NanoMGT: Marker gene typing of low complexity mono-species metagenomic samples using noisy long reads.

Rapid advancements in sequencing technologies have led to significant progress in microbial genomics, yet challenges persist in accurately identifying microbial strain diversity in metagenomic samples, especially when working with noisy long-read data from platforms like Oxford Nanopore Technologies (ONT). In this article, we introduce NanoMGT, a tool designed to enhance marker gene typing in low-complexity mono-species samples, leveraging the unique properties of long reads. NanoMGT excels in its ability to accurately identify mutations amidst high error rates, ensuring the reliable detection of multiple strain-specific marker genes. Our tool implements a novel scoring system that rewards mutations co-occurring across different reads and penalizes densely grouped, likely erroneous variants, thereby achieving a good balance between sensitivity and precision. A comparative evaluation of NanoMGT, using a simulated multi-strain sample of seven bacterial species, demonstrated superior performance relative to existing tools and the advantages of using a threshold-based filtering approach to calling minority variants in ONT's sequencing data. NanoMGT's potential as a post-binning tool in metagenomic pipelines is particularly notable, enabling researchers to more accurately determine specific alleles and understand strain diversity in microbial communities. Our findings have significant implications for clinical diagnostics, environmental microbiology, and the broader field of genomics. The findings offer a reliable and efficient approach to marker gene typing in complex metagenomic samples.

Real-time PCR assay to detect the novel Clade Ib monkeypox virus, September 2023 to May 2024.

Monkeypox virus (MPXV) is an emerging zoonotic pathogen with complex epidemiology necessitating rapid diagnosis and distinguishing between clades and subclades. The emerging Clade Ib lacks the genomic region used in the Clade I-specific assay from the Centers for Disease Control and Prevention. We report an MPXV real-time PCR to specifically detect Clade Ib. The assay demonstrated proficient sensitivity and specificity in 92 samples and can be included along other TaqMan-based assays to detect MPXV and distinguish between clades and subclades.

Time-series sewage metagenomics distinguishes seasonal, human-derived and environmental microbial communities potentially allowing source-attributed surveillance.

Sewage metagenomics has risen to prominence in urban population surveillance of pathogens and antimicrobial resistance (AMR). Unknown species with similarity to known genomes cause database bias in reference-based metagenomics. To improve surveillance, we seek to recover sewage genomes and develop a quantification and correlation workflow for these genomes and AMR over time. We use longitudinal sewage sampling in seven treatment plants from five major European cities to explore the utility of catch-all sequencing of these population-level samples. Using metagenomic assembly methods, we recover 2332 metagenome-assembled genomes (MAGs) from prokaryotic species, 1334 of which were previously undescribed. These genomes account for ~69% of sequenced DNA and provide insight into sewage microbial dynamics. Rotterdam (Netherlands) and Copenhagen (Denmark) show strong seasonal microbial community shifts, while Bologna, Rome, (Italy) and Budapest (Hungary) have occasional blooms of Pseudomonas-dominated communities, accounting for up to ~95% of sample DNA. Seasonal shifts and blooms present challenges for effective sewage surveillance. We find that bacteria of known shared origin, like human gut microbiota, form communities, suggesting the potential for source-attributing novel species and their ARGs through network community analysis. This could significantly improve AMR tracking in urban environments.

SpanSeq: similarity-based sequence data splitting method for improved development and assessment of deep learning projects.

The use of deep learning models in computational biology has increased massively in recent years, and it is expected to continue with the current advances in the fields such as Natural Language Processing. These models, although able to draw complex relations between input and target, are also inclined to learn noisy deviations from the pool of data used during their development. In order to assess their performance on unseen data (their capacity to generalize), it is common to split the available data randomly into development (train/validation) and test sets. This procedure, although standard, has been shown to produce dubious assessments of generalization due to the existing similarity between samples in the databases used. In this work, we present SpanSeq, a database partition method for machine learning that can scale to most biological sequences (genes, proteins and genomes) in order to avoid data leakage between sets. We also explore the effect of not restraining similarity between sets by reproducing the development of two state-of-the-art models on bioinformatics, not only confirming the consequences of randomly splitting databases on the model assessment, but expanding those repercussions to the model development. SpanSeq is available at https://github.com/genomicepidemiology/SpanSeq.

Discovery of Vibrio cholerae in Urban Sewage in Copenhagen, Denmark.

We report the discovery of a persistent presence of Vibrio cholerae at very low abundance in the inlet of a single wastewater treatment plant in Copenhagen, Denmark at least since 2015. Remarkably, no environmental or locally transmitted clinical case of V. cholerae has been reported in Denmark for more than 100 years. We, however, have recovered a near-complete genome out of 115 metagenomic sewage samples taken over the past 8 years, despite the extremely low relative abundance of one V. cholerae read out of 500,000 sequenced reads. Due to the very low relative abundance, routine screening of the individual samples did not reveal V. cholerae. The recovered genome lacks the gene responsible for cholerae toxin production, but although this strain may not pose an immediate public health risk, our finding illustrates the importance, challenges, and effectiveness of wastewater-based pathogen surveillance.

Utilizing co-abundances of antimicrobial resistance genes to identify potential co-selection in the resistome.

The rapid spread of antimicrobial resistance (AMR) is a threat to global health, and the nature of co-occurring antimicrobial resistance genes (ARGs) may cause collateral AMR effects once antimicrobial agents are used. Therefore, it is essential to identify which pairs of ARGs co-occur. Given the wealth of next-generation sequencing data available in public repositories, we have investigated the correlation between ARG abundances in a collection of 214,095 metagenomic data sets. Using more than 6.76∙108 read fragments aligned to acquired ARGs to infer pairwise correlation coefficients, we found that more ARGs correlated with each other in human and animal sampling origins than in soil and water environments. Furthermore, we argued that the correlations could serve as risk profiles of resistance co-occurring to critically important antimicrobials (CIAs). Using these profiles, we found evidence of several ARGs conferring resistance for CIAs being co-abundant, such as tetracycline ARGs correlating with most other forms of resistance. In conclusion, this study highlights the important ARG players indirectly involved in shaping the resistomes of various environments that can serve as monitoring targets in AMR surveillance programs. IMPORTANCE: Understanding the collateral effects happening in a resistome can reveal previously unknown links between antimicrobial resistance genes (ARGs). Through the analysis of pairwise ARG abundances in 214K metagenomic samples, we observed that the co-abundance is highly dependent on the environmental context and argue that these correlations can be used to show the risk of co-selection occurring in different settings.

Metagenomic analysis of sewage for surveillance of bacterial pathogens: A release experiment to determine sensitivity.

Accurate monitoring of gastro-enteric and other diseases in large populations poses a challenge for public health management. Sewage represents a larger population, is freely obtainable and non-subject to ethical approval. Metagenomic sequencing offers simultaneous, multiple-target analysis. However, no study has demonstrated the sensitivity of metagenomics for detecting bacteria in sewage. In this study, we spot-released 1013 colony-forming units (CFU) of Staphyloccus hyicus (non-pathogenetic strain 842J-88). The strain was flushed down a toilet into the sewer in the catchment area of a public wastewater treatment plant (WWTP), serving a population of 36,000 people. Raw sewage was continuously sampled at the WWTP's inlet over 30- and 60-minute intervals for a total period of seven hours. The experiment was conducted twice with one week in-between release days and under comparable weather conditions. For the metagenomics analyses, the pure single isolate of S. hyicus was sequenced, assembled and added to a large database of bacterial reference sequences. All sewage samples were analyzed by shotgun metagenome sequencing and mapped against the reference database. S. hyicus was identified in duplicate samples at both of two release days and these sequence fragment counts served as a proxy to estimate the minimum number of sick people or sensitivity required in order to observe at least one sick person at 95% probability. We found the sensitivity to be in the range 41-140 and 16-36 sick people at release days 1 and 2, respectively. The WWTP normally serves 36,000 people giving a normalized sensitivity in the range of one in 257 to 2,250 persons.

Complete genome sequences of Cytobacillus sp., Domibacillus sp., Enterobacter sp., Neisseria sp., Pseudomonas sp., and Streptococcus sp. strains from human clinical infections collected at diagnostic units in 2020.

Members of Bacillota and Pseudomonadota phyla are frequently considered bacterial infectious agents in humans. As part of a large sequencing project of clinically relevant pathogens, we hybrid-assembled complete genomes of Cytobacillus, Domibacillus, Enterobacter, Neisseria, Pseudomonas, and Streptococcus species isolated from clinical specimens.

Genomic analysis of Salmonella isolated from canal water in Bangkok, Thailand.

Antimicrobial resistance (AMR) poses an escalating global public health threat. Canals are essential in Thailand, including the capital city, Bangkok, as agricultural and daily water sources. However, the characteristic and antimicrobial-resistance properties of the bacteria in the urban canals have never been elucidated. This study employed whole genome sequencing to characterize 30 genomes of a causal pathogenic bacteria, Salmonella enterica, isolated from Bangkok canal water between 2016 and 2020. The dominant serotype was Salmonella Agona. In total, 35 AMR genes and 30 chromosomal-mediated gene mutations were identified, in which 21 strains carried both acquired genes and mutations associated with fluoroquinolone resistance. Virulence factors associated with invasion, adhesion, and survival during infection were detected in all study strains. 75.9% of the study stains were multidrug-resistant and all the strains harbored the necessary virulence factors associated with salmonellosis. One strain carried 20 resistance genes, including mcr-3.1, mutations in GyrA, ParC, and ParE, and typhoid toxin-associated genes. Fifteen plasmid replicon types were detected, with Col(pHAD28) being the most common type. Comparative analysis of nine S. Agona from Bangkok and 167 from public databases revealed that specific clonal lineages of S. Agona might have been circulating between canal water and food sources in Thailand and globally. These findings provide insight into potential pathogens in the aquatic ecosystem and support the inclusion of environmental samples into comprehensive AMR surveillance initiatives as part of a One Health approach. This approach aids in comprehending the rise and dissemination of AMR and devising sustainable intervention strategies.IMPORTANCEBangkok is the capital city of Thailand and home to a large canal network that serves the city in various ways. The presence of pathogenic and antimicrobial-resistant Salmonella is alarming and poses a significant public health risk. The present study is the first characterization of the genomic of Salmonella strains from Bangkok canal water. Twenty-two of 29 strains (75.9%) were multidrug-resistant Salmonella and all the strains carried essential virulence factors for pathogenesis. Various plasmid types were identified in these strains, potentially facilitating the horizontal transfer of AMR genes. Additional investigations indicated a potential circulation of S. Agona between canal water and food sources in Thailand. The current study underscores the role of environmental water in an urban city as a reservoir of pathogens and these data obtained can serve as a basis for public health risk assessment and help shape intervention strategies to combat AMR challenges in Thailand.

Ongoing mpox outbreak in Kamituga, South Kivu province, associated with monkeypox virus of a novel Clade I sub-lineage, Democratic Republic of the Congo, 2024.

Since the beginning of 2023, the number of people with suspected monkeypox virus (MPXV) infection have sharply increased in the Democratic Republic of the Congo (DRC). We report near-to-complete MPXV genome sequences derived from six cases from the South Kivu province. Phylogenetic analyses reveal that the MPXV affecting the cases belongs to a novel Clade I sub-lineage. The outbreak strain genome lacks the target sequence of the probe and primers of a commonly used Clade I-specific real-time PCR.

The European livestock resistome.

Metagenomic sequencing has proven to be a powerful tool in the monitoring of antimicrobial resistance (AMR). Here, we provide a comparative analysis of the resistome from pigs, poultry, veal calves, turkey, and rainbow trout, for a total of 538 herds across nine European countries. We calculated the effects of per-farm management practices and antimicrobial usage (AMU) on the resistome in pigs, broilers, and veal calves. We also provide an in-depth study of the associations between bacterial diversity, resistome diversity, and AMR abundances as well as co-occurrence analysis of bacterial taxa and antimicrobial resistance genes (ARGs) and the universality of the latter. The resistomes of veal calves and pigs clustered together, as did those of avian origin, while the rainbow trout resistome was different. Moreover, we identified clear core resistomes for each specific food-producing animal species. We identified positive associations between bacterial alpha diversity and both resistome alpha diversity and abundance. Network analyses revealed very few taxa-ARG associations in pigs but a large number for the avian species. Using updated reference databases and optimized bioinformatics, previously reported significant associations between AMU, biosecurity, and AMR in pig and poultry farms were validated. AMU is an important driver for AMR; however, our integrated analyses suggest that factors contributing to increased bacterial diversity might also be associated with higher AMR load. We also found that dispersal limitations of ARGs are shaping livestock resistomes, and future efforts to fight AMR should continue to emphasize biosecurity measures.IMPORTANCEUnderstanding the occurrence, diversity, and drivers for antimicrobial resistance (AMR) is important to focus future control efforts. So far, almost all attempts to limit AMR in livestock have addressed antimicrobial consumption. We here performed an integrated analysis of the resistomes of five important farmed animal populations across Europe finding that the resistome and AMR levels are also shaped by factors related to bacterial diversity, as well as dispersal limitations. Thus, future studies and interventions aimed at reducing AMR should not only address antimicrobial usage but also consider other epidemiological and ecological factors.

Dispersion of antimicrobial resistant bacteria in pig farms and in the surrounding environment.

BACKGROUND: Antimicrobial resistance has been identified as a major threat to global health. The pig food chain is considered an important source of antimicrobial resistance genes (ARGs). However, there is still a lack of knowledge on the dispersion of ARGs in pig production system, including the external environment. RESULTS: In the present study, we longitudinally followed one swine farm located in Italy from the weaning phase to the slaughterhouse to comprehensively assess the diversity of ARGs, their diffusion, and the bacteria associated with them. We obtained shotgun metagenomic sequences from 294 samples, including pig feces, farm environment, soil around the farm, wastewater, and slaughterhouse environment. We identified a total of 530 species-level genome bins (SGBs), which allowed us to assess the dispersion of microorganisms and their associated ARGs in the farm system. We identified 309 SGBs being shared between the animals gut microbiome, the internal and external farm environments. Specifically, these SGBs were characterized by a diverse and complex resistome, with ARGs active against 18 different classes of antibiotic compounds, well matching antibiotic use in the pig food chain in Europe. CONCLUSIONS: Collectively, our results highlight the urgency to implement more effective countermeasures to limit the dispersion of ARGs in the pig food systems and the relevance of metagenomics-based approaches to monitor the spread of ARGs for the safety of the farm working environment and the surrounding ecosystems.

A novel metagenomic approach uncovers phage genes as markers for increased disinfectant tolerance in mixed Listeria monocytogenes communities.

Listeria monocytogenes is an important human pathogen with a high mortality rate. Consumption of contaminated ready-to-eat food is the main mode of transmission to humans. Disinfectant-tolerant L. monocytogenes have emerged, which are believed to have increased persistence potential. Elucidating the mechanisms of L. monocytogenes disinfectant tolerance has been the focus of previous studies using pure cultures. A limitation of such approach is the difficulty to identify strains with reduced susceptibility due to inter-strain variation and the need to screen large numbers of strains and genes. In this study, we applied a novel metagenomic approach to detect genes associated with disinfectant tolerance in mixed L. monocytogenes planktonic communities. Two communities, consisting of 71 and 80 isolates each, were treated with the food industry disinfectants benzalkonium chloride (BC, 1.75 mg/L) or peracetic acid (PAA, 38 mg/L). The communities were subjected to metagenomic sequencing and differences in individual gene abundances between biocide-free control communities and biocide-treated communities were determined. A significant increase in the abundance of Listeria phage-associated genes was observed in both communities after treatment, suggesting that prophage carriage could lead to an increased disinfectant tolerance in mixed L. monocytogenes planktonic communities. In contrast, a significant decrease in the abundance of a high-copy emrC-harbouring plasmid pLmN12-0935 was observed in both communities after treatment. In PAA-treated community, a putative ABC transporter previously found to be necessary for L. monocytogenes resistance to antimicrobial agents and virulence, was among the genes with the highest weight for differentiating treated from control samples. The undertaken metagenomic approach in this study can be applied to identify genes associated with increased tolerance to other antimicrobials in mixed bacterial communities.

ARGprofiler-a pipeline for large-scale analysis of antimicrobial resistance genes and their flanking regions in metagenomic datasets.

MOTIVATION: Analyzing metagenomic data can be highly valuable for understanding the function and distribution of antimicrobial resistance genes (ARGs). However, there is a need for standardized and reproducible workflows to ensure the comparability of studies, as the current options involve various tools and reference databases, each designed with a specific purpose in mind. RESULTS: In this work, we have created the workflow ARGprofiler to process large amounts of raw sequencing reads for studying the composition, distribution, and function of ARGs. ARGprofiler tackles the challenge of deciding which reference database to use by providing the PanRes database of 14 078 unique ARGs that combines several existing collections into one. Our pipeline is designed to not only produce abundance tables of genes and microbes but also to reconstruct the flanking regions of ARGs with ARGextender. ARGextender is a bioinformatic approach combining KMA and SPAdes to recruit reads for a targeted de novo assembly. While our aim is on ARGs, the pipeline also creates Mash sketches for fast searching and comparisons of sequencing runs. AVAILABILITY AND IMPLEMENTATION: The ARGprofiler pipeline is a Snakemake workflow that supports the reuse of metagenomic sequencing data and is easily installable and maintained at https://github.com/genomicepidemiology/ARGprofiler.

Mobilisation and analyses of publicly available SARS-CoV-2 data for pandemic responses.

The COVID-19 pandemic has seen large-scale pathogen genomic sequencing efforts, becoming part of the toolbox for surveillance and epidemic research. This resulted in an unprecedented level of data sharing to open repositories, which has actively supported the identification of SARS-CoV-2 structure, molecular interactions, mutations and variants, and facilitated vaccine development and drug reuse studies and design. The European COVID-19 Data Platform was launched to support this data sharing, and has resulted in the deposition of several million SARS-CoV-2 raw reads. In this paper we describe (1) open data sharing, (2) tools for submission, analysis, visualisation and data claiming (e.g. ORCiD), (3) the systematic analysis of these datasets, at scale via the SARS-CoV-2 Data Hubs as well as (4) lessons learnt. This paper describes a component of the Platform, the SARS-CoV-2 Data Hubs, which enable the extension and set up of infrastructure that we intend to use more widely in the future for pathogen surveillance and pandemic preparedness.

VirulenceFinder for Enterococcus faecium and Enterococcus lactis: an enhanced database for detection of putative virulence markers by using whole-genome sequencing data.

Enterococcus faecium (Efm) is a leading cause of hospital-associated (HA) infections, often enriched in putative virulence markers (PVMs). Recently, the Efm clade B was assigned as Enterococcus lactis (Elts), which usually lack HA-Efm infection markers. Available databases for extracting PVM are incomplete and/or present an intermix of genes from Efm and Enterococcus faecalis, with distinct virulence profiles. In this study, we constructed a new database containing 27 PVMs [acm, scm, sgrA, ecbA, fnm, sagA, hylEfm, ptsD, orf1481, fms15, fms21-fms20 (pili gene cluster 1, PGC-1), fms14-fms17-fms13 (PGC-2), empA-empB-empC (PGC-3), fms11-fms19-fms16 (PGC-4), ccpA, bepA, gls20-glsB1, and gls33-glsB] from nine reference genomes (seven Efm + two Elts). The database was validated against these reference genomes and further evaluated using a collection of well-characterized Efm (n = 43) and Elts (n = 7) control strains, by assessing PVM presence/absence and its variants together with a genomic phylogeny constructed as single-nucleotide polymorphisms. We found a high concordance between the phylogeny and in silico findings of the PVM, with Elts clustering separately and mostly carrying Elts-specific PVM gene variants. Based on our validation results, we recommend using the database with raw reads instead of assemblies to avoid missing gene variants. This newly constructed database of 27 PVMs will enable a more comprehensive characterization of Efm and Elts based on WGS data. The developed database exhibits scalability and boasts a range of applications in public health, including diagnostics, outbreak investigations, and epidemiological studies. It can be further used in risk assessment for distinguishing between safe and unsafe enterococci.IMPORTANCEThe newly constructed database, consisting of 27 putative virulence markers, is highly scalable and serves as a valuable resource for the comprehensive characterization of these closely related species using WGS data. It holds significant potential for various public health applications, including hospital outbreak investigations, surveillance, and risk assessment for probiotics and feed additives.

West Nile virus spread in Europe: Phylogeographic pattern analysis and key drivers.

BACKGROUND: West Nile virus (WNV) outbreaks in birds, humans, and livestock have occurred in multiple areas in Europe and have had a significant impact on animal and human health. The patterns of emergence and spread of WNV in Europe are very different from those in the US and understanding these are important for guiding preparedness activities. METHODS: We mapped the evolution and spread history of WNV in Europe by incorporating viral genome sequences and epidemiological data into phylodynamic models. Spatially explicit phylogeographic models were developed to explore the possible contribution of different drivers to viral dispersal direction and velocity. A "skygrid-GLM" approach was used to identify how changes in environments would predict viral genetic diversity variations over time. FINDINGS: Among the six lineages found in Europe, WNV-2a (a sub-lineage of WNV-2) has been predominant (accounting for 73% of all sequences obtained in Europe that have been shared in the public domain) and has spread to at least 14 countries. In the past two decades, WNV-2a has evolved into two major co-circulating clusters, both originating from Central Europe, but with distinct dynamic history and transmission patterns. WNV-2a spreads at a high dispersal velocity (88km/yr-215 km/yr) which is correlated to bird movements. Notably, amongst multiple drivers that could affect the spread of WNV, factors related to land use were found to strongly influence the spread of WNV. Specifically, the intensity of agricultural activities (defined by factors related to crops and livestock production, such as coverage of cropland, pasture, cultivated and managed vegetation, livestock density) were positively associated with both spread direction and velocity. In addition, WNV spread direction was associated with high coverage of wetlands and migratory bird flyways. CONCLUSION: Our results suggest that-in addition to ecological conditions favouring bird- and mosquito- presence-agricultural land use may be a significant driver of WNV emergence and spread. Our study also identified significant gaps in data and the need to strengthen virological surveillance in countries of Central Europe from where WNV outbreaks are likely seeded. Enhanced monitoring for early detection of further dispersal could be targeted to areas with high agricultural activities and habitats of migratory birds.

The first complete genome sequence of Staphylococcus aureus ST5477.

This study presents the first complete genome of Staphylococcus aureus ST5477, one of the most common sequence types (ST) from bovine in eastern Africa. The genome consists of a 2,723,132-bp circular chromosome and a 3,044-bp plasmid. This strain was collected in 2017 from cow milk in Tanzania.

Predicting Listeria monocytogenes virulence potential using whole genome sequencing and machine learning.

Contamination with food-borne pathogens, such as Listeria monocytogenes, remains a big concern for food safety. Hence, rigorous and continuous microbial surveillance is a standard procedure. At this point, however, the food industry and authorities only focus on detection of Listeria monocytogenes without characterization of individual strains into groups of more or less concern. As whole genome sequencing (WGS) gains increasing interest in the industry, this methodology presents an opportunity to obtain finer resolution of microbial traits such as virulence. Within this study, we therefore aimed to explore the use of WGS in combination with Machine Learning (ML) to predict L. monocytogenes virulence potential on a sub-species level. The WGS datasets used in this study for ML model training consisted of i) national surveillance isolates (n = 169, covering 38 MLST types) and ii) publicly available isolates acquired through the GenomeTrakr network (n = 2880, spanning 80 MLST types). We used the clinical frequency, i.e., ratio of the number of clinical isolates to total amount of isolates, as estimate for virulence potential. The predictive performance of input features from three different genomic levels (i.e., virulence genes, pan-genome genes, and single nucleotide polymorphisms (SNPs)) and six machine learning algorithms (i.e., Support Vector Machine with a linear kernel, Support Vector Machine with a radial kernel, Random Forrest, Neural Networks, LogitBoost, and Majority Voting) were compared. Our machine learning models predicted sub-species virulence potential with nested cross-validation F1-scores up to 0.88 for the majority voting classifier trained on national surveillance data and using pan-genome genes as input features. The validation of the pre-trained ML models based on 101 previously in vivo studied isolates resulted in F1-scores up to 0.76. Furthermore, we found that the more rapid and less computationally intensive raw read alignment yields comparably accurate models as de novo assembly. The results of our study suggest that a majority voting classifier trained on pan-genome genes is the best and most robust choice for the prediction of clinical frequency. Our study contributes to more rapid and precise characterization of L. monocytogenes virulence and its variation on a sub-species level. We further demonstrated a possible application of WGS data in the context of microbial hazard characterization for food safety. In the future, predictive models may assist case-specific microbial risk management in the food industry. The python code, pre-trained models, and prediction pipeline are deposited at (https://github.com/agmei/LmonoVirulenceML).

Flankophile: a bioinformatic pipeline for prokaryotic genomic synteny analysis.

IMPORTANCE: The Flankophile pipeline enables the analysis and visualization of flanking regions of prokaryotic sequences of interest on large data sets in one step and in a consistent manner. A specific tool for flanking region analysis with automated visualization has not been developed before, and Flankophile will make flanking region analysis easier and accessible to more people. Flankophile will be especially useful in the field of genomic epidemiology of acquired antimicrobial resistance genes. Here, information from flanking region sequences can be instrumental in rejecting or supporting the possibility of a recent common source of the same resistance gene found in different samples.