Abstract Killer-cell immunoglobulin-like receptor (KIR) genes, key modulators of natural killer (NK) cell activity, play critical roles in immune response and disease susceptibility. Accurate KIR genotyping from short-read sequencing data remains challenging because of high sequence similarity among genes, extensive copy number variation, and substantial allelic diversity. Here, we present KIR*BLOOM, a likelihood-based approach for KIR genotyping from short-read data that models read depth and sequencing error across alternative genotype configurations. KIR*BLOOM first identifies KIR-relevant read pairs, maps them to a KIR allele database, and reduces the candidate allele space by excluding alleles unlikely to be present. It then infers gene copy number and selects alleles under the inferred copy-number constraints. Finally, variant calling is used to refine CDS sequences and identify potential novel alleles. We evaluated performance on 45 whole-genome sequencing samples with haplotype-resolved assemblies from the HPRC or HGSVC, using Immuannot-derived annotations as ground truth. KIR*BLOOM achieved 99.85% precision, 99.92% recall, and a Jaccard index of 99.77% for copy-number inference. At five-digit allele resolution, it achieved 92.73% precision, 92.69% recall, and an 87.29% Jaccard index, outperforming T1K, GraphKIR, and Geny. Together, these results demonstrate that KIR*BLOOM enables highly accurate KIR genotyping from short-read sequencing data.

The oral microbiota has been associated with Alzheimer's disease (AD). However, earlier studies provided conflicting results using varying sampling methods, sequencing techniques, and statistics, as well as independent subjects. To robustly identify disease-associated microbial features, we recruited patients and their healthy life partners from the same households sharing a more similar microbiota compared to independent individuals increasing statistical power via paired design and combined three different sequencing methods – including metagenomics—and several bioinformatic pipelines. We recruited 26 AD-patients and their life partners. Salivary and supragingival samples were collected and a clinical examination of the mouth was performed. Both groups showed comparable oral health. By focusing primarily on recurrently identified species across the different datasets we were able to identify a Core dysbiosis. This Core dysbiosis surprisingly spares the most central of oral diseases pathogens, namely Porphyromonas gingivalis. However, it includes numerous other species commonly associated with oral pathologies such as Prevotella nigrescens, Streptococcus anginosus, Dialister invisus, Anaeroglobus geminatus, Olsenella uli and Mogibacterium timidum. In contrast, more host-compatible species such as Prevotella melaninogenica or Streptococcus parasanguinis are identified in controls. This is the first study using a combined sequencing approach and a paired study design to identify robust features of the oral microbiota of AD-patients. Although promising, the results should nevertheless be interpreted with caution, as the cross-sectional study design limits the possibilities of interpretation, and larger, longitudinal data are necessary for causal conclusions. However, this combined approach on multiple processing levels to identify intra-partnership differences still offers the possibility to better identify disease-associated microbial features potentially involved in AD-pathogenesis. This study was prospectively registered at the German Clinical Trials Register (DRKS00023456) at the 30th of November 2020.

The local public health authority (LPHA) in Duesseldorf received notification of two SARS-CoV-2 outbreaks in nursing home A and nursing home B in October 2021 and embarked on an outbreak investitation to identify the source of the outbreak and epidemiological links between cases. The LPHA conducted a retrospective outbreak investigation involving residents and staff and combined results of active case finding, weekly point-of-care testing (POCT), and integrated genomic surveillance (IGS) with structured case interviews, whole-genome sequencing, and cluster analysis. The LPHA identified two linked outbreaks of SARS-CoV-2 infections in nursing homes A and B, with a total of 32 cases occurring from 12 to 29 October 2021. First case notification and routine contact tracing revealed a cluster of six SARS-CoV-2 cases in nursing home A and two cases in nursing home B. Weekly POCT independent of symptoms and IGS data revealed that the true extent of the outbreak involved 27 cases associated with nursing home A and five additional, genetically identical cases that were linked to nursing home B. Routine contact tracing identified 20.0

Motivation In prokaryotic genomes, methylation is an important epigenetic modification that regulates the uptake of foreign DNA; it can also contribute to replication or virulence. We present MPore, a novel method for the database-driven detection of active methyltransferases and their associated target site recognition motifs from Nanopore R10 sequencing data of prokaryotic isolates. In contrast to existing methods, which typically start with the de novo identification of differentially methylated sequence motifs, MPore starts by identifying potential methyltransferase genes by homology search against REBASE; activity is then assessed through a regularized logistic regression model of observed genome-wide methylation patterns, integrating motif and genomic sequence context information.Results On two benchmarking datasets, 10 bacterial monocultures and two Helicobacter pylori genomes with complex methylation patterns, MPore achieved a combined recall of 93% and a combined PPV of 96%, outperforming Nanomotif (81%/91%), Modkit (66%/4%), and Snappy (89%/50%). Further validation on a well-characterized dataset of Mycoplasma hominis isolates showed perfect agreement with wet-lab-based validation results and demonstrated that MPore could complement REBASE information by disambiguating the specific methylated base in a motif with multiple potential methylation sites. MPore automatically produces integrated visualizations of the identified methyltransferases and observed methylation patterns; the tool is implemented as a user-friendly Snakemake pipeline.Availability and implementation MPore is freely available under the MIT license at https://github.com/DiltheyLab/MPore.

The majority of SARS-CoV-2 genomes obtained during the pandemic were derived by amplifying overlapping windows of the genome (‘tiled amplicons’), reconstructing their sequences and fitting them together. This leads to systematic errors in genomes unless the software is both aware of the amplicon scheme and of the error modes of amplicon sequencing. Additionally, over time, amplicon schemes need to be updated as new mutations in the virus interfere with the primer binding sites at the end of amplicons. Thus, waves of variants swept the world during the pandemic and were followed by waves of systematic errors in the genomes, which had significant impacts on the inferred phylogenetic tree. Here we reconstruct the genomes from all public data as of June 2024 using an assembly tool called Viridian ( https://github.com/iqbal-lab-org/viridian ), developed to rigorously process amplicon sequence data. With these high-quality consensus sequences we provide a global phylogenetic tree of 4,471,579 samples, viewable at https://viridian.taxonium.org . We provide simulation and empirical validation of the methodology, and quantify the improvement in the phylogeny.

Respiratory syncytial virus (RSV) is a globally circulating virus, causing severe respiratory infections in infants and the elderly. Two RSV vaccines were recently approved, and passive immunization is now recommended in several countries for all newborns, therefore careful surveillance of RSV variants will be important in the future. We therefore develop an integrated whole genome RSV amplification, sequencing and bioinformatics analysis method („RSVTyper“; https://anaconda.org/bioconda/rsv-typer ) that is suitable for patient samples as well as wastewater. 243 RSV isolates from 2008 to 2025 and wastewater samples from 2023/2024 were amplified in a multiplex tiling PCR with specific primers, generating 39 amplicons ~ 550 bp in length, and sequenced with Oxford Nanopore Technologies. Sequencing reads of patient isolates were analyzed with the RSVTyper pipeline, a tailored analysis pipeline including automatic reference selection, consensus sequence generation and clade assignment via Nextclade. Amplification and sequencing were successful for 213/243 samples. Whole genomes (> 90% coverage) were obtained from 98% of samples with > 10,000 copies/mL, from 3/14 samples with 1,000–10,000 copies/mL, and from none with < 1,000 copies/mL. Average genome-wide mean depth for successfully sequenced samples was 31,800x with an average mean depth of 41,400x in the F gene. Phylogenetic analysis showed seasonal clade and subtype shifting, with a good representation of clade frequencies from patients in wastewater sequences. No variants with known escape mutations from prophylactic monoclonal antibodies were detected. In conclusion, we developed RSVTyper, a cost-effective and scalable RSV sequencing pipeline by integrating sequencing and bioinformatic analysis. It is suitable for both resource-limited settings and high-throughput applications. It will facilitate enhanced RSV surveillance, allowing for further characterization of the RSV genome and rapid detection of potential escape mutations.

Background: Integrated genomic surveillance (IGS), i.e. the integrated analysis of pathogen whole genome sequencing and classical epidemiological data, can contribute substantially to the disease surveillance and infection prevention activities of local public health authorities (LPHAs). Aim: Our aim was to characterise how LPHAs use IGS, and factors required or important for their implementation, in the context of the German public health system. Methods: We employed a mixed-methods design combining a quantitative survey of 60 LPHAs in three German states with five qualitative case studies based on LPHAs in four German localities and one state-level public health authority. Results: Approximately half of LPHAs reported adoption of IGS; applications included outbreak analysis (n = 25), targeting and evaluation of infection control measures (n = 25 and n = 18, respectively) and characterisation of pathogen transmission chains (n = 25). Factors identified as required or important for the implementation of IGS in LPHAs included fast sample-to-result turnaround times, organisational data interpretation capabilities and clearly defined surveillance sampling strategies. Based on the case studies in which the adoption of IGS was successful, we formulate recommendations for implementing IGS at the level of LPHAs, including establishment of dedicated IGS analysis teams within LPHAs, use of user-friendly digital solutions (e.g. browser-based dashboards) for data exchange and analysis, and implementation of IGS in collaboration with local academic institutions. Conclusion: Our analysis paves the way for increasing the implementation of IGS by LPHAs in Germany and other countries with similarly structured public health systems.

The vaginal microbiome plays an important role in female health; it is associated with reproductive success, susceptibility to sexually transmitted infections, and, importantly, the most prevalent vaginal condition in reproduction-age women, bacterial vaginosis (BV). Traditionally, 16S rRNA gene sequencing-based approaches have been used to characterize the composition of vaginal microbiomes, but shallow shotgun metagenomic sequencing (SMS) approaches, in particular when implemented with the Oxford Nanopore Technologies, have important potential advantages with respect to cost effectiveness, speed of data generation, and the availability of flexible multiplexing schemes. Based on a study cohort of n = 52 women, of which 23 were diagnosed with BV, we evaluated the applicability of Nanopore-based SMS for the characterization of vaginal microbiomes in direct comparison to Illumina 16S-based sequencing. We observed perfect agreement between the two approaches with respect to detecting the dominance of individual samples by either Lactobacilli, vaginosis-associated, or other taxa; very high concordance (92

Toxoplasma gondii is an important pathogen and model organism for studying mechanisms of immune evasion and defense. Within the same strain, model organisms are typically assumed to be isogenic; for T. gondii, within-strain genetic divergence has been detected based on phenotypic changes and older molecular techniques but not characterized at the genomic level. We therefore used Oxford Nanopore long-read sequencing to characterize three independently maintained T. gondii ME49 isolates: 2015T and 2020T (obtained from ATCC and propagated in cell culture), and 2000B (propagated in mice). We de novo assembled a new T. gondii ME49 reference genome and, using state-of-the-art variant calling combined with pangenomic genotyping, detected variants between the sequenced isolates. Our new reference genome exceeded existing reference genomes in continuity (NG50 = 6.68 Mb versus 1.2 Mb in RefSeq) and structural accuracy, resolving all chromosomes except for a single break in the ribosomal DNA region. For isolates 2000B and 2020T, we identified 106 and 128 variants, respectively, across a final call set of 79 SNVs, 93 INDELs, and five structural variants; 18 small non-synonymous variants included genes associated with T. gondii life cycle (AP2X-8) and virulence in vivo (6-phosphogluconate dehydrogenase). A 13 kb expansion in the ROP8-ROP2A virulence locus increased the copy number of ROP2A-ROP8 genes in isolates 2000B and 2020T from three to six. We provide an improved T. gondii ME49 reference genome and demonstrate the potentially confounding effect of intra-strain genetic heterogeneity, highlighting the need for continuous genomic monitoring for long-term genetic identity.

BACKGROUND:The emergence of resistance-associated substitutions in RSV against novel monoclonal antibodies is a concern given widespread prophylactic use. AIM:To assess the prevalence of resistance-associated substitutions in the RSV F protein against nirsevimab, clesrovimab, and palivizumab in German infants before widespread implementation of nirsevimab. MATERIALS & METHODS:We sequenced the F protein of n = 1042 RSV samples from German infants from seasons 2021/2022 and 2022/2023 and screened for variants in binding sites for nirsevimab (Site Ø), clesrovimab (Site IV), and palivizumab (Site II). RESULTS:Prevalence of resistance-associated substitutions was low (< 1%) for all three monoclonal antibodies. CONCLUSION:Although the current risk of infections with escape-mutants appears to be low, our results underline the need for continued surveillance, as resistance-conferring mutations to new mAbs circulated and may be selected under selection pressure.

The importance of genomic surveillance strategies for pathogens has been particularly evident during the coronavirus disease 2019 (COVID-19) pandemic, as genomic data from the causative agent, severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), have guided public health decisions worldwide. Bayesian phylodynamic inference, integrating epidemiology and evolutionary biology, has become an essential tool in genomic epidemiological surveillance. It enables the estimation of epidemiological parameters, such as the reproductive number, from pathogen sequence data alone. Despite the phylodynamic approach being widely adopted, the abundance of phylodynamic models often makes it challenging to select the appropriate model for specific research questions. This article illustrates the application of phylodynamic birth-death-sampling models in public health using genomic data, with a focus on SARS-CoV-2. Targeting researchers less familiar with phylodynamics, it introduces a comprehensive workflow, including the conceptualisation of a research study and detailed steps for data preprocessing and postprocessing. In addition, we demonstrate the versatility of birth-death-sampling models through three case studies from Germany, utilising the BEAST2 software and its model implementations. Each case study addresses a distinct research question relevant not only to SARS-CoV-2 but also to other pathogens: Case study 1 finds traces of a superspreading event at the start of an early outbreak, exemplifying how simple models for genomic data can provide information that would otherwise only be accessible through extensive contact tracing. Case study 2 compares transmission dynamics in a nosocomial outbreak to community transmission, highlighting distinct dynamics through integrative analysis. Case study 3 investigates whether local transmission patterns align with national trends, demonstrating how phylodynamic models can disentangle complex population substructure with little additional information. For each case study, we emphasise critical points where model assumptions and data properties may misalign and outline appropriate validation assessments. Overall, we aim to provide researchers with examples on using birth-death-sampling models in genomic epidemiology, balancing theoretical and practical aspects.

Affordable genotyping methods are essential in genomics. Commonly used genotyping methods primarily support single nucleotide variants and short indels but neglect structural variants. Additionally, accuracy of read alignments to a reference genome is unreliable in highly polymorphic and repetitive regions, further impacting genotyping performance. Recent works highlight the advantage of haplotype-resolved pangenome graphs in addressing these challenges. Building on these developments, we propose a rigorous alignment-free genotyping framework. Our formulation seeks a path through the pangenome graph that maximizes the matches between the path and substrings of sequencing reads (e.g., k-mers) while minimizing recombination events (haplotype switches) along the path. We prove that this problem is NP-hard and develop efficient integer-programming solutions. We benchmarked the algorithm using downsampled short-read datasets from homozygous human cell lines with coverage ranging from 0.1× to 10× . Our algorithm accurately estimates complete major histocompatibility complex (MHC) haplotype sequences with small edit distances from the ground-truth sequences, providing a significant advantage over existing methods on low-coverage inputs. While the current design of the algorithm is applicable to haploid samples, we outline potential directions for extending it to diploid samples.

Immune evasion is a hallmark of gliomas, yet the genetic mechanisms by which tumors escape immune surveillance remain incompletely understood. In this study, we systematically examined the presence of somatic mutations in HLA genes and genes encoding proteins involved in antigen presentation across isocitrate dehydrogenase wild-type and mutant gliomas using targeted next-generation sequencing. To address the challenges associated with detecting somatic mutations in these highly polymorphic and complex regions of the genome, we applied a combination of short-read and long-read sequencing techniques, extended the genetic region of interest (exons and introns), and applied a tailored bioinformatics analysis pipeline, which enabled an accurate evaluation of comprehensive sequencing data. Our analysis identified mutations in HLA class II and nonclassic HLA genes as well as genes associated with antigen presentation, such as TAP1/2 and B2M. Three-dimensional modeling of individual mutations simulated the potential impact of somatic mutations in TAP1 and B2M on the encoded protein configuration. The presence of somatic mutations supports the role of antigen-presenting genes in the pathophysiology and potential immune escape of gliomas. Our data demonstrated an increased frequency of such mutations in recurrent glioblastoma, potentially resulting from a positive selection or mutagenic enrichment of tumor cells during tumor progression. Taken together, this research generates new insights and hypotheses for the functional analysis and optimization of immunotherapy strategies for gliomas, which may guide personalized treatment paradigms.

AbstractAbnormal female reproductive tract microbiota are associated with gynecological disorders such as endometriosis or chronic endometritis and may affect reproductive outcomes. However, the differential diagnostic utility of the vaginal or the endometrial microbiome and the impact of important technical covariates such as the choice of hypervariable regions for 16 S rRNA sequencing remain to be characterized. The aim of this retrospective study was to compare vaginal and endometrial microbiomes in IVF patients diagnosed with implantation failure (IF) and/or recurrent pregnancy loss (RPL) and to investigate the overlap between established vaginal and endometrial microbiome classification schemes. An additional aim was to characterize to which extent the choice of V1-V2 or V2-V3 16 S rRNA sequencing schemes influences the characterization of genital microbiomes. We compared microbiome composition based on V1-V2 rRNA sequencing between matched vaginal smear and endometrial pipelle-obtained biopsy samples (n = 71); in a sub-group (n = 61), we carried out a comparison between V1-V2 and V2-V3 rRNA sequencing. Vaginal and endometrial microbiomes were found to be Lactobacillus-dominated in the majority of patients, with the most abundant Lactobacillus species typically shared between sample types of same patient. Endometrial microbiomes were found to be more diverse than vaginal microbiomes (average Shannon entropy = 1.89 v/s 0.75, p = 10−5) and bacterial species such as Corynebacterium sp., Staphylococcus sp., Prevotella sp. and Propionibacterium sp. were found to be enriched in the endometrial samples. The use of two widely used clinical classification schemes to detect microbiome dysbiosis in the reproductive tract often led to inconsistent results vaginal community state type (CST) IV, which is associated with bacterial vaginosis, was detected in 9.8% of patients; however, 31,0% of study participants had a non-Lactobacillus-dominated (NLD) endometrial microbiome associated with unfavorable reproductive outcomes. Results based on V2-V3 rRNA sequencing were generally consistent with V1-V2-based; differences were observed for a small number of species, e.g. Bifidobacterium sp., Propionibacterium sp. and Staphylococcus sp. and with respect to slightly increased detection rates of CST IV and NLD. Our study showed that endometrial microbiomes differ substantially from their vaginal counterparts, the application of a trans-cervical sampling method notwithstanding. Characterization of endometrial microbiomes may contribute to the improved detection of women with an unfavorable reproductive outcome prognosis in IVF patients..

The gut microbiome is a diverse ecosystem, dominated by bacteria; however, fungi, phages/viruses, archaea, and protozoa are also important members of the gut microbiota. Exploration of taxonomic compositions beyond bacteria as well as an understanding of the interaction between the bacteriome with the other members is limited using 16S rDNA sequencing. Here, we developed a pipeline enabling the simultaneous interrogation of the gut microbiome (bacteriome, mycobiome, archaeome, eukaryome, DNA virome) and of antibiotic resistance genes based on optimized long-read shotgun metagenomics protocols and custom bioinformatics. Using our pipeline we investigated the longitudinal composition of the gut microbiome in an exploratory clinical study in patients undergoing allogeneic hematopoietic stem cell transplantation (alloHSCT; n = 31). Pre-transplantation microbiomes exhibited a 3-cluster structure, characterized by Bacteroides spp. /Phocaeicola spp., mixed composition and Enterococcus abundances. We revealed substantial inter-individual and temporal variabilities of microbial domain compositions, human DNA, and antibiotic resistance genes during the course of alloHSCT. Interestingly, viruses and fungi accounted for substantial proportions of microbiome content in individual samples. In the course of HSCT, bacterial strains were stable or newly acquired. Our results demonstrate the disruptive potential of alloHSCTon the gut microbiome and pave the way for future comprehensive microbiome studies based on long-read metagenomics.

MOTIVATION:Microbial sequencing data from clinical samples is often contaminated with human sequences, which have to be removed prior to sharing. Existing methods for human read removal, however, are applicable only after the target dataset has been retrieved in its entirety, putting the recipient at least temporarily in control of a potentially identifiable genetic dataset with potential implications under regulatory frameworks such as the GDPR. In some instances, the ability to carry out stream-based host depletion as part of the data transfer process may be preferable. RESULTS:We present SWGTS, a client-server application for the transfer and stream-based host depletion of sequencing reads. SWGTS enforces a robust upper bound on the maximum amount of human genetic data from any one client held in memory at any point in time by storing all incoming sequencing data in a limited-size, client-specific intermediate processing buffer, and by throttling the rate of incoming data if it exceeds the speed of host depletion carried out on the SWGTS server in the background. SWGTS exposes a HTTP-REST interface, is implemented using docker-compose, Redis and traefik, and requires less than 8 Gb of RAM for deployment. We demonstrate high filtering accuracy of SWGTS; incoming data transfer rates of up to 1.65 megabases per second in a conservative configuration; and mitigation of re-identification risks by the ability to limit the number of SNPs present on a popular population-scale genotyping array covered by reads in the SWGTS buffer to a low user-defined number, such as 10 or 100. AVAILABILITY AND IMPLEMENTATION:SWGTS is available on GitHub: https://github.com/AlBi-HHU/swgts (https://doi.org/10.5281/zenodo.10891052). The repository also contains a jupyter notebook that can be used to reproduce all the benchmarks used in this article. All datasets used for benchmarking are publicly available.

16S rRNA targeted amplicon sequencing is an established standard for elucidating microbial community composition. While high-throughput short-read sequencing can elicit only a portion of the 16S rRNA gene due to their limited read length, third generation sequencing can read the 16S rRNA gene in its entirety and thus provide more precise taxonomic classification. Here, we present a protocol for generating full-length 16S rRNA sequences with Oxford Nanopore Technologies (ONT) and a microbial community profile with Emu. We select Emu for analyzing ONT sequences as it leverages information from the entire community to overcome errors due to incomplete reference databases and hardware limitations to ultimately obtain species-level resolution. This pipeline provides a low-cost solution for characterizing microbiome composition by exploiting real-time, long-read ONT sequencing and tailored software for accurate characterization of microbial communities. (c) 2024 Wiley Periodicals LLC.Basic Protocol: Microbial community profiling with EmuSupport Protocol 1: Full-length 16S rRNA microbial sequences with Oxford Nanopore Technologies sequencing platformSupport Protocol 2: Building a custom reference database for Emu

How human genetic variation contributes to vaccine immunogenicity and effectiveness is unclear, particularly in infants from Africa. We undertook genome-wide association analyses of eight vaccine antibody responses in 2,499 infants from three African countries and identified significant associations across the human leukocyte antigen (HLA) locus for five antigens spanning pertussis, diphtheria and hepatitis B vaccines. Using high-resolution HLA typing in 1,706 individuals from 11 African populations we constructed a continental imputation resource to fine-map signals of association across the class II HLA observing genetic variation explaining up to 10% of the observed variance in antibody responses. Using follicular helper T-cell assays, in silico binding, and immune cell eQTL datasets we find evidence of HLA-DRB1 expression correlating with serological response and inferred protection from pertussis following vaccination. This work improves our understanding of molecular mechanisms underlying HLA associations that should support vaccine design and development across Africa with wider global relevance.

Genomic surveillance enables the early detection of pathogen transmission in healthcare facilities and contributes to the reduction of substantial patient harm. Fast turnaround times, flexible multiplexing, and low capital requirements make Nanopore sequencing well suited for genomic surveillance purposes; the analysis of Nanopore data, however, can be challenging. We present NanoCore, a user-friendly method for Nanopore-based genomic surveillance in healthcare facilities, enabling the calculation and visualization of cgMLST-like (core-genome multilocus sequence typing) sample distances directly from unassembled Nanopore reads. NanoCore implements a mapping, variant calling, and multilevel filtering strategy and also supports the analysis of Illumina data. We validated NanoCore on two 24-isolate data sets of methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Enterococcus faecium (VRE). In the Nanopore-only mode, NanoCore-based pairwise distances between closely related isolates were near-identical to Illumina-based SeqSphere+ distances, a gold standard commercial method (average differences of 0.75 and 0.81 alleles for MRSA and VRE; sd = 0.98 and 1.00), and gave an identical clustering into closely related and non-closely related isolates. In the "hybrid" mode, in which only Nanopore data are used for some isolates and only Illumina data for others, increased average pairwise isolate distance differences were observed (average differences of 3.44 and 1.95 for MRSA and VRE, respectively; sd = 2.76 and 1.34), while clustering results remained identical. NanoCore is computationally efficient (<15 hours of wall time for the analysis of a 24-isolate data set on a workstation), available as free software, and supports installation via conda. In conclusion, NanoCore enables the effective use of the Nanopore technology for bacterial pathogen surveillance in healthcare facilities. IMPORTANCE:Genomic surveillance involves sequencing the genomes and measuring the relatedness of bacteria from different patients or locations in the same healthcare facility, enabling an improved understanding of pathogen transmission pathways and the detection of "silent" outbreaks that would otherwise go undetected. It has become an indispensable tool for the detection and prevention of healthcare-associated infections and is routinely applied by many healthcare institutions. The earlier an outbreak or transmission chain is detected, the better; in this context, the Oxford Nanopore sequencing technology has important potential advantages over traditionally used short-read sequencing technologies, because it supports "real-time" data generation and the cost-effective "on demand" sequencing of small numbers of bacterial isolates. The analysis of Nanopore sequencing data, however, can be challenging. We present NanoCore, a user-friendly software for genomic surveillance that works directly based on Nanopore sequencing reads in FASTQ format, and demonstrate that its accuracy is equivalent to traditional gold standard short read-based analyses.