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506800.9885Ultrasensitive Label-Free Detection of Unamplified Multidrug-Resistance Bacteria Genes with a Bimodal Waveguide Interferometric Biosensor. Infections by multidrug-resistant bacteria are becoming a major healthcare emergence with millions of reported cases every year and an increasing incidence of deaths. An advanced diagnostic platform able to directly detect and identify antimicrobial resistance in a faster way than conventional techniques could help in the adoption of early and accurate therapeutic interventions, limiting the actual negative impact on patient outcomes. With this objective, we have developed a new biosensor methodology using an ultrasensitive nanophotonic bimodal waveguide interferometer (BiMW), which allows a rapid and direct detection, without amplification, of two prevalent and clinically relevant Gram-negative antimicrobial resistance encoding sequences: the extended-spectrum betalactamase-encoding gene blaCTX-M-15 and the carbapenemase-encoding gene blaNDM-5 We demonstrate the extreme sensitivity and specificity of our biosensor methodology for the detection of both gene sequences. Our results show that the BiMW biosensor can be employed as an ultrasensitive (attomolar level) and specific diagnostic tool for rapidly (less than 30 min) identifying drug resistance. The BiMW nanobiosensor holds great promise as a powerful tool for the control and management of healthcare-associated infections by multidrug-resistant bacteria.202033086716
974310.9879Simultaneous Detection of Antibiotic Resistance Genes on Paper-Based Chip Using [Ru(phen)(2)dppz](2+) Turn-on Fluorescence Probe. Antibiotic resistance, the ability of some bacteria to resist antibiotic drugs, has been a major global health burden due to the extensive use of antibiotic agents. Antibiotic resistance is encoded via particular genes; hence the specific detection of these genes is necessary for diagnosis and treatment of antibiotic resistant cases. Conventional methods for monitoring antibiotic resistance genes require the sample to be transported to a central laboratory for tedious and sophisticated tests, which is grueling and time-consuming. We developed a paper-based chip, integrated with loop-mediated isothermal amplification (LAMP) and the "light switch" molecule [Ru(phen)(2)dppz](2+), to conduct turn-on fluorescent detection of antibiotic resistance genes. In this assay, the amplification reagents can be embedded into test spots of the chip in advance, thus simplifying the detection procedure. [Ru(phen)(2)dppz](2+) was applied to intercalate into amplicons for product analysis, enabling this assay to be operated in a wash-free format. The paper-based detection device exhibited a limit of detection (LOD) as few as 100 copies for antibiotic resistance genes. Meanwhile, it could detect antibiotic resistance genes from various bacteria. Noticeably, the approach can be applied to other genes besides antibiotic resistance genes by simply changing the LAMP primers. Therefore, this paper-based chip has the potential for point-of-care (POC) applications to detect various gene samples, especially in resource-limited conditions.201829323478
507020.9877Sequence-specific DNA solid-phase extraction in an on-chip monolith: Towards detection of antibiotic resistance genes. Antibiotic resistance of bacteria is a growing problem and presents a challenge for prompt treatment in patients with sepsis. Currently used methods rely on culturing or amplification; however, these steps are either time consuming or suffer from interference issues. A microfluidic device was made from black polypropylene, with a monolithic column modified with a capture oligonucleotide for sequence selective solid-phase extraction of a complementary target from a lysate sample. Porous properties of the monolith allow flow and hybridization of a target complementary to the probe immobilized on the column surface. Good flow-through properties enable extraction of a 100μL sample and elution of target DNA in 12min total time. Using a fluorescently labeled target oligonucleotide related to Verona Integron-Mediated Metallo-β-lactamase it was possible to extract and detect a 1pM sample with 83% recovery. Temperature-mediated elution by heating above the duplex melting point provides a clean extract without any agents that interfere with base pairing, allowing various labeling methods or further downstream processing of the eluent. Further integration of this extraction module with a system for isolation and lysis of bacteria from blood, as well as combining with single-molecule detection should allow rapid determination of antibiotic resistance.201728734608
974430.9876PARGT: a software tool for predicting antimicrobial resistance in bacteria. With the ever-increasing availability of whole-genome sequences, machine-learning approaches can be used as an alternative to traditional alignment-based methods for identifying new antimicrobial-resistance genes. Such approaches are especially helpful when pathogens cannot be cultured in the lab. In previous work, we proposed a game-theory-based feature evaluation algorithm. When using the protein characteristics identified by this algorithm, called 'features' in machine learning, our model accurately identified antimicrobial resistance (AMR) genes in Gram-negative bacteria. Here we extend our study to Gram-positive bacteria showing that coupling game-theory-identified features with machine learning achieved classification accuracies between 87% and 90% for genes encoding resistance to the antibiotics bacitracin and vancomycin. Importantly, we present a standalone software tool that implements the game-theory algorithm and machine-learning model used in these studies.202032620856
582640.9873Rapid and accurate sepsis diagnostics via a novel probe-based multiplex real-time PCR system. Sepsis is a critical clinical emergency that requires prompt diagnosis and intervention. Its prevalence has increased due to the aging population and increased antibiotic resistance. Early identification and the use of innovative technologies are crucial for improving patient outcomes. Modern methodologies are needed to minimize the turnaround time for diagnosis and improve outcomes. Rapid diagnostic tests and multiplex PCR are effective but have limitations in identifying a range of pathogens and target genes. Our study evaluated two novel probe-based multiplex real-time PCR systems: the SEPSI ID and SEPSI DR panels. These systems can quickly identify bacterial and fungal pathogens, alongside antibiotic resistance genes. The assays cover 29 microorganisms (gram-negative bacteria, gram-positive bacteria, yeast, and mold species), alongside 23 resistance genes and four virulence factors. A streamlined workflow uses 2 µL of broth from positive blood cultures (BCs) without nucleic acid extraction and provides results in approximately 1 h. We present the results from an evaluation of 228 BCs and 22 isolates previously characterized by whole-genome sequencing. In comparison to the reference methods, the SEPSI ID panel demonstrated a sensitivity of 96.88%, a specificity of 100%, and a PPV of 100%, whereas the SEPSI DR panel showed a sensitivity of 97.8%, a PPV of 89.7%, and a specificity of 96.7%. Both panels also identified additional pathogens and resistance-related targets not detected by conventional methods. This assay shows promise for rapidly and accurately diagnosing sepsis. Future studies should validate its performance in various clinical settings to enhance sepsis management and improve patient outcomes.IMPORTANCEWe present a new diagnostic method that enables the quick and precise identification of pathogens and resistance genes from positive blood cultures, eliminating the need for nucleic acid extraction. This technique can also be used on fresh pathogen cultures. It has the potential to greatly improve treatment protocols, leading to better patient outcomes, more responsible antibiotic use, and more efficient management of healthcare resources.202541025980
918450.9872Unlocking the potential of phages: Innovative approaches to harnessing bacteriophages as diagnostic tools for human diseases. Phages, viruses that infect bacteria, have been explored as promising tools for the detection of human disease. By leveraging the specificity of phages for their bacterial hosts, phage-based diagnostic tools can rapidly and accurately detect bacterial infections in clinical samples. In recent years, advances in genetic engineering and biotechnology have enabled the development of more sophisticated phage-based diagnostic tools, including those that express reporter genes or enzymes, or target specific virulence factors or antibiotic resistance genes. However, despite these advancements, there are still challenges and limitations to the use of phage-based diagnostic tools, including concerns over phage safety and efficacy. This review aims to provide a comprehensive overview of the current state of phage-based diagnostic tools, including their advantages, limitations, and potential for future development. By addressing these issues, we hope to contribute to the ongoing efforts to develop safe and effective phage-based diagnostic tools for the detection of human disease.202337770168
974260.9871BOCS: DNA k-mer content and scoring for rapid genetic biomarker identification at low coverage. A single, inexpensive diagnostic test capable of rapidly identifying a wide range of genetic biomarkers would prove invaluable in precision medicine. Previous work has demonstrated the potential for high-throughput, label-free detection of A-G-C-T content in DNA k-mers, providing an alternative to single-letter sequencing while also having inherent lossy data compression and massively parallel data acquisition. Here, we apply a new bioinformatics algorithm - block optical content scoring (BOCS) - capable of using the high-throughput content k-mers for rapid, broad-spectrum identification of genetic biomarkers. BOCS uses content-based sequence alignment for probabilistic mapping of k-mer contents to gene sequences within a biomarker database, resulting in a probability ranking of genes on a content score. Simulations of the BOCS algorithm reveal high accuracy for identification of single antibiotic resistance genes, even in the presence of significant sequencing errors (100% accuracy for no sequencing errors, and >90% accuracy for sequencing errors at 20%), and at well below full coverage of the genes. Simulations for detecting multiple resistance genes within a methicillin-resistant Staphylococcus aureus (MRSA) strain showed 100% accuracy at an average gene coverage of merely 0.515, when the k-mer lengths were variable and with 4% sequencing error within the k-mer blocks. Extension of BOCS to cancer and other genetic diseases met or exceeded the results for resistance genes. Combined with a high-throughput content-based sequencing technique, the BOCS algorithm potentiates a test capable of rapid diagnosis and profiling of genetic biomarkers ranging from antibiotic resistance to cancer and other genetic diseases.201931173943
907470.9871BacAnt: A Combination Annotation Server for Bacterial DNA Sequences to Identify Antibiotic Resistance Genes, Integrons, and Transposable Elements. Whole genome sequencing (WGS) of bacteria has become a routine method in diagnostic laboratories. One of the clinically most useful advantages of WGS is the ability to predict antimicrobial resistance genes (ARGs) and mobile genetic elements (MGEs) in bacterial sequences. This allows comprehensive investigations of such genetic features but can also be used for epidemiological studies. A plethora of software programs have been developed for the detailed annotation of bacterial DNA sequences, such as rapid annotation using subsystem technology (RAST), Resfinder, ISfinder, INTEGRALL and The Transposon Registry. Unfortunately, to this day, a reliable annotation tool of the combination of ARGs and MGEs is not available, and the generation of genbank files requires much manual input. Here, we present a new webserver which allows the annotation of ARGs, integrons and transposable elements at the same time. The pipeline generates genbank files automatically, which are compatible with Easyfig for comparative genomic analysis. Our BacAnt code and standalone software package are available at https://github.com/xthua/bacant with an accompanying web application at http://bacant.net.202134367079
511880.9871Automated extraction of genes associated with antibiotic resistance from the biomedical literature. The detection of bacterial antibiotic resistance phenotypes is important when carrying out clinical decisions for patient treatment. Conventional phenotypic testing involves culturing bacteria which requires a significant amount of time and work. Whole-genome sequencing is emerging as a fast alternative to resistance prediction, by considering the presence/absence of certain genes. A lot of research has focused on determining which bacterial genes cause antibiotic resistance and efforts are being made to consolidate these facts in knowledge bases (KBs). KBs are usually manually curated by domain experts to be of the highest quality. However, this limits the pace at which new facts are added. Automated relation extraction of gene-antibiotic resistance relations from the biomedical literature is one solution that can simplify the curation process. This paper reports on the development of a text mining pipeline that takes in English biomedical abstracts and outputs genes that are predicted to cause resistance to antibiotics. To test the generalisability of this pipeline it was then applied to predict genes associated with Helicobacter pylori antibiotic resistance, that are not present in common antibiotic resistance KBs or publications studying H. pylori. These genes would be candidates for further lab-based antibiotic research and inclusion in these KBs. For relation extraction, state-of-the-art deep learning models were used. These models were trained on a newly developed silver corpus which was generated by distant supervision of abstracts using the facts obtained from KBs. The top performing model was superior to a co-occurrence model, achieving a recall of 95%, a precision of 60% and F1-score of 74% on a manually annotated holdout dataset. To our knowledge, this project was the first attempt at developing a complete text mining pipeline that incorporates deep learning models to extract gene-antibiotic resistance relations from the literature. Additional related data can be found at https://github.com/AndreBrincat/Gene-Antibiotic-Resistance-Relation-Extraction.202235134132
956190.9871The resistance tsunami, antimicrobial stewardship, and the golden age of microbiology. Modern medicine is built on antibiotics. Antibiotics are something that we take for granted. We have however spent over 60 years educating bacteria to become resistant, and the global resistance tsunami has caught everyone unawares. Since bacteria have changed, we also have to change, and to change most of the practices of how we use antibiotics. Because the development of new antibiotics is so expensive, a stewardship approach may help to preserve those that we have now while we work to develop new antibiotics and to develop other approaches to controlling and treating infections. We need to adopt the ethic of Good Stewardship Practice (GSP) as an active and dynamic process of continuous improvement in antibiotic use, a process with many steps of different sizes involving everyone involved in antibiotic use. All antibiotic users have an important role to play in GSP. Although the resistance situation is pessimistic, and the future of antibiotics looks uncertain, we are fortunately entering what may be seen as the golden age of microbiology. This encompasses an astonishing array of technologies for rapid pathogen and resistance gene detection, for clone identification by genome sequencing, for identification of novel bacterial genes and for identification of the Achilles' heels of different pathogens. Future antibiotics may have to be far more targeted to the individual pathogen and the site of infection. A global tax on antibiotics might reduce their use while funding the cost of developing new antibiotics and new approaches to control of infectious diseases.201424646601
9741100.9870ARGai 1.0: A GAN augmented in silico approach for identifying resistant genes and strains in E. coli using vision transformer. The emergence of infectious disease and antibiotic resistance in bacteria like Escherichia coli (E. coli) shows the necessity for novel computational techniques for identifying essential genes that contribute to resistance. The task of identifying resistant strains and multi-drug patterns in E. coli is a major challenge with whole genome sequencing (WGS) and next-generation sequencing (NGS) data. To address this issue, we suggest ARGai 1.0 a deep learning architecture enhanced with generative adversarial networks (GANs). We mitigate data scarcity difficulties by augmenting limited experimental datasets with synthetic data generated by GANs. Our in-silico method (augmentation with feature selection) improves the identification of resistance genes in E. coli by using feature extraction techniques to identify valuable features from actual and GAN-generated data. Employing comprehensive validation, we exhibit the effectiveness of our ARGai 1.0 in precisely identifying the informative and resistant genes. In addition, our ARGai 1.0 identifies the resistant strains with a classification accuracy of 98.96 % on Deep Convolutional Generative Adversarial Network (DCGAN) augmented data. Additionally, ARGai 1.0 achieves more than 98 % of sensitivity and specificity. We also benchmark our ARGai 1.0 with several state-of-the-art AI models for resistant strain classification. In the fight against antibiotic resistance, ARGai 1.0 offers a promising avenue for computational genomics. With implications for research and clinical practice, this work shows the potential of deep networks with GAN augmentation as a practical and successful method for gene identification in E. coli.202539813877
9738110.9870Detection and Quantification of Antimicrobial-Resistant Cells in Aquatic Environments by Bioorthogonal Noncanonical Amino Acid Tagging. Aquatic environments are important reservoirs of antibiotic wastes, antibiotic resistance genes, and bacteria, enabling the persistence and proliferation of antibiotic resistance in different bacterial populations. To prevent the spread of antibiotic resistance, effective approaches to detect antimicrobial susceptibility in aquatic environments are highly desired. In this work, we adopt a metabolism-based bioorthogonal noncanonical amino acid tagging (BONCAT) method to detect, visualize, and quantify active antimicrobial-resistant bacteria in water samples by exploiting the differences in bacterial metabolic responses to antibiotics. The BONCAT approach can be applied to rapidly detect bacterial resistance to multiple antibiotics within 20 min of incubation, regardless of whether they act on proteins or DNA. In addition, the combination of BONCAT with the microscope enables the intuitive characterization of antibiotic-resistant bacteria in mixed systems at single-cell resolution. Furthermore, BONCAT coupled with flow cytometry exhibits good performance in determining bacterial resistance ratios to chloramphenicol and population heterogeneity in hospital wastewater samples. In addition, this approach is also effective in detecting antibiotic-resistant bacteria in natural water samples. Therefore, such a simple, fast, and efficient BONCAT-based approach will be valuable in monitoring the increase and spread of antibiotic resistance within natural and engineered aquatic environments.202236251006
9076120.9870ResiDB: An automated database manager for sequence data. The amount of publicly available DNA sequence data is drastically increasing, making it a tedious task to create sequence databases necessary for the design of diagnostic assays. The selection of appropriate sequences is especially challenging in genes affected by frequent point mutations such as antibiotic resistance genes. To overcome this issue, we have designed the webtool resiDB, a rapid and user-friendly sequence database manager for bacteria, fungi, viruses, protozoa, invertebrates, plants, archaea, environmental and whole genome shotgun sequence data. It automatically identifies and curates sequence clusters to create custom sequence databases based on user-defined input sequences. A collection of helpful visualization tools gives the user the opportunity to easily access, evaluate, edit, and download the newly created database. Consequently, researchers do no longer have to manually manage sequence data retrieval, deal with hardware limitations, and run multiple independent software tools, each having its own requirements, input and output formats. Our tool was developed within the H2020 project FAPIC aiming to develop a single diagnostic assay targeting all sepsis-relevant pathogens and antibiotic resistance mechanisms. ResiDB is freely accessible to all users through https://residb.ait.ac.at/.202133495705
354130.9870New cloning vectors to facilitate quick allelic exchange in gram-negative bacteria. New cloning vectors have been developed with features to enhance quick allelic exchange in gram-negative bacteria. The conditionally replicative R6K and transfer origins facilitate conjugation and chromosomal integration into a variety of bacterial species, whereas the sacB gene provides counterselection for allelic exchange. The vectors have incorporated the lacZ alpha fragment with an enhanced multicloning site for easy blue/white screening and priming sites identified for efficient in vivo assembly or other DNA assembly cloning techniques. Different antibiotic resistance markers allow versatility for use with different bacteria, and transformation into an Escherichia coli strain capable of conjugation enables a quick method for allelic exchange. As a proof of principle, the authors used these vectors to inactivate genes in Vibrio cholerae and Salmonella typhimurium.202133492170
8185140.9870RNA-cleaving DNAzymes as a diagnostic and therapeutic agent against antimicrobial resistant bacteria. The development of nucleic-acid-based antimicrobials such as RNA-cleaving DNAzyme (RCD), a short catalytically active nucleic acid, is a promising alternative to the current antibiotics. The current rapid spread of antimicrobial resistance (AMR) in bacteria renders some antibiotics useless against bacterial infection, thus creating the need for alternative antimicrobials such as DNAzymes. This review summarizes recent advances in the use of RCD as a diagnostic and therapeutic agent against AMR. Firstly, the recent diagnostic application of RCD for the detection of bacterial cells and the associated resistant gene(s) is discussed. The next section summarises the therapeutic application of RCD in AMR bacterial infections which includes direct targeting of the resistant genes and indirect targeting of AMR-associated genes. Finally, this review extends the discussion to challenges of utilizing RCD in real-life applications, and the potential of combining both diagnostic and therapeutic applications of RCD into a single agent as a theranostic agent.202234505182
5073150.9869Parallel Detection of the Unamplified Carbapenem Resistance Genes bla(NDM-1) and bla(OXA-1) Using a Plasmonic Nano-Biosensor with a Field-Portable DNA Extraction Method. Antimicrobial resistance (AMR) is a rapidly growing global concern resulting from the overuse of antibiotics in agricultural and clinical settings. The challenge is exacerbated by the lack of rapid surveillance for resistant bacteria in clinical, environmental, and food supply settings. The increasing resistance to carbapenems, an important sub-class of beta-lactam antibiotics, is a major concern in the healthcare community. Carbapenem resistance (CR) has been found in the environment and food supply chain, where it has the potential to spread to pathogens, animals, and humans through direct or indirect contact. Rapid detection for preventative and control measures should be developed. This study utilized a gold nanoparticle-based plasmonic biosensor for the parallel detection of the CR genes bla(NDM-1) and bla(OXA-1). To explore the field portability, DNA was extracted using two methods: a commercial extraction kit and a boiling method. The results were compared between the two methods using a spectrophotometer and a cellphone application for RGB values to quantify the visual results. The results showed that the boiling method of extraction was more effective than extraction with a commercial kit for this analysis. The parallel detection of unamplified genes extracted via the boiling method is novel. When combined with other portable testing equipment, the approach has the potential to be an inexpensive, rapid, and simple on-site CR gene detection protocol.202539997014
4885160.9868A Review of the Diagnostic Approaches for the Detection of Antimicrobial Resistance, Including the Role of Biosensors in Detecting Carbapenem Resistance Genes. Antimicrobial resistance (AMR) is a rapidly growing global concern resulting from the overuse of antibiotics in both agricultural and clinical settings, the lack of surveillance for resistant bacteria, and the low quality of some available antimicrobial agents. Resistant pathogens are no longer susceptible to common clinical antimicrobials, which decreases the effectiveness of medicines used to treat infections caused by these organisms. Carbapenems are an important class of antibiotics due to their broad-spectrum effectiveness in treating infections caused by Gram-positive and Gram-negative organisms. Carbapenem-resistant bacteria have been found not only in healthcare but also in the environment and food supply chain, where they have the potential to spread to pathogens and infect humans and animals. Current methods of detecting AMR genes are expensive and time-consuming. While these methods, like polymerase chain reactions or whole-genome sequencing, are considered the "gold standard" for diagnostics, the development of inexpensive, rapid diagnostic assays is necessary for effective AMR detection and management. Biosensors have shown potential for success in diagnostic testing due to their ease of use, inexpensive materials, rapid results, and portable nature. Biosensors can be combined with nanomaterials to produce sensitive and easily interpretable results. This review presents an overview of carbapenem resistance, current and emerging detection methods of antimicrobial resistance, and the application of biosensors for rapid diagnostic testing for bacterial resistance.202540725449
4886170.9868Molecular diagnostics for genotypic detection of antibiotic resistance: current landscape and future directions. Antimicrobial resistance (AMR) among bacteria is an escalating public health emergency that has worsened during the COVID-19 pandemic. When making antibiotic treatment decisions, clinicians rely heavily on determination of antibiotic susceptibility or resistance by the microbiology laboratory, but conventional methods often take several days to identify AMR. There are now several commercially available molecular methods that detect antibiotic resistance genes within hours rather than days. While these methods have limitations, they offer promise for optimizing treatment and patient outcomes, and reducing further emergence of AMR. This review provides an overview of commercially available genotypic assays that detect individual resistance genes and/or resistance-associated mutations in a variety of specimen types and discusses how clinical outcomes studies may be used to demonstrate clinical utility of such diagnostics.202336816746
9554180.9868A multi-label learning framework for predicting antibiotic resistance genes via dual-view modeling. The increasing prevalence of antibiotic resistance has become a global health crisis. For the purpose of safety regulation, it is of high importance to identify antibiotic resistance genes (ARGs) in bacteria. Although culture-based methods can identify ARGs relatively more accurately, the identifying process is time-consuming and specialized knowledge is required. With the rapid development of whole genome sequencing technology, researchers attempt to identify ARGs by computing sequence similarity from public databases. However, these computational methods might fail to detect ARGs due to the low sequence identity to known ARGs. Moreover, existing methods cannot effectively address the issue of multidrug resistance prediction for ARGs, which is a great challenge to clinical treatments. To address the challenges, we propose an end-to-end multi-label learning framework for predicting ARGs. More specifically, the task of ARGs prediction is modeled as a problem of multi-label learning, and a deep neural network-based end-to-end framework is proposed, in which a specific loss function is introduced to employ the advantage of multi-label learning for ARGs prediction. In addition, a dual-view modeling mechanism is employed to make full use of the semantic associations among two views of ARGs, i.e. sequence-based information and structure-based information. Extensive experiments are conducted on publicly available data, and experimental results demonstrate the effectiveness of the proposed framework on the task of ARGs prediction.202235272349
5115190.9868Search Engine for Antimicrobial Resistance: A Cloud Compatible Pipeline and Web Interface for Rapidly Detecting Antimicrobial Resistance Genes Directly from Sequence Data. BACKGROUND: Antimicrobial resistance remains a growing and significant concern in human and veterinary medicine. Current laboratory methods for the detection and surveillance of antimicrobial resistant bacteria are limited in their effectiveness and scope. With the rapidly developing field of whole genome sequencing beginning to be utilised in clinical practice, the ability to interrogate sequencing data quickly and easily for the presence of antimicrobial resistance genes will become increasingly important and useful for informing clinical decisions. Additionally, use of such tools will provide insight into the dynamics of antimicrobial resistance genes in metagenomic samples such as those used in environmental monitoring. RESULTS: Here we present the Search Engine for Antimicrobial Resistance (SEAR), a pipeline and web interface for detection of horizontally acquired antimicrobial resistance genes in raw sequencing data. The pipeline provides gene information, abundance estimation and the reconstructed sequence of antimicrobial resistance genes; it also provides web links to additional information on each gene. The pipeline utilises clustering and read mapping to annotate full-length genes relative to a user-defined database. It also uses local alignment of annotated genes to a range of online databases to provide additional information. We demonstrate SEAR's application in the detection and abundance estimation of antimicrobial resistance genes in two novel environmental metagenomes, 32 human faecal microbiome datasets and 126 clinical isolates of Shigella sonnei. CONCLUSIONS: We have developed a pipeline that contributes to the improved capacity for antimicrobial resistance detection afforded by next generation sequencing technologies, allowing for rapid detection of antimicrobial resistance genes directly from sequencing data. SEAR uses raw sequencing data via an intuitive interface so can be run rapidly without requiring advanced bioinformatic skills or resources. Finally, we show that SEAR is effective in detecting antimicrobial resistance genes in metagenomic and isolate sequencing data from both environmental metagenomes and sequencing data from clinical isolates.201526197475