Kleborate, a genotyping tool with primary utility for genomes belonging to the K. pneumoniae species complex, uses a curated version of the CARD v3.0.8 antimicrobial resistance (AMR) database to screen input genomes for AMR determinants. The spreadsheet contains details for changes made to the database, including exclusions, gene name changes, sequence replacements and additional inclusions of AMR entries from ARGannot.

Antimicrobial resistance (AMR) remains a critical challenge in public health and research. AmrProfiler is a comprehensive tool with three specialized modules: identifying acquired AMR genes, resistance-associated point mutations, and rRNA gene mutations across nearly 18,000 bacterial species. By integrating and refining data from established databases, it provides a robust framework for AMR analysis. AmrProfiler is the first to systematically report mutations in rRNA genes, offering in-depth analysis of rRNA copy numbers and mutations—key for identifying potential rRNA-associated resistance mechanisms. Its curated database includes 7,600 unique AMR gene entries, 245 resistance-related genes, and over 4,300 mutations, alongside a detailed catalog of rRNA gene copy numbers and mutations. Supporting genome assemblies in multiple formats, AmrProfiler allows users to customize detection thresholds for AMR genes, mutations, and rRNA analysis. Validation with Acinetobacter baumannii, Escherichia coli, Klebsiella pneumoniae, Staphylococcus aureus, and Staphylococcus epidermidis demonstrated that AmrProfiler accurately identified all AMR genes and mutations reported by other tools while also detecting additional resistance markers and mutations not previously recognized. By bridging AMR genotypes and phenotypes, AmrProfiler provides actionable insights that advance both research and clinical applications in antimicrobial resistance. AmrProfiler is freely available as an open-access web server without login at https://dianalab.e-ce.uth.gr/amrprofiler .

The uploaded files are folders in zip format to be unzipped inside the folder stored on the GitHub page https://github.com/dianalabgr/amrprofiler. The above folders are needed for AmrProfiler to run locally. The file Readme.txt on the GitHub page explains how to run AmrProfiler locally.

This dataset contains the curated databases used by AMRProfiler. It integrates non-redundant AMR gene sequences and mutation records derived from multiple publicly available sources, including CARD, Reference Gene Catalog, and ResFinder.

Contents of this dataset:

✅ Curated AMRProfiler database with non-redundant AMR gene entries.
✅ Processed metadata and annotations from various AMR databases.
✅ Additional curation and filtering applied for AMRProfiler.

Important Licensing Information:

📌 Included third-party data sources and their licenses:

• Reference Gene Catalog (NCBI) – Public domain, freely available for unrestricted use.
• ResFinder & PointFinder – Apache License 2.0, allowing free use, modification, and distribution.
• AMRProfiler Curation & Metadata – MIT License, allowing free use, modification, and redistribution under its terms.

By using this dataset, users acknowledge that they must comply with the respective licenses of each third-party database.

⚠ This dataset does NOT include raw CARD data. CARD is available for non-commercial academic research only and must be downloaded separately from its official source (https://card.mcmaster.ca/download). Users are responsible for complying with its terms of use.

PanRes database of antimicrobial resistance genes

Many different collections of antimicrobial resistance genes (ARGs) have been collected and used for various purposes. In order to develop a workflow for mass screening of public metagenomes, we recently gathered up and filtered in a number of these gene collections to produce PanRes.

For details, please see the methods section in the following publication:

"ARGfinder - a pipeline for large-scale analysis of antimicrobial resistance genes and their flanking regions in metagenomic datasets" (Unpublished, submitted)

Briefly, the PanRes gene collection is gathered from a combination of other resistance gene collections into one, so each unique sequence has an "pan_" identifier (PanRes_genes). A separate table (PanRes_data) provides an overview of all the genes, their origin database and which genes cluster together in high-identity clusters.

A number of previously published collections of ARGs were used in the creation of PanRes (See references):

ResFinder (downloaded 2023-01-20, (Bortolaia et al. 2020)),
ResFinderFG (version 2.0, (Gschwind et al. 2023))
CARD (version 3.2.5, (Alcock et al. 2023))
MegaRes (version 3.0.0, (Bonin et al. 2023))
AMRFinderPlus (version 3.11/2022-12-19.1, (Feldgarden et al. 2021))
ARGANNOT (V6_July2019, (Gupta et al. 2014))
The 'CsabaPal' collection (Provided by Csaba Pál and Zoltán Farkas in November 2022, Daruka et al. 2023))
BacMet (version 1.1, (Pal et al. 2014))

Additional file 1: Pseudomonas aeruginosa ARDaP-compatible antimicrobial resistance variant database.

Increasing antimicrobial resistance (AMR) poses a substantial threat to global health and economies, which has led many countries and regions to develop AMR National Action Plans (NAPs). However, inadequate logistical capacity, funding, and essential information can hinder NAP policymaking, especially in low-to-middle-income countries (LMICs). Therefore, major gaps exist between aspirations and actions, such as fully operationalized environmental AMR surveillance programs in NAPs. To help bridge knowledge gaps, we compiled a multilingual database that contains policy guidance from 146 countries composed of NAPs, internal reports, and other guidance documents on AMR mitigations, including environmental considerations. Leveraging this database, we developed an AMR-Policy GPT, a large language model with advanced retrieval-augmented generation capabilities. This prototype model can search and summarize evidence from plans, metadata, and technical knowledge and provide traceable references from global document databases. It was then manually validated to show its proficiency in accurately managing diverse inquiries while minimizing misinformation. Overall, the AMR-Policy GPT offers a prototype that, with the deepening of its database and further road testing, has the potential to support inclusive, evidence-informed AMR policy guidance to support governments, research, and public agencies. A conversational version of our prototype is available at www.liuhuibot.com/amrpolicy.

Pre-processed AMR data on S. aureus isolates from PATRIC database. The majority class size was decreased to reach the class ratio of 1:1 when the susceptible/resistant or resistant/susceptible class ratio exceeded 3.5.

Antibiotic resistance, projected to cause 10 million deaths annually by 2050, remains a critical health threat. Hospitals drive multidrug resistance via horizontal gene transfer. The 2023 Critical Assessment of Massive Data Analysis challenge presents resistance markers from 146 Johns Hopkins bacterial isolates, aiming to analyze resistomes without metadata or genomic sequences. Persistent homology, a topological data analysis method, effectively captures processes beyond vertical inheritance. A 1-hole is a topological feature representing a loop or gap in the data, where relationships form a circular structure rather than a linear one. Unlike vertical inheritance, which lacks topological 1-holes, horizontal gene transfer generates distinct patterns. Since antimicrobial resistance genes often spread via horizontal gene transfer, we simulated vertical and horizontal inheritance in bacterial resistomes. The number of 1-holes from simulations and a documented horizontal gene transfer case was analyzed using persistence barcodes. In a simulated population of binary sequences, we observed that, on average, two 1-holes form for every three genomes undergoing horizontal gene transfer. Using a presence-absence gene table, we confirmed the existence of 1-holes in a documented case of horizontal gene transfer between two bacterial genera in a Pittsburgh hospital. Notably, the Critical Assessment of Massive Data Analysis resistomes of Klebsiella and Escherichia exhibit 1-holes, while Enterobacter shows none. Lastly, we provide a mathematical example of a non-tree-like space that contains no 1-holes. Persistent homology provides a framework for uncovering complex clinical patterns, offering an alternative to understanding resistance mobility using presence-absence data, which could be obtained through methods beyond genomic sequencing.

Veterinary-Human AMR Data Exchange Market Outlook

According to our latest research, the global Veterinary-Human AMR Data Exchange market size reached USD 1.23 billion in 2024, with a robust compound annual growth rate (CAGR) of 12.8% projected through 2033. By the end of the forecast period, the market is expected to achieve a value of USD 3.66 billion. The primary growth factor for this market is the escalating threat of antimicrobial resistance (AMR), which has necessitated seamless data integration between veterinary and human health sectors to enhance surveillance, policy-making, and clinical interventions.

The growth of the Veterinary-Human AMR Data Exchange market is primarily driven by the increasing global prevalence of antimicrobial resistance, which poses a significant threat to both human and animal health. This has led governments, international health bodies, and private sector stakeholders to invest heavily in integrated data exchange platforms. These platforms enable real-time sharing and analysis of AMR data, helping to identify emerging resistance patterns, facilitate timely interventions, and support the development of targeted antimicrobial stewardship programs. Furthermore, the growing adoption of digital health technologies in both veterinary and human healthcare settings is accelerating the deployment of advanced data exchange solutions, making data-driven decision-making more accessible and effective.

Another crucial growth driver is the rising awareness and regulatory mandates surrounding One Health initiatives. The One Health approach emphasizes the interconnectedness of human, animal, and environmental health, thereby underscoring the need for integrated AMR surveillance systems. Regulatory agencies, such as the World Health Organization (WHO) and the World Organisation for Animal Health (OIE), are advocating for the harmonization of AMR data collection and exchange protocols. This has prompted the development of interoperable software, hardware, and service solutions that can seamlessly bridge the gap between veterinary and human health data repositories. The implementation of these systems is further supported by funding from international organizations and public-private partnerships, which are fostering innovation and market expansion.

Technological advancements in big data analytics, artificial intelligence, and cloud computing are also propelling the Veterinary-Human AMR Data Exchange market forward. These technologies enable the aggregation, normalization, and analysis of vast and diverse AMR datasets from multiple sources, including surveillance networks, diagnostic laboratories, and prescription databases. The integration of genomic data into AMR surveillance has further enhanced the precision and timeliness of resistance detection, allowing for more effective outbreak containment and resource allocation. As a result, stakeholders across the healthcare continuum are increasingly adopting sophisticated data exchange platforms to support research, clinical decision-making, and policy formulation.

From a regional perspective, North America currently dominates the Veterinary-Human AMR Data Exchange market, owing to its advanced healthcare infrastructure, strong regulatory framework, and substantial investments in digital health technologies. Europe follows closely, driven by robust public health initiatives and cross-border data sharing agreements. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, fueled by rising awareness of AMR, expanding veterinary and human healthcare sectors, and increasing government support for One Health initiatives. Latin America and the Middle East & Africa are also showing steady progress, albeit at a slower pace, due to improving healthcare infrastructure and growing international collaborations.

Component Analysis

The Component segment of the Veterinary-Human AMR Data Exchange market is categorized into soft

The data set includes relative abundances of antimicrobial resistance genes in urban karst groundwater systems in western Kentucky. This dataset is not publicly accessible because: This data was generated and is being stored with the first author, a non-EPA researcher. It can be accessed through the following means: This data can be accessed by emailing the first author, Rachel Kaiser at rakaiser42@tntech.edu. Format: Excel spreadsheets with columns for samples, sites, collection date and relative abundances.

This dataset is associated with the following publication: Rachel , K., J. Polk, T. Datta, S. Keely, N. Brinkman, R. Parekh, and G. Agga. Occurrence and Prevalence of Antimicrobial Resistance in Urban Karst Groundwater Systems Based on Targeted Resistome Analysis. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 874: 162571, (2023).

This dataset provides a curated and standardized collection of antimicrobial resistance (AMR) gene sequences and annotations for drug resistance classification tasks. It integrates entries from the Comprehensive Antibiotic Resistance Database (CARD) and MEGARes v3.0, and unifies resistance labels using the Antibiotic Resistance Ontology (ARO). To enhance reliability, classes with fewer than 15 samples were excluded. Each data sample includes a full-length nucleotide sequence, along with harmonized annotations for Drug Class, Resistance Mechanism, and Gene Family. The dataset covers 9 major antimicrobial Drug Classes: Beta-lactams Aminoglycosides Glycopeptides Tetracyclines Fluoroquinolones MLS (Macrolide-Lincosamide-Streptogramin) Sulfonamides Phenicol Multi-drug resistance Resistance mechanisms include categories such as antibiotic inactivation, target alteration, efflux, target protection, target replacement, and reduced permeability to antibiotics. Gene family annotations show a long-tailed distribution, with frequently observed families including beta-lactamases, aminoglycoside-modifying enzymes, major facilitator superfamily (MFS) efflux pumps, ribosomal protection proteins, and rRNA methyltransferases. This dataset has been used in studies involving sequence-based classification models such as Nucleotide Transformer. For model training, input sequences were truncated to 1000 base pairs, although the dataset itself provides full-length sequences. It is suitable for AMR prediction tasks and supports research in computational biology, genomic analysis, and biomedical natural language processing.

The main objective of the mapping tools is to provide a simple and useable platform for Member States and other reporting countries to map their country-specific standard terminology to that used by EFSA and to enable the production of an XML file for the submission of antimicrobial resistance data via the Data Collection Framework (DCF).

The tools can be used to report antimicrobial resistance data within the framework of Directive 2003/99/EC and Decision 2020/1729/EU.

The catalogues and the specific hierarchy of each data model (AMR and ESBL) are already inserted into each of the specific mapping tool. Specific Excel mapping tools corresponding to each of the two data models are available.

Dynamic or manual version of the tool can be chosen for each data models.

A multi-fasta file of unique alleles of ARGs in the AMRFinder database

The dataset collates the relative concentration of nearly 300 antimicrobial resistance (AMR) genes, and concentrations of polycyclic aromatic hydrocarbons (PAH) and potentially toxic elements (PTE; e.g., “metals”) found in soils across northeastern England during a sampling expedition in June 2016 by researchers at Newcastle University. Top soils (15 cm depths; “A” horizon) were obtained from 24 rural and urban locations around Newcastle upon Tyne, representing a spectrum of landscape conditions relative to anticipated PTE contamination. There are three files related to different types of data collected: antimicrobial resistance genes, metal concentrations and PAH concentrations. The high-throughput analysis of nearly 300 AMR genes include many resistance traits representing major antibiotic classes: aminoglycosides, beta lactams, FCA (fluoroquinolone, quinolone, chloramphenicol, florfenicol and amphenicol resistance genes), MLSB (macrolide, lincosamide, streptogramin B), tetracycline, vancomycin, sulphonamide, and efflux pumps. PAH data represent the US Environmental Protection Agency priority polycyclic aromatic hydrocarbons as one of the measures of pollution impact. The other measure of impact is based on levels of twelve PTE represented by “total” and “two bio-available” concentrations, based on three extraction methods. Elements included aluminium, arsenic, beryllium, cadmium, chromium, copper, iron, lead, mercury, nickel, phosphorus, and zinc.

Counts of susceptible and resistant genomes used in this study, data are displayed as (Susceptible|Resistant).

A single simulated metagenome from the high AMR data from hAMRoaster.

House flies (Musca domestica L.) are a global pest ubiquitous in urban and agricultural settings. Their dependence on microbe-rich substrates for development, as well as ability to acquire and transmit pathogenic and antimicrobial resistant (AMR) bacteria, make house flies a risk to human and animal health. Large livestock operations, like confined cattle, are environments which are conducive to both house flies and developing AMR due to large accumulations of animal feed and waste. However, little is known about what factors influence bacterial abundances and AMR prevalence carried by house flies in confined cattle operations. Adult house flies (n=6/fly sex/location) were collected on alternating weeks mid-August through early October of 2019 from a dairy and beef feedlot cattle operation in each of three Kansas counties (Riley, Marion, and Washington). We enumerated colony forming units (CFUs) of culturable aerobic bacteria and suspected coliforms (SC) from house fly homogenates on nonselective (tryptic soy agar, TSA) and selective (violet-red bile agar, VRBA) media to investigate factors, such as fly sex, farm type, location, and climate, which may be associated with bacterial abundances carried by house flies. Further, we screened unique morphotypes of SC isolates for tetracycline (Tet) resistance, then tested for additional resistance to florfenicol (Flo), enrofloxacin (Enr) ceftiofur (Cef), and ampicillin (Amp) to identify multi-drug resistant (MDR) isolates. AMR isolates were identified via 16S rRNA Sanger sequencing or, in select cases, matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS).Resources in this dataset:Resource File: Raw_fly_CFU_counts.xlsxResource description: Raw CFU counts for culturable aerobic bacteria (TSA) and suspected coliforms (VRBA) cultured from replica plating of ten-fold serially diluted house fly homogenates.Resource File: Raw_daily_avg_climate_Jul-Oct 2019.xlsxResource description: Raw climate data downloaded from Kansas Mesonet weather stations for sampling period.Resource File: Metadata_bacterial_isolates.xlsxResource description: Spreadsheet gives information linking individual isolate (Isolate ID #) data resources with which fly they originated from and other collection information (fly sex, farm type, collection date, county).Resource File: Raw_isolates_disk_susceptibility.xlsxResource description: Measured inhibition zones (in millimeters) of individual isolates which underwent disk susceptibility testing against 5 antibiotics (Tet, Flo, Enr, Cef, Amp).Resource file: Raw_isolates_MALDI-TOF_outputs.xlsxResource description: Spreadsheet of best and second-best matches of Bruker MALDI Biotyper Identification Results for individual isolates (Sample ID). Each isolate was measured in duplicate.All trimmed Sanger sequence reads of AMR isolates are publicly available at GenBank (PQ636534 - PQ636762).The code repository for 16S sequence analysis for this project can be found here:https://github.com/vlpickens04/Sanger_Phred_Code

The database contains the evidence presented by the Data Visualization tool (available on EFSA website) accompanying the publication of the 2015 European Union Summary Report on antimicrobial resistance (AMR). Data correspond to occurrence of resistance in Salmonella from animals and humans, occurrence of resistance in E. Coli in animals and prevalence of ESBL-producing E.coli in animals and meat, in EU Member States.

Format XLSX; Contact zoonoses_support@efsa.europa.eu (EFSA); FWD@ecdc.europa.eu (ECDC)

South Korea LI: ME: AMR: DB data was reported at 94.300 % in Feb 2025. This records a decrease from the previous number of 94.500 % for Jan 2025. South Korea LI: ME: AMR: DB data is updated monthly, averaging 96.250 % from Jan 2023 (Median) to Feb 2025, with 26 observations. The data reached an all-time high of 96.800 % in Apr 2023 and a record low of 94.300 % in Feb 2025. South Korea LI: ME: AMR: DB data remains active status in CEIC and is reported by Korea Life Insurance Association. The data is categorized under Global Database’s South Korea – Table KR.RG002: Life Insurance: IFRS 17: Management Efficiency.

Background Livestock systems have been proposed as a reservoir for antimicrobial-resistant (AMR) bacteria and AMR genetic determinants that may infect or colonise humans, yet quantitative evidence regarding their epidemiological role remains lacking. Here we used a combination of genomics, epidemiology and ecology to investigate patterns of AMR gene carriage in Escherichia coli, regarded as a sentinel organism. Methods We conducted a structured epidemiological survey of 99 households across Nairobi, Kenya, and whole genome sequenced E. coli isolates from 311 human, 606 livestock, and 399 wildlife faecal samples. We used statistical models to investigate the prevalence of AMR carriage and characterise AMR gene diversity and structure of AMR genes in different host populations across the city. We also investigated house-hold level risk factors for exchange of AMR genes between sympatric humans and livestock. Findings We detected 56 unique acquired genes along with 13 point mutations present in variable proportions in human and animal isolates, known to confer resistance to nine antibiotic classes. We find that AMR gene community composition is not associated with host species, but AMR genes were frequently co-located, potentially enabling the acquisition and dispersal of multi-drug resistance in a single step. We find that whilst keeping livestock had no influence on human AMR gene carriage, the potential for AMR transmission across human-livestock interfaces is greatest when manure is poorly disposed of and in larger households. Conclusions Findings of widespread carriage of AMR bacteria in human and animal populations, including in long-distance wildlife species, in community settings, highlight the value of evidence-based surveillance to address antimicrobial resistance on a global scale. Our genomic analysis provided in-depth understanding of AMR determinants at the interfaces of One-Health sectors that will inform AMR prevention and control.

The Antibiotic Resistance Microbiology Dataset (ARMD) is a structured and de-identified resource developed using electronic health records (EHR) from Stanford Healthcare. It provides a comprehensive overview of microbiological cultures including urine, respiratory, and blood cultures. This dataset includes 283,715 unique adult patients and features detailed information on culture results, identified organisms, antibiotic susceptibility, and associated demographic and clinical data. The dataset was meticulously constructed through a multi-step process designed to enhance data quality and relevance. By enabling the study of antimicrobial resistance patterns and supporting antimicrobial stewardship efforts, ARMD offers a valuable resource for researchers and clinicians seeking to improve the management of infectious diseases and combat the growing threat of antimicrobial resistance., Cohort Selection The ARMD was created using de-identified EHR data from Stanford Healthcare to address this need. This dataset provides microbiological cultures from adult patients (≥18 years old) and includes key clinical data points relevant to studying antimicrobial resistance. The cohort construction involved the following features and processes:

Culture Types: Microbiological cultures were included, specifically urine, respiratory, and blood cultures.

Temporal Adjustment: The timing of culture orders was adjusted for data privacy through jittering, ensuring patient confidentiality while retaining meaningful temporal relationships.

Culture Positivity: Each culture is flagged as either positive or negative, indicating whether an organism was identified. Cultures flagged as negative are represented by a null value in the susceptibility field.

Organism Identification and Susceptibility: For positive cultures, the identified organism and its antibiotic susceptibility are recorde..., , # Antibiotic Resistance Microbiology Dataset (ARMD)

Background

Antimicrobial resistance (AMR) represents a pressing global health challenge, exacerbated by the overuse and misuse of antibiotics. Efforts to mitigate AMR require high-quality datasets to analyze trends in microbial susceptibility, guide clinical decision-making, and inform stewardship programs. electronic health records (EHR) are a rich source of real-world data that can be leveraged to study antimicrobial use and resistance patterns. However, constructing meaningful datasets from EHR data requires rigorous curation and preprocessing to ensure accuracy, relevance, and usability. ARMD aims to facilitate research in antimicrobial stewardship, with applications in identifying resistance patterns, evaluating treatment practices, and informing public health interventions. By leveraging de-identified EHR data from Stanford Healthcare, this dataset provides a unique opportunity to generate insights that can help improve infec...