# | Rank | Similarity | Title + Abs. | Year | PMID |
|---|---|---|---|---|---|
| 0 | 1 | 2 | 3 | 4 | 5 |
| 9076 | 0 | 0.9909 | ResiDB: 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/. | 2021 | 33495705 |
| 5068 | 1 | 0.9895 | Ultrasensitive 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. | 2020 | 33086716 |
| 9075 | 2 | 0.9894 | CamPype: an open-source workflow for automated bacterial whole-genome sequencing analysis focused on Campylobacter. BACKGROUND: The rapid expansion of Whole-Genome Sequencing has revolutionized the fields of clinical and food microbiology. However, its implementation as a routine laboratory technique remains challenging due to the growth of data at a faster rate than can be effectively analyzed and critical gaps in bioinformatics knowledge. RESULTS: To address both issues, CamPype was developed as a new bioinformatics workflow for the genomics analysis of sequencing data of bacteria, especially Campylobacter, which is the main cause of gastroenteritis worldwide making a negative impact on the economy of the public health systems. CamPype allows fully customization of stages to run and tools to use, including read quality control filtering, read contamination, reads extension and assembly, bacterial typing, genome annotation, searching for antibiotic resistance genes, virulence genes and plasmids, pangenome construction and identification of nucleotide variants. All results are processed and resumed in an interactive HTML report for best data visualization and interpretation. CONCLUSIONS: The minimal user intervention of CamPype makes of this workflow an attractive resource for microbiology laboratories with no expertise in bioinformatics as a first line method for bacterial typing and epidemiological analyses, that would help to reduce the costs of disease outbreaks, or for comparative genomic analyses. CamPype is publicly available at https://github.com/JoseBarbero/CamPype . | 2023 | 37474912 |
| 9074 | 3 | 0.9889 | BacAnt: 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. | 2021 | 34367079 |
| 5116 | 4 | 0.9888 | Prediction of Antimicrobial Resistance in Gram-Negative Bacteria From Whole-Genome Sequencing Data. BACKGROUND: Early detection of antimicrobial resistance in pathogens and prescription of more effective antibiotics is a fast-emerging need in clinical practice. High-throughput sequencing technology, such as whole genome sequencing (WGS), may have the capacity to rapidly guide the clinical decision-making process. The prediction of antimicrobial resistance in Gram-negative bacteria, often the cause of serious systemic infections, is more challenging as genotype-to-phenotype (drug resistance) relationship is more complex than for most Gram-positive organisms. METHODS AND FINDINGS: We have used NCBI BioSample database to train and cross-validate eight XGBoost-based machine learning models to predict drug resistance to cefepime, cefotaxime, ceftriaxone, ciprofloxacin, gentamicin, levofloxacin, meropenem, and tobramycin tested in Acinetobacter baumannii, Escherichia coli, Enterobacter cloacae, Klebsiella aerogenes, and Klebsiella pneumoniae. The input is the WGS data in terms of the coverage of known antibiotic resistance genes by shotgun sequencing reads. Models demonstrate high performance and robustness to class imbalanced datasets. CONCLUSION: Whole genome sequencing enables the prediction of antimicrobial resistance in Gram-negative bacteria. We present a tool that provides an in silico antibiogram for eight drugs. Predictions are accompanied with a reliability index that may further facilitate the decision making process. The demo version of the tool with pre-processed samples is available at https://vancampn.shinyapps.io/wgs2amr/. The stand-alone version of the predictor is available at https://github.com/pieterjanvc/wgs2amr/. | 2020 | 32528441 |
| 9789 | 5 | 0.9888 | Nosocomial antibiotic resistance in multiple gram-negative species: experience at one hospital with squeezing the resistance balloon at multiple sites. Increased use of antibiotics has led to the isolation of multidrug-resistant bacteria, especially in intensive care units and long-term care facilities. Resistance in specific gram-negative bacteria, including Klebsiella pneumoniae, Acinetobacter baumannii, and Pseudomonas aeruginosa, is of great concern, because a growing number of reports have documented mechanisms whereby these microorganisms have become resistant to all available antibacterial agents used in therapy. Reduction in the selection of these multidrug-resistant bacteria can be accomplished by a combination of several strategies. These include having an understanding of the genetics of both innate and acquired characteristics of bacteria; knowing resistance potentials for specific antibacterials; monitoring resistance trends in bacteria designated as problematic organisms within a particular institution on a routine basis; modifying antibiotic formularies when and where needed; creating institutional education programs; and enforcing strict infection-control practices. Strategies appropriate for primary prevention of nosocomial resistance may differ from those required for control of existing epidemic or endemic resistance. | 2002 | 11797177 |
| 5103 | 6 | 0.9888 | Revolutionising bacteriology to improve treatment outcomes and antibiotic stewardship. LABORATORY INVESTIGATION OF BACTERIAL INFECTIONS GENERALLY TAKES TWO DAYS: one to grow the bacteria and another to identify them and to test their susceptibility. Meanwhile the patient is treated empirically, based on likely pathogens and local resistance rates. Many patients are over-treated to prevent under-treatment of a few, compromising antibiotic stewardship. Molecular diagnostics have potential to improve this situation by accelerating precise diagnoses and the early refinement of antibiotic therapy. They include: (i) the use of 'biomarkers' to swiftly distinguish patients with bacterial infection, and (ii) molecular bacteriology to identify pathogens and their resistance genes in clinical specimens, without culture. Biomarker interest centres on procalcitonin, which has given good results particularly for pneumonias, though broader biomarker arrays may prove superior in the future. PCRs already are widely used to diagnose a few infections (e.g. tuberculosis) whilst multiplexes are becoming available for bacteraemia, pneumonia and gastrointestinal infection. These detect likely pathogens, but are not comprehensive, particularly for resistance genes; there is also the challenge of linking pathogens and resistance genes when multiple organisms are present in a sample. Next-generation sequencing offers more comprehensive profiling, but obstacles include sensitivity when the bacterial load is low, as in bacteraemia, and the imperfect correlation of genotype and phenotype. In short, rapid molecular bacteriology presents great potential to improve patient treatments and antibiotic stewardship but faces many technical challenges; moreover it runs counter to the current nostrum of defining resistance in pharmacodynamic terms, rather than by the presence of a mechanism, and the policy of centralising bacteriology services. | 2013 | 24265945 |
| 5826 | 7 | 0.9886 | Rapid 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. | 2025 | 41025980 |
| 6691 | 8 | 0.9886 | The antimicrobial resistance monitoring and research (ARMoR) program: the US Department of Defense response to escalating antimicrobial resistance. Responding to escalating antimicrobial resistance (AMR), the US Department of Defense implemented an enterprise-wide collaboration, the Antimicrobial Resistance Monitoring and Research Program, to aid in infection prevention and control. It consists of a network of epidemiologists, bioinformaticists, microbiology researchers, policy makers, hospital-based infection preventionists, and healthcare providers who collaborate to collect relevant AMR data, conduct centralized molecular characterization, and use AMR characterization feedback to implement appropriate infection prevention and control measures and influence policy. A particularly concerning type of AMR, carbapenem-resistant Enterobacteriaceae, significantly declined after the program was launched. Similarly, there have been no further reports or outbreaks of another concerning type of AMR, colistin resistance in Acinetobacter, in the Department of Defense since the program was initiated. However, bacteria containing AMR-encoding genes are increasing. To update program stakeholders and other healthcare systems facing such challenges, we describe the processes and impact of the program. | 2014 | 24795331 |
| 8173 | 9 | 0.9886 | Advancing Antibacterial Strategies: CRISPR-Phage-Mediated Gene Therapy Targeting Bacterial Resistance Genes. One of the most significant issues facing the world today is antibiotic resistance, which makes it increasingly difficult to treat bacterial infections. Regular antibiotics no longer work against many bacteria, affecting millions of people. A novel approach known as CRISPR-phage therapy may be beneficial. This technique introduces a technology called CRISPR into resistant bacteria using bacteriophages. The genes that cause bacteria to become resistant to antibiotics can be identified and cut using CRISPR. This enables antibiotics to function by inhibiting the bacteria. This approach is highly precise, unlike conventional antibiotics, so it doesn't damage our bodies' beneficial bacteria. Preliminary studies and limited clinical trials suggest that this technique can effectively target drug-resistant bacteria such as Klebsiella pneumoniae and Methicillinresistant Staphylococcus aureus (MRSA). However, challenges in phage engineering, host delivery, and the growing threat of bacterial CRISPR resistance demand urgent and strategic innovation. Our perspective underscores that without proactive resolution of these hurdles, the current hopefulness could disappear. Looking ahead, integrating next-generation Cas effectors, non-DSB editors, and resistance monitoring frameworks could transform CRISPR-phage systems from an experimental novelty into a clinical mainstay. This shift will require not only scientific ingenuity but also coordinated advances in regulatory, translational, and manufacturing efforts. | 2025 | 40990280 |
| 8164 | 10 | 0.9885 | Antibiotic Resistance - A Cause for Reemergence of Infections. This article can rightly be called 'the rise of the microbial phoenix'; for, all the microbial infections whose doomsday was predicted with the discovery of antibiotics, have thumbed their noses at mankind and reemerged phoenix like. The hubris generated by Sir Alexander Fleming's discovery of Penicillin in 1928, exemplified best by the comment by William H Stewart, the US Surgeon General in 1967, "It is time to close the books on infectious diseases" has been replaced by the realisation that the threat of antibiotic resistance is, in the words of the Chief Medical Officer of England, Dame Sally Davies, "just as important and deadly as climate change and international terrorism". Antimicrobial resistance threatens to negate all the major medical advances of the last century because antimicrobial use is linked to many other fields like organ transplantation and cancer chemotherapy. Antibiotic resistance genes have been there since ancient times in response to naturally occurring antibiotics. Modern medicine has only driven further evolution of antimicrobial resistance by use, misuse, overuse and abuse of antibiotics. Resistant bacteria proliferate by natural selection when their drug sensitive comrades are removed by antibiotics. In this article the authors discuss the various causes of antimicrobial resistance and dwell in some detail on antibiotic resistance in gram-positive and gram-negative organisms. Finally they stress on the important role clinicians have in limiting the development and spread of antimicrobial resistance. | 2020 | 32026301 |
| 3765 | 11 | 0.9885 | An allelic exchange system for compliant genetic manipulation of the select agents Burkholderia pseudomallei and Burkholderia mallei. Burkholderia pseudomallei and B. mallei are Gram-negative bacterial pathogens that cause melioidosis in humans and glanders in horses, respectively. Both bacteria are classified as category B select agents in the United States. Due to strict select-agent regulations, the number of antibiotic selection markers approved for use in these bacteria is greatly limited. Approved markers for B. pseudomallei include genes encoding resistance to kanamycin (Km), gentamicin (Gm), and zeocin (Zeo); however, wild type B. pseudomallei is intrinsically resistant to these antibiotics. Selection markers for B. mallei are limited to Km and Zeo resistance genes. Additionally, there are few well developed counter-selection markers for use in Burkholderia. The use of SacB as a counter-selection method has been of limited success due to the presence of endogenous sacBC genes in the genomes of B. pseudomallei and B. mallei. These impediments have greatly hampered the genetic manipulation of B. pseudomallei and B. mallei and currently few reliable tools for the genetic manipulation of Burkholderia exist. To expand the repertoire of genetic tools for use in Burkholderia, we developed the suicide plasmid pMo130, which allows for the compliant genetic manipulation of the select agents B. pseudomallei and B. mallei using allelic exchange. pMo130 harbors an aphA gene which allows for Km selection, the reporter gene xylE, which allows for reliable visual detection of Burkholderia transformants, and carries a modified sacB gene that allows for the resolution of co-integrants. We employed this system to generate multiple unmarked and in-frame mutants in B. pseudomallei, and one mutant in B. mallei. This vector significantly expands the number of available tools that are select-agent compliant for the genetic manipulation of B. pseudomallei and B. mallei. | 2009 | 19010402 |
| 9073 | 12 | 0.9885 | EpitoCore: Mining Conserved Epitope Vaccine Candidates in the Core Proteome of Multiple Bacteria Strains. In reverse vaccinology approaches, complete proteomes of bacteria are submitted to multiple computational prediction steps in order to filter proteins that are possible vaccine candidates. Most available tools perform such analysis only in a single strain, or a very limited number of strains. But the vast amount of genomic data had shown that most bacteria contain pangenomes, i.e., their genomic information contains core, conserved genes, and random accessory genes specific to each strain. Therefore, in reverse vaccinology methods it is of the utmost importance to define core proteins and core epitopes. EpitoCore is a decision-tree pipeline developed to fulfill that need. It provides surfaceome prediction of proteins from related strains, defines core proteins within those, calculate their immunogenicity, predicts epitopes for a given set of MHC alleles defined by the user, and then reports if epitopes are located extracellularly and if they are conserved among the core homologs. Pipeline performance is illustrated by mining peptide vaccine candidates in Mycobacterium avium hominissuis strains. From a total proteome of ~4,800 proteins per strain, EpitoCore predicted 103 highly immunogenic core homologs located at cell surface, many of those related to virulence and drug resistance. Conserved epitopes identified among these homologs allows the users to define sets of peptides with potential to immunize the largest coverage of tested HLA alleles using peptide-based vaccines. Therefore, EpitoCore is able to provide automated identification of conserved epitopes in bacterial pangenomic datasets. | 2020 | 32431712 |
| 3771 | 13 | 0.9885 | RFPlasmid: predicting plasmid sequences from short-read assembly data using machine learning. Antimicrobial-resistance (AMR) genes in bacteria are often carried on plasmids and these plasmids can transfer AMR genes between bacteria. For molecular epidemiology purposes and risk assessment, it is important to know whether the genes are located on highly transferable plasmids or in the more stable chromosomes. However, draft whole-genome sequences are fragmented, making it difficult to discriminate plasmid and chromosomal contigs. Current methods that predict plasmid sequences from draft genome sequences rely on single features, like k-mer composition, circularity of the DNA molecule, copy number or sequence identity to plasmid replication genes, all of which have their drawbacks, especially when faced with large single-copy plasmids, which often carry resistance genes. With our newly developed prediction tool RFPlasmid, we use a combination of multiple features, including k-mer composition and databases with plasmid and chromosomal marker proteins, to predict whether the likely source of a contig is plasmid or chromosomal. The tool RFPlasmid supports models for 17 different bacterial taxa, including Campylobacter, Escherichia coli and Salmonella, and has a taxon agnostic model for metagenomic assemblies or unsupported organisms. RFPlasmid is available both as a standalone tool and via a web interface. | 2021 | 34846288 |
| 5097 | 14 | 0.9885 | Comparing Graph Sample and Aggregation (SAGE) and Graph Attention Networks in the Prediction of Drug-Gene Associations of Extended-Spectrum Beta-Lactamases in Periodontal Infections and Resistance. INTRODUCTION: Gram-negative bacteria exhibit more antibiotic resistance than gram-positive bacteria due to their cell wall structure and composition differences. Porins, or protein channels in these bacteria, can allow small, hydrophilic antibiotics to diffuse, affecting their susceptibility. Mutations in porin protein genes can also impair antibiotic entry. Predicting drug-gene associations of extended-spectrum beta-lactamases (ESBLs) is crucial as they confer resistance to beta-lactam antibiotics, challenging the treatment of infections. This aids clinicians in selecting suitable treatments, optimizing drug usage, enhancing patient outcomes, and controlling antibiotic resistance in healthcare settings. Graph-based neural networks can predict drug-gene associations in periodontal infections and resistance. The aim of the study was to predict drug-gene associations of ESBLs in periodontal infections and resistance. METHODS: The study focuses on analyzing drug-gene associations using probes and drugs. The data was converted into graph language, assigning nodes and edges for drugs and genes. Graph neural networks (GNNs) and similar algorithms were implemented using Google Colab and Python. Cytoscape and CytoHubba are open-source software platforms used for network analysis and visualization. GNNs were used for tasks like node classification, link prediction, and graph-level prediction. Three graph-based models were used: graph convolutional network (GCN), Graph SAGE, and graph attention network (GAT). Each model was trained for 200 epochs using the Adam optimizer with a learning rate of 0.01 and a weight decay of 5e-4. RESULTS: The drug-gene association network has 57 nodes, 79 edges, and a 2.730 characteristic path length. Its structure, organization, and connectivity are analyzed using the GCN and Graph SAGE, which show high accuracy, precision, recall, and an F1-score of 0.94. GAT's performance metrics are lower, with an accuracy of 0.68, precision of 0.47, recall of 0.68, and F1-score of 0.56, suggesting that it may not be as effective in capturing drug-gene relationships. CONCLUSION: Compared to ESBLs, both GCN and Graph SAGE demonstrate excellent performance with accuracy, precision, recall, and an F1-score of 0.94. These results indicate that GCN and Graph SAGE are highly effective in predicting drug-gene associations related to ESBLs. GCN and Graph SAGE outperform GAT in predicting drug-gene associations for ESBLs. Improvements include data augmentation, regularization, and cross-validation. Ethical considerations, fairness, and open-source implementations are crucial for future research in precision periodontal treatment. | 2024 | 39347119 |
| 9744 | 15 | 0.9885 | PARGT: 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. | 2020 | 32620856 |
| 8405 | 16 | 0.9884 | Mapping Major Disease Resistance Genes in Soybean by Genome-Wide Association Studies. Soybean is one of the most valuable agricultural crops in the world. Besides, this legume is constantly attacked by a wide range of pathogens (fungi, bacteria, viruses, and nematodes) compromising yield and increasing production costs. One of the major disease management strategies is the genetic resistance provided by single genes and quantitative trait loci (QTL). Identifying the genomic regions underlying the resistance against these pathogens on soybean is one of the first steps performed by molecular breeders. In the past, genetic mapping studies have been widely used to discover these genomic regions. However, over the last decade, advances in next-generation sequencing technologies and their subsequent cost decreasing led to the development of cost-effective approaches to high-throughput genotyping. Thus, genome-wide association studies applying thousands of SNPs in large sets composed of diverse soybean accessions have been successfully done. In this chapter, a comprehensive review of the majority of GWAS for soybean diseases published since this approach was developed is provided. Important diseases caused by Heterodera glycines, Phytophthora sojae, and Sclerotinia sclerotiorum have been the focus of the several GWAS. However, other bacterial and fungi diseases also have been targets of GWAS. As such, this GWAS summary can serve as a guide for future studies of these diseases. The protocol begins by describing several considerations about the pathogens and bringing different procedures of molecular characterization of them. Advice to choose the best isolate/race to maximize the discovery of multiple R genes or to directly map an effective R gene is provided. A summary of protocols, methods, and tools to phenotyping the soybean panel is given to several diseases. We also give details of options of DNA extraction protocols and genotyping methods, and we describe parameters of SNP quality to soybean data. Websites and their online tools to obtain genotypic and phenotypic data for thousands of soybean accessions are highlighted. Finally, we report several tricks and tips in Subheading 4, especially related to composing the soybean panel as well as generating and analyzing the phenotype data. We hope this protocol will be helpful to achieve GWAS success in identifying resistance genes on soybean. | 2022 | 35641772 |
| 5108 | 17 | 0.9884 | Surveillance of antimicrobial resistance: the WHONET program. Genes expressing resistance to each antimicrobial agent emerged after each agent became widely used. More than a hundred such genes now spread selectively through global networks of populations of bacteria in humans or animals treated with those agents. Information to monitor and manage this spread exists in the susceptibility test results of tens of thousands of laboratories around the world. The comparability of those results is uncertain, however, and their storage in paper files or in computer files with diverse codes and formats has made them inaccessible for analysis. The WHONET program puts each laboratory's data into a common code and file format at that laboratory, either by serving as or by translating from its own computer reporting system. It then enables each medical center to analyze its files in ways that help it monitor and manage resistance locally and to merge them with files of other centers for collaborative national or global surveillance of resistance. | 1997 | 8994799 |
| 9184 | 18 | 0.9884 | Unlocking 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. | 2023 | 37770168 |
| 9083 | 19 | 0.9884 | ARGNet: using deep neural networks for robust identification and classification of antibiotic resistance genes from sequences. BACKGROUND: Emergence of antibiotic resistance in bacteria is an important threat to global health. Antibiotic resistance genes (ARGs) are some of the key components to define bacterial resistance and their spread in different environments. Identification of ARGs, particularly from high-throughput sequencing data of the specimens, is the state-of-the-art method for comprehensively monitoring their spread and evolution. Current computational methods to identify ARGs mainly rely on alignment-based sequence similarities with known ARGs. Such approaches are limited by choice of reference databases and may potentially miss novel ARGs. The similarity thresholds are usually simple and could not accommodate variations across different gene families and regions. It is also difficult to scale up when sequence data are increasing. RESULTS: In this study, we developed ARGNet, a deep neural network that incorporates an unsupervised learning autoencoder model to identify ARGs and a multiclass classification convolutional neural network to classify ARGs that do not depend on sequence alignment. This approach enables a more efficient discovery of both known and novel ARGs. ARGNet accepts both amino acid and nucleotide sequences of variable lengths, from partial (30-50 aa; 100-150 nt) sequences to full-length protein or genes, allowing its application in both target sequencing and metagenomic sequencing. Our performance evaluation showed that ARGNet outperformed other deep learning models including DeepARG and HMD-ARG in most of the application scenarios especially quasi-negative test and the analysis of prediction consistency with phylogenetic tree. ARGNet has a reduced inference runtime by up to 57% relative to DeepARG. CONCLUSIONS: ARGNet is flexible, efficient, and accurate at predicting a broad range of ARGs from the sequencing data. ARGNet is freely available at https://github.com/id-bioinfo/ARGNet , with an online service provided at https://ARGNet.hku.hk . Video Abstract. | 2024 | 38725076 |