereas the TNAlib workflow evaluated here largely behaved as an intermediate strategy rather than a consistent improvement over dedicated DNA- or RNA-based workflows. Together, these results establish that the library preparation protocol is a major determinant of how clinical mNGS data should be interpreted and provide a framework for selecting sequencing strategies according to specific diagnostic and biological questions.IMPORTANCEMetagenomic sequencing is increasingly used in infectious disease research and clinical diagnostics, but different library preparation strategies may recover fundamentally different biological signals from the same sample. These signals include not only pathogens but also background microbes, microbial functional activity, and host immune-response patterns. Here, we systematically compared DNA-, RNA-, and total nucleic acid-based metagenomic sequencing libraries using the same clinical samples processed in parallel. We found that the three strategies did not provide equivalent information. RNA-based sequencing generated the most informative single-library view of infection, particularly for RNA viruses, cellular pathogens, functional microbial signals, and host immune-response patterns. DNA-based sequencing was more effective for DNA virus and host genome recovery, whereas the total nucleic acid sequencing workflow evaluated here generally behaved as an intermediate strategy. These findings show that library preparation can substantially influence the interpretation of metagenomic data.

functional characterization↗

PubMed2024

Rapid inference of antibiotic susceptibility phenotype of uropathogens using metagenomic sequencing with neighbor typing.

UNLABELLED: Timely diagnostic tools are needed to improve antibiotic treatment. Pairing metagenomic sequencing with genomic neighbor typing algorithms may support rapid clinically actionable results. We created resistance-associated sequence elements (RASE) databases for Escherichia coli and Klebsiella spp. and used them to predict antibiotic susceptibility in directly sequenced (Oxford Nanopore) urine specimens from critically ill patients. RASE analysis was performed on pathogen-specific reads from metagenomic sequencing. We evaluated the ability to predict (i) multi-locus sequence type (MLST) and (ii) susceptibility profiles. We used neighbor typing to predict MLST and susceptibility phenotype of E. coli (64/80) and Klebsiella spp. (16/80) from urine samples. When optimized by lineage score, MLST predictions were concordant for 73% of samples. Similarly, a RASE-susceptible prediction for a given isolate was associated with a specificity and a positive likelihood ratio (LR+) for susceptibility of 0.65 (95% CI, 0.54-0.76) and 2.26 (95% CI, 1.75-2.92), respectively, with an increase in the probability of susceptibility of 10%. A RASE-non-susceptible prediction was associated with a sensitivity and a negative likelihood ratio (LR-) for susceptibility of 0.79 (95% CI, 0.74-0.84) and 0.32 (95% CI, 0.24-0.43) respectively, with a decrease in the probability of susceptibility of 20%. Numerous antibiotic classes could reasonably be reconsidered empiric therapy by shifting empiric probabilities of susceptibility across relevant treatment thresholds. Moreover, these predictions can be available within 6 h. Metagenomic sequencing of urine specimens with neighbor typing provides rapid and informative predictions of lineage and antibiotic susceptibility with the potential to impact clinical decision-making. IMPORTANCE: Urinary tract infections (UTIs) are a common diagnosis in hospitals and are often treated empirically with broad-spectrum antibiotics. These broad-spectrum agents can select for resistance in these bacteria and co-colonizing organisms. The use of narrow-spectrum agents is desirable as an antibiotic stewardship measure; however, it is counterbalanced by the need for adequate therapy. Identification of causative organisms and their antibiotic susceptibility can help direct treatment; however, conventional testing requires days to produce actionable results. Methods to quickly and accurately predict susceptibility phenotypes for pathogens causing UTI could thus improve both patient outcomes and antibiotic stewardship. Here, expanding on previous work showing accurate prediction for certain Gram-positive pathogens, we demonstrate how the use of RASE from metagenomic sequencing can provide informative and rapid phenotype prediction results for common Gram-negative pathogens in UTI, highlighting the future potential of this method to be used in clinical settings to guide empiric antibiotic selection.

Humans↗

PubMed2026

Amplicon and metagenomic sequencing reveal thifluzamide drive rhizosphere microbial structural shifts and functional adaption.

Thifluzamide (TF) is a widely used phenyl urea fungicide in rice production; however, its impacts on the structural composition and functional dynamics of the rhizosphere microbiome remain poorly understood. Here, we systematically investigated the effects of TF on the structure, interactions, and functional potential of the rice (Oryza sativa L.) rhizosphere microbiome using integrated amplicon sequencing and metagenomic approaches. TF application significantly altered both bacterial and fungal community composition, bacterial diversity was markedly reduced, whereas fungal diversity increased. With bacterial diversity markedly reduced while fungal diversity increased. Beta-diversity analyses revealed strong treatment-driven community separation, indicating pronounced TF-induced microbial restructuring. Co-occurrence network analysis demonstrated reduced complexity and connectivity in bacterial networks but increased negative co-occurrence patterns within fungal communities, suggesting contrasting stability responses between microbial kingdoms. Metagenomic profiling further revealed substantial functional shifts, including the differential enrichment of KEGG and COG pathways associated with xenobiotic metabolism. Notably, while total ARG abundance remained stable, TF exposure altered the resistome profile by selectively enriching specific classes of antibiotic resistance genes (ARGs), biocide resistance genes (BRGs), and mobile genetic elements (MGEs). Strong positive correlations between MGEs and ARGs highlighted an elevated potential for horizontal gene transfer. Metagenome-assembled genome (MAG) analysis identified specific TF-enriched bacterial taxa, including Methylophilus, Sulfurospirillum, and Azospirillum, which harbored genes involved in pesticide degradation and xenobiotic transformation. Collectively, these findings demonstrate that TF profoundly reshapes the rice rhizosphere microbiome by altering microbial diversity, interaction networks, resistance gene profiles, and functional capacities. This study provides genomic insights into fungicide-microbiome interactions, underscoring the potential ecological implications associated with TF application, while identifying candidate microbial taxa that may contribute to pesticide degradation and rhizosphere microecology resilience.

Rhizosphere↗

PubMed2026

Comparative evaluation of probe-capture and conventional metagenomic sequencing across multiple clinical sample types, with analysis of paired bronchoalveolar lavage fluid and blood samples.

Conventional metagenomic next-generation sequencing (mNGS) suffers from host nucleic acid interference and poor performance in low-biomass samples. Probe-capture metagenomic sequencing (PC-mNGS), which enriches microbial targets via hybridization probes, shows superior sensitivity but lacks systematic multi-sample evaluations. This study compared PC-mNGS and mNGS across diverse clinical specimens (bronchoalveolar lavage fluid [BALF], blood, cerebrospinal fluid [CSF]) and assessed the clinical utility of pathogen co-detection in paired BALF-blood samples from sepsis patients. A total of 282 samples (81 BALF, 141 blood, 25 CSF, 35 others) sequenced by both PC-mNGS and mNGS were analyzed. Additionally, 621 paired BALF-blood samples from sepsis patients with pulmonary infections were evaluated. PC-mNGS achieved higher pathogen detection rates (66.67% vs 57.10%, P = 0.000198) than mNGS, particularly in blood (66.67% vs 47.52%, P = 2.5 × 10⁻⁵). PC-mNGS detected more bacteria (19 species exclusive) and fungi (11 species exclusive) than mNGS. Viruses showed comparable detection. BALF and CSF exhibited high overall agreement (OPA: 96.30% and 88%, respectively), while blood had lower concordance (NPA: 54.05%, OPA: 70.92%). A total of 60.55% of BALF-positive samples (PC-mNGS) had co-detected pathogens in blood. Gram-negative bacteria (e.g., Klebsiella pneumoniae) and fungi (e.g., Candida albicans) showed higher blood co-detection rates than viruses. In this study, PC-mNGS detected more pathogens and showed a higher positivity rate than mNGS in blood samples. BALF sequencing data, particularly bacterial reads per million (RPM), may predict bloodstream co-detection, aiding in sepsis management. However, clinical validation and integration with traditional diagnostics are needed to confirm utility. This study highlights PC-mNGS as a promising tool for complex infections but underscores the need for rigorous multi-context validation.IMPORTANCEAccurate and rapid identification of pathogens is critical for effective treatment of severe infectious diseases, such as sepsis. This study demonstrates that probe-capture metagenomic sequencing (PC-mNGS) detected more pathogens in blood samples compared to conventional metagenomic sequencing, especially for bacterial and fungal infections. By analyzing paired lung and blood samples, we show that high pathogen levels in lung fluid may predict bloodstream infection, offering a potential early warning for clinicians. These findings support the use of PC-mNGS as a more sensitive diagnostic tool, which could lead to faster, more targeted therapies and better outcomes for patients with complex infections.

Humans↗

PubMed2025

Inferring the sensitivity of wastewater metagenomic sequencing for early detection of viruses: a statistical modelling study.

BACKGROUND: Metagenomic sequencing of wastewater (W-MGS) can in principle detect any known or novel pathogen in a population. We aimed to quantify the sensitivity and cost of W-MGS for viral pathogen detection by jointly analysing W-MGS and epidemiological data for a range of human-infecting viruses. METHODS: In this statistical modelling study, we analysed sequencing data from four studies of untargeted W-MGS to estimate the relative abundance of 11 human-infecting viruses. Corresponding prevalence and incidence estimates were obtained or calculated from academic and public health reports. We combined these estimates using a hierarchical Bayesian model to predict relative abundance at set prevalence or incidence values, allowing comparison across studies and viruses. These predictions were then used to estimate the sequencing depth and concomitant cost required for pathogen detection using W-MGS with or without use of a hybridisation capture enrichment panel. FINDINGS: After controlling for variation in local infection rates, relative abundance varied by orders of magnitude across studies for a given virus. For instance, a local SARS-CoV-2 weekly incidence of 1% corresponded to a predicted SARS-CoV-2 relative abundance ranging from 3·8 × 10-10 to 2·4 × 10-7 across studies, translating to orders-of-magnitude variation in the cost of operating a system able to detect a SARS-CoV-2-like pathogen at a given sensitivity. Use of a respiratory virus enrichment panel in two studies greatly increased predicted relative abundance of SARS-CoV-2, lowering yearly costs by 27-fold (from US$7·87 million to $287 000) and 29-fold (from $1·98 million to $69 100) for a system able to detect a SARS-CoV-2-like pathogen before reaching 0·01% cumulative incidence. INTERPRETATION: The large variation in viral relative abundance after controlling for epidemiological factors indicates that other sources of inter-study variation, such as differences in sewershed hydrology and laboratory protocols, have a substantial impact on the sensitivity and cost of W-MGS. Well chosen hybridisation capture panels can greatly increase sensitivity and reduce cost for viruses in the panel, but might reduce sensitivity to unknown or unexpected pathogens. FUNDING: The Wellcome Trust, Open Philanthropy, and Musk Foundation.

Humans↗

PubMed2026

MetaFX: feature extraction from whole-genome metagenomic sequencing data.

MOTIVATION: Microbial communities consist of thousands of microorganisms and viruses and have a tight connection with an environment, such as gut microbiota modulation of host body metabolism. However, the direct relationship between the presence of certain microorganism and the host state often remains unknown. Toolkits using reference-based approaches are limited to microbes present in databases. Reference-free methods often require enormous resources for metagenomic assembly or results in many poorly interpretable features based on k-mers. RESULTS: Here we present MetaFX-an open-source library for feature extraction from whole-genome metagenomic sequencing data and classification of groups of samples. Using a large volume of metagenomic samples deposited in databases, MetaFX compares samples grouped by metadata criteria (e.g. disease, treatment, etc.) and constructs genomic features distinct for certain types of communities. Features constructed based on statistical k-mer analysis and de Bruijn graphs partition. Those features are used in machine learning models for classification of novel samples. Extracted features can be visualized on de Bruijn graphs and annotated for providing biological insights. We demonstrate the utility of MetaFX by building classification models for 590 human gut samples with inflammatory bowel disease. Our results outperform the previous research disease prediction accuracy up to 17%, and improves classification results compared to taxonomic analysis by 9±10% on average. AVAILABILITY AND IMPLEMENTATION: MetaFX is a feature extraction toolkit applicable for metagenomic datasets analysis and samples classification. The source code, test data, and relevant information for MetaFX are freely accessible at https://github.com/ctlab/metafx under the MIT License. Alternatively, MetaFX can be obtained via http://doi.org/10.5281/zenodo.16949369.

Metagenomics↗

PubMed2025

Mapler: a pipeline for assessing assembly quality in taxonomically rich metagenomes sequenced with HiFi reads.

SUMMARY: Metagenome assembly seeks to reconstruct the most high-quality genomes from sequencing data of microbial ecosystems. Despite technological advancements that facilitate assembly, such as Hi-Fi long reads, the process remains challenging in complex environmental samples consisting of hundreds to thousands of populations. Mapler is a metagenome assembly and evaluation pipeline with a focus on evaluating the quality of Hi-Fi long read metagenome assemblies. It incorporates several state-of-the-art metrics, as well as novel metrics assessing the diversity that remains uncaptured by the assembly process. Mapler facilitates the comparison of assembly strategies and helps identify methodological bottlenecks that hinder genome reconstruction. AVAILABILITY AND IMPLEMENTATION: Mapler is open source and publicly available under the AGPL-3.0 licence at https://github.com/Nimauric/Mapler. Source code is implemented in Python and Bash as a Snakemake pipeline. A snapshot of the code is available on Software Heritage at swh:1:snp:df4f5f02e22ebbab285ec14af58d4d88436ee5d6. Raw data and results are available at https://entrepot.recherche.data.gouv.fr/dataset.xhtml?persistentId=doi:10.57745/2SA8AB.

Metagenome↗

PubMed2026

Whole metagenome sequencing: not deep enough for complete microbial function recovery.

BACKGROUND: Whole metagenome shotgun sequencing (WMS) is widely used to profile microbial function. However, technical variability in sequencing and analysis often obscures true biological patterns. Large-scale studies are particularly susceptible to batch effects, such as differences in sequencing depth and platform and annotation strategies, as well as sample-to-flow-cell assignments. However, the relative effects of these factors on functional inference in such studies have yet to be systematically evaluated. We analyzed oral-rinse WMS data from 671 Nigerian youths aged 9-18, sequenced on two Illumina platforms. Microbial molecular functionality encoded in these data was annotated using the mi-faser/Fusion pipeline, to capture the broad functional repertoire, and HUMAnN 3/EC numbers pipeline to characterize curated enzymatic activities. We then quantified how technical factors and batch effects shaped the recovery of microbial functionality. RESULTS: Three findings of our work were most salient. First, we observed that the choice of annotation strategy traded off between breadth and specificity of functional coverage. Second, we found that low-prevalence functions were disproportionately lost at shallow sequencing depths, indicating that in, e.g., case-control studies with few representatives of the minor class, sequencing depth could critically impact study resolution. Finally, using our newly developed model relating sequencing depth to functional recovery, we demonstrated that increasing sequencing depth does not directly or proportionally improve functional recall. That is, at as little as 10% of this study's sequencing depth, 30% of the estimated complete microbiome functional repertoire was detectable. However, even at the full depth used in this study, we were only able to recover an estimated 60% of that complete functional repertoire. We further showed that despite biomes differences in functional diversity and host contamination levels (e.g., soil, fecal), incomplete functional recovery at commonly used sequencing depths was consistently observed. CONCLUSIONS: Together, these findings and our depth-to-function mapping framework provide practical guidelines for the design and interpretation of WMS studies. Coordinating sequencing depth planning with annotation strategy, experimental design, and rigorous batch control is thus essential for robust detection of microbial functions and for ensuring reproducible microbiome insights. Video Abstract.

Humans↗

PubMed2026

Predicting bloodstream infection by plasma cell-free metagenomic sequencing: a prospective cohort study.

BACKGROUND: Patients receiving myelosuppressive chemotherapy or haematopoietic cell transplantation are at high risk for life-threatening bloodstream infections. A novel pre-emptive treatment paradigm guided by pathogen detection before symptoms appear might reduce this risk, but no validated screening test is available. This study evaluated the sensitivity and specificity of plasma microbial cell-free DNA metagenomic sequencing (mcfDNA-Seq) for predicting bloodstream infections in children and adolescents receiving therapy for high-risk leukaemia. METHODS: In this prospective cohort study, between Aug 9, 2017, and Feb 28, 2022, leftover clinical plasma samples were prospectively collected up to once per day from patients who were younger than 25 years, receiving care for leukaemia at St Jude Children's Research Hospital (Memphis, TN, USA), and at high risk for life-threatening bloodstream infections. mcfDNA-Seq was used to identify pathogen DNA in blood samples obtained during the 7 days before to 1 day after bloodstream infection onset, and in control samples from the same population in the absence of fever or infection. The testing laboratory was masked to sample status. Primary outcomes were predictive sensitivity of mcfDNA-Seq for detecting the expected bloodstream infection pathogen during the 3 days preceding the day of bloodstream infection onset, with a prespecified favourable sensitivity of 50%, and predictive specificity of mcfDNA-Seq in control samples. Exploratory analyses comprised assessing sensitivity and specificity restricted to bacteria or common bloodstream infection pathogens, and after applying a data-derived DNA fragment concentration cutoff; estimating the predictive sensitivity on each of the 7 days before bloodstream infection onset; identifying clinical characteristics that affected predictive sensitivity or specificity; and examining the clinical relevance of additional organisms identified by mcfDNA-Seq during bloodstream infection episodes. Diagnostic sensitivity was also assessed on samples collected on the day of, or day after, diagnosis of bloodstream infection. This study is registered with ClinicalTrials.gov, NCT03226158. FINDINGS: 94 evaluable bloodstream infections occurred in 60 (38%) of 158 enrolled participants; 19 episodes were previously described in the pilot phase of this study. The predictive sensitivity of mcfDNA-Seq was 51·9% (95% CI 40·5-63·1) for all bloodstream infection episodes, 53·8% (42·2-65·2) for bacterial infection only, and 51·9% (40·5-63·1) when applying a DNA fragment concentration cutoff of 140 molecules per μL. Sensitivity was lowest at day -7 and increased daily until the day of diagnosis. Diagnostic sensitivity was 81·3% (95% CI 71·0-89·1) for all bloodstream infection episodes and 83·1% (72·9-90·7) for bacterial infections only. Predictive specificity was 82·7% (95% CI 76·0-88·2), but improved to 88·9% (83·0-93·3) for common bloodstream infection pathogens, and to 93·8% (88·9-97·0) when also applying the DNA fragment concentration cutoff. Predictive sensitivity was higher in participants with acute lymphoblastic leukaemia (adjusted odds ratio [aOR] 11·1 [1·7-74·2] vs those with acute myeloid leukaemia), and it was lower in polymicrobial infections (aOR 0·0 [0·0-0·2] vs monomicrobial Gram-positive infections). Clinical false-positive results were positively associated with gastrointestinal disturbance alone (p=0·037) or combined with recent administration of high-dose cytarabine (p=0·012). Additional organisms identified by mcfDNA-Seq that were not identified by blood culture were less likely than expected organisms to have an increasing DNA concentration during the days preceding bloodstream infection diagnosis. INTERPRETATION: mcfDNA-Seq can detect causative pathogens before the onset of some bloodstream infection episodes in profoundly immunocompromised patients. Predictive specificity might be improved by restricting results to a subgroup of relevant organisms, excluding patients with high risk of false-positive results, or applying a higher concentration cutoff. Clinical trials are needed to evaluate mcfDNA-Seq-guided pre-emptive therapy for preventing life-threatening bloodstream infections in patients with high risk. FUNDING: The National Cancer Institute, American Lebanese Syrian Associated Charities, St Jude Children's Research Hospital, and Karius.

Adolescent↗

PubMed2026

Microbial signal profiles and organism-level concordance between plasma metagenomic sequencing and blood culture in suspected bloodstream infection.

Plasma metagenomic next-generation sequencing (mNGS) and blood culture detect different components of the microbial signal and frequently produce discordant organism reports. We characterized microbial signal class, report-derived burden, organism-level concordance, and independent clinical attribution in a retrospective, single-center, episode-level cohort. Among 329 episodes with evaluable plasma mNGS reports, 315 had blood culture performed; 232 were mNGS positive/culture negative and 53 were positive by both methods. In the 232 discordant episodes, the recorded routine-care diagnosis classified 124 as bloodstream infection (BSI) and 108 as non-BSI. Nonviral signals were present in 78.2% and 42.6%, respectively (P < 0.001), and median maximum report-derived sequence counts were 98.5 and 11.5 (P < 0.001). Two laboratory physicians then independently reviewed source records using structured criteria while masked to the recorded BSI label and mNGS organism and sequence-count information. Initial agreement for the five-category BSI assessment was 97.6% (Cohen's kappa, 0.960). Within the mNGS-positive/culture-negative subgroup, adjudicated BSI likelihood showed a modest ordinal association with report burden (Spearman rho = 0.190; P = 0.004), while mNGS organisms were considered supported in 1 episode, plausible in 158, unlikely or contaminant in 72, and unresolved in 1. Among 53 dual-positive episodes, 33 (62.3%) shared at least one species, but only 5 (9.4%) had complete species-set concordance. Plasma mNGS and blood culture therefore frequently generated non-equivalent organism sets. Signal class and report burden contributed graded contextual evidence, but organism-level attribution required clinical review and orthogonal microbiology rather than binary positivity alone.

Humans↗

PubMed2026

Meta-CD: a metagenomic sequencing coverage and depth calculator for target species.

Metagenomic Coverage and Depth Calculator (Meta-CD) is a convenient, biologist-friendly tool for determining coverage and depth to enhance taxonomic detection, functional profiling, and metagenome-assembled genome (MAG) recovery in metagenomics. It supports experimental design and post-sequencing analysis, modeling how genome size, relative abundance, sequencing depth, and DNA quantity influence detection of target species.

metagenomics↗

PubMed2025

Direct cost savings associated with reduction in plasma metagenomic sequencing.

Following recognition that our hospital had higher use of plasma metagenomic next-generation sequencing than our peers, we implemented a process for approval by infectious diseases before test collection. This intervention is calculated to result in a direct cost savings of $79,505-$84,057/year, driven mainly by reduced laboratory costs.

Humans↗

PubMed2026

The chromosomal genome sequence of the spiny sea fan, Muricea muricata (Pallas, 1766) (Malacalcyonacea: Plexauridae) and its associated microbial metagenome sequences.

We present a genome assembly from a Muricea muricata specimen (spiny sea fan; Cnidaria; Anthozoa; Malacalcyonacea; Plexauridae). The genome sequence has a total length of 453.40 megabases. Most of the assembly (98.45%) is scaffolded into 16 chromosomal pseudomolecules. The mitochondrial genome has also been assembled, with a length of 19.29 kilobases. Gene annotation of this assembly by Ensembl identified 52 164 protein-coding genes. From the metagenome data, we recovered five bins, of which three were high-quality MAGs.

Malacalcyonacea↗

PubMed2026

The chromosomal genome sequence of the maze coral, Meandrina meandrites (Linnaeus, 1758) (Scleractinia: Meandrinidae) and its associated microbial metagenome sequences.

We present a genome assembly from a specimen of Meandrina meandrites (maze coral; Cnidaria; Anthozoa; Scleractinia; Meandrinidae). The genome sequence has a total length of 551.16 megabases. Most of the assembly (99.25%) is scaffolded into 14 chromosomal pseudomolecules. The mitochondrial genome has also been assembled, with a length of 17.2 kilobases. Gene annotation of this assembly by Ensembl identified 30 464 protein-coding genes. We recovered two bins from the metagenome data.

Meandrina meandrites↗

PubMed2026

The chromosomal genome sequence of the lesser starlet coral, Siderastrea radians (Pallas, 1766) (Scleractinia: Rhizangiidae) and its associated microbial metagenome sequences.

We present a genome assembly from a specimen of Siderastrea radians (lesser starlet coral; Cnidaria; Anthozoa; Scleractinia; Rhizangiidae). The genome sequence has a total length of 807.19 megabases. Most of the assembly (94.17%) is scaffolded into 14 chromosomal pseudomolecules. The mitochondrial genome has also been assembled, with a length of 19.38 kilobases. Gene annotation of this assembly by Ensembl identified 47 051 protein-coding genes. From the metagenome data, we recovered two binned metagenomes assigned to the bacterial phylum Bacteroidota and class Bacteroidia.

Scleractinia↗

PubMed2026

Metagenomic sequencing in encephalitis diagnostics: Challenges and opportunities in clinical settings.

The primary aim of this study was to determine whether metagenomic next-generation sequencing (mNGS) can identify potential microbial agents responsible for encephalitis of unknown origin in immunocompetent patients, thereby enhancing clinical diagnostics. Cerebrospinal fluid samples from well-characterized patients (n = 17) diagnosed with encephalitis of unknown origin, according to Swedish national guidelines, were sequenced using mNGS using the Ion Torrent platform and analyzed using bioinformatic platforms. Samples from patients with known viral CNS infections i.e. HSV-2 meningitis (n = 4), VZV CNS infections (n = 3), enterovirus meningitis (n = 2), JCV CNS infection (n = 2) were used as controls for the methodology (n = 11). No viral agents were detected in 16/17 CSF samples from patients with encephalitis of unknown etiology. 13/17 CSF samples were analysed for the most common autoimmune antibodies and were negative. In one CSF sample from patients with encephalitis of unknown origin a Human pegivirus (HPgV) was detected. In 9/11 control CSF samples from patients with CNS infections, RNA or DNA of the known virus were detected. The main conclusion in this study was that the negative results were related to that the majority of included patients were immunocompetent. The finding of HPgV in a patient with unknown encephalitis was judged as a bystander. However, mNGS might detect more pathogens in other patient cohorts and this study implicates that a close collaboration between the clinical laboratory and the clinicians enables a safe implementation of metagenomics.

Humans↗

PubMed2026

Rapid diagnosis of common, undetected, and uncultivable bloodstream infections from positive blood cultures using Oxford Nanopore sequencing: a metagenomic pipeline analysis.

BACKGROUND: Metagenomic sequencing can potentially transform clinical microbiology by enabling rapid pathogen identification and antimicrobial resistance (AMR) prediction in critically ill patients with bloodstream infections. However, the clinical use of metagenomic sequencing has been constrained by its speed, accuracy, and technical feasibility. Our aim was to develop and evaluate a direct-from-positive blood culture workflow using Oxford Nanopore sequencing that overcomes these limitations and delivers rapid, accurate results. METHODS: In this metagenomic pipeline analysis, 211 positive (130 aerobic and 81 anaerobic) and 62 negative (30 aerobic and 32 anaerobic) randomly selected blood cultures were processed from Oxford University Hospitals for comparing species identification, AMR detection, and time-to-result against standard culture-based diagnostics performed by the hospital's routine microbiology laboratory. Species prediction was performed using Kraken2 with a comprehensive standard database, applying heuristic and random forest classification models. Additionally, we benchmarked AMR classification tools and databases, including ResFinder, CARD, and NCBI AMRFinderPlus. FINDINGS: Across all samples, our method achieved 97% sensitivity and 94% specificity for species identification compared with that of routine culture and matrix-assisted laser desorption ionisation time-of-flight-based diagnostics; both sensitivity and specificity increased to 100% after adjudication of plausible additional infections. We detected 19 additional infections (13 polymicrobial, five previously unidentifiable, and one in a culture-negative sample) and delivered species identification results within 3 h 20 min (IQR 3 h 7 min-3 h 27 min), approximately 10 h earlier than routine diagnostic methods. For the ten most common clinically relevant pathogens, our method yielded AMR results 20 h earlier than current antimicrobial susceptibility testing, with an overall sensitivity of 88% and specificity of 93%. Performance varied by species. For Staphylococcus aureus, the AMR prediction sensitivity was 100% and specificity was 99%, and for Escherichia coli, the prediction sensitivity was 91% and specificity was 94%. INTERPRETATION: These findings show that metagenomic sequencing has the potential to rapidly and comprehensively detect pathogens and AMR in bloodstream infections. Integration into clinical practice could help to close diagnostic gaps, reduce empirical antibiotic use, and enable rapid targeted treatment. Nonetheless, improvements in AMR prediction for some species and drugs, along with further multisite validation, are required before clinical implementation. FUNDING: National Institute for Health Research (NIHR) Oxford Biomedical Research Centre.

Humans↗

PubMed2026

RAmpSim: a thermodynamic simulator for hybridization capture in metagenomic sequencing.

MOTIVATION: Simulators that generate synthetic datasets help address the lack of ground truth for developing and benchmarking computational tools. Many read simulators assume uniform sampling across reference genomes; however, for newer capture-based sequencing technologies (e.g. TELSeq), this assumption is intentionally broken to oversample regions of interest. Along with systematic biases arising from probe multiplicity, sequence composition, and species abundances inherent to capture-based sequencing, this mismatch between modeling assumptions and the characteristics of real data necessitates the design of a new capture-based sequencing-specific simulator. RESULTS: We present RAmpSim, a fast simulator that models bait-target hybridization and fragment capture using a thermodynamic nearest-neighbor energy model and Boltzmann-weighted sampling of binding sites. Fragments are generated through multinomial sampling parameterized by bait concentration, binding energy, and genomic abundance before being passed to existing models of platform-specific errors. Implemented in Rust, RAmpSim reproduces empirical within-genome coverage and cross-species enrichment patterns observed in capture-based metagenomic datasets. RAmpSim generally outperforms a uniform baseline with respect to position-based earth mover's distance when compared against the empirical coverage distribution. Classification analysis also shows high recall in recovering empirical high-coverage regions while outperforming a uniform baseline. AVAILABILITY: Code, example scripts, and data sources are available at https://github.com/az002/RAmpSim.git.

Metagenomics↗