# | Rank | Similarity | Title + Abs. | Year | PMID |
|---|---|---|---|---|---|
| 0 | 1 | 2 | 3 | 4 | 5 |
| 9077 | 0 | 1.0000 | The PLSDB 2025 update: enhanced annotations and improved functionality for comprehensive plasmid research. Plasmids are extrachromosomal DNA molecules in bacteria and archaea, playing critical roles in horizontal gene transfer, antibiotic resistance, and pathogenicity. Since its first release in 2018, our database on plasmids, PLSDB, has significantly grown and enhanced its content and scope. From 34 513 records contained in the 2021 version, PLSDB now hosts 72 360 entries. Designed to provide life scientists with convenient access to extensive plasmid data and to support computer scientists by offering curated datasets for artificial intelligence (AI) development, this latest update brings more comprehensive and accurate information for plasmid research, with interactive visualization options. We enriched PLSDB by refining the identification and classification of plasmid host ecosystems and host diseases. Additionally, we incorporated annotations for new functional structures, including protein-coding genes and biosynthetic gene clusters. Further, we enhanced existing annotations, such as antimicrobial resistance genes and mobility typing. To accommodate these improvements and to host the increase plasmid sets, the webserver architecture and underlying data structures of PLSDB have been re-reconstructed, resulting in decreased response times and enhanced visualization of features while ensuring that users have access to a more efficient and user-friendly interface. The latest release of PLSDB is freely accessible at https://www.ccb.uni-saarland.de/plsdb2025. | 2025 | 39565221 |
| 9560 | 1 | 0.9995 | The History of Colistin Resistance Mechanisms in Bacteria: Progress and Challenges. Since 2015, the discovery of colistin resistance genes has been limited to the characterization of new mobile colistin resistance (mcr) gene variants. However, given the complexity of the mechanisms involved, there are many colistin-resistant bacterial strains whose mechanism remains unknown and whose exploitation requires complementary technologies. In this review, through the history of colistin, we underline the methods used over the last decades, both old and recent, to facilitate the discovery of the main colistin resistance mechanisms and how new technological approaches may help to improve the rapid and efficient exploration of new target genes. To accomplish this, a systematic search was carried out via PubMed and Google Scholar on published data concerning polymyxin resistance from 1950 to 2020 using terms most related to colistin. This review first explores the history of the discovery of the mechanisms of action and resistance to colistin, based on the technologies deployed. Then we focus on the most advanced technologies used, such as MALDI-TOF-MS, high throughput sequencing or the genetic toolbox. Finally, we outline promising new approaches, such as omics tools and CRISPR-Cas9, as well as the challenges they face. Much has been achieved since the discovery of polymyxins, through several innovative technologies. Nevertheless, colistin resistance mechanisms remains very complex. | 2021 | 33672663 |
| 9554 | 2 | 0.9995 | A 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. | 2022 | 35272349 |
| 9567 | 3 | 0.9994 | How to discover new antibiotic resistance genes? Antibiotic resistance (AR) is a worldwide concern and the description of AR have been discovered mainly because of their implications in human medicine. Since the recent burden of whole-genome sequencing of microorganisms, the number of new AR genes (ARGs) have dramatically increased over the last decade. Areas covered: In this review, we will describe the different methods that could be used to characterize new ARGs using classic or innovative methods. First, we will focus on the biochemical methods, then we will develop on molecular methods, next-generation sequencing and bioinformatics approaches. The use of various methods, including cloning, mutagenesis, transposon mutagenesis, functional genomics, whole genome sequencing, metagenomic and functional metagenomics will be reviewed here, outlining the advantages and drawbacks of each method. Bioinformatics softwares used for resistome analysis and protein modeling will be also described. Expert opinion: Biological experiments and bioinformatics analysis are complementary. Nowadays, the ARGs described only account for the tip of the iceberg of all existing resistance mechanisms. The multiplication of the ecosystems studied allows us to find a large reservoir of AR mechanisms. Furthermore, the adaptation ability of bacteria facing new antibiotics promises a constant discovery of new AR mechanisms. | 2019 | 30895843 |
| 9744 | 4 | 0.9994 | 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 |
| 9565 | 5 | 0.9994 | Finding drug targets in microbial genomes. In this era of genomic science, knowledge about biological function is integrated increasingly with DNA sequence data. One area that has been significantly impacted by this accumulation of information is the discovery of drugs to treat microbial infections. Genome sequencing and bioinformatics is driving the discovery and development of novel classes of broad-spectrum antimicrobial compounds, and could enable medical science to keep pace with the increasing resistance of bacteria, fungi and parasites to current antimicrobials. This review discusses the use of genomic information in the rapid identification of target genes for antimicrobial drug discovery. | 2001 | 11522517 |
| 3782 | 6 | 0.9994 | CRISPR spacers acquired from plasmids primarily target backbone genes, making them valuable for predicting potential hosts and host range. In recent years, there has been a surge in metagenomic studies focused on identifying plasmids in environmental samples. Although these studies have unearthed numerous novel plasmids, enriching our understanding of their environmental roles, a significant gap remains: the scarcity of information regarding the bacterial hosts of these newly discovered plasmids. Furthermore, even when plasmids are identified within bacterial isolates, the reported host is typically limited to the original isolate, with no insights into alternative hosts or the plasmid's potential host range. Given that plasmids depend on hosts for their existence, investigating plasmids without the knowledge of potential hosts offers only a partial perspective. This study introduces a method for identifying potential hosts and host ranges for plasmids through alignment with CRISPR spacers. To validate the method, we compared the PLSDB plasmids database with the CRISPR spacers database, yielding host predictions for 46% of the plasmids. When compared with reported hosts, our predictions achieved 84% concordance at the family level and 99% concordance at the phylum level. Moreover, the method frequently identified multiple potential hosts for a plasmid, thereby enabling predictions of alternative hosts and the host range. Notably, we found that CRISPR spacers predominantly target plasmid backbone genes while sparing functional genes, such as those linked to antibiotic resistance, aligning with our hypothesis that CRISPR spacers are acquired from plasmid-specific regions rather than insertion elements from diverse sources. Finally, we illustrate the network of connections among different bacterial taxa through plasmids, revealing potential pathways for horizontal gene transfer.IMPORTANCEPlasmids are notorious for their role in distributing antibiotic resistance genes, but they may also carry and distribute other environmentally important genes. Since plasmids are not free-living entities and rely on host bacteria for survival and propagation, predicting their hosts is essential. This study presents a method for predicting potential hosts for plasmids and offers insights into the potential paths for spreading functional genes between different bacteria. Understanding plasmid-host relationships is crucial for comprehending the ecological and clinical impact of plasmids and implications for various biological processes. | 2024 | 39508585 |
| 9566 | 7 | 0.9994 | Computational resources in the management of antibiotic resistance: Speeding up drug discovery. This article reviews more than 50 computational resources developed in past two decades for forecasting of antibiotic resistance (AR)-associated mutations, genes and genomes. More than 30 databases have been developed for AR-associated information, but only a fraction of them are updated regularly. A large number of methods have been developed to find AR genes, mutations and genomes, with most of them based on similarity-search tools such as BLAST and HMMER. In addition, methods have been developed to predict the inhibition potential of antibiotics against a bacterial strain from the whole-genome data of bacteria. This review also discuss computational resources that can be used to manage the treatment of AR-associated diseases. | 2021 | 33892146 |
| 5113 | 8 | 0.9994 | Identification of bacterial antibiotic resistance genes in next-generation sequencing data (review of literature). The spread of antibiotic-resistant human bacterial pathogens is a serious threat to modern medicine. Antibiotic susceptibility testing is essential for treatment regimens optimization and preventing dissemination of antibiotic resistance. Therefore, development of antibiotic susceptibility testing methods is a priority challenge of laboratory medicine. The aim of this review is to analyze the capabilities of the bioinformatics tools for bacterial whole genome sequence data processing. The PubMed database, Russian scientific electronic library eLIBRARY, information networks of World health organization and European Society of Clinical Microbiology and Infectious Diseases (ESCMID) were used during the analysis. In this review, the platforms for whole genome sequencing, which are suitable for detection of bacterial genetic resistance determinants, are described. The classic step of genetic resistance determinants searching is an alignment between the query nucleotide/protein sequence and the subject (database) nucleotide/protein sequence, which is performed using the nucleotide and protein sequence databases. The most commonly used databases are Resfinder, CARD, Bacterial Antimicrobial Resistance Reference Gene Database. The results of the resistance determinants searching in genome assemblies is more correct in comparison to results of the searching in contigs. The new resistance genes searching bioinformatics tools, such as neural networks and machine learning, are discussed in the review. After critical appraisal of the current antibiotic resistance databases we designed a protocol for predicting antibiotic resistance using whole genome sequence data. The designed protocol can be used as a basis of the algorithm for qualitative and quantitative antimicrobial susceptibility testing based on whole genome sequence data. | 2021 | 34882354 |
| 9671 | 9 | 0.9994 | Genome-scale genetic manipulation methods for exploring bacterial molecular biology. Bacteria are diverse and abundant, playing key roles in human health and disease, the environment, and biotechnology. Despite progress in genome sequencing and bioengineering, much remains unknown about the functional organization of prokaryotes. For instance, roughly a third of the protein-coding genes of the best-studied model bacterium, Escherichia coli, currently lack experimental annotations. Systems-level experimental approaches for investigating the functional associations of bacterial genes and genetic structures are essential for defining the fundamental molecular biology of microbes, preventing the spread of antibacterial resistance in the clinic, and driving the development of future biotechnological applications. This review highlights recently introduced large-scale genetic manipulation and screening procedures for the systematic exploration of bacterial gene functions, molecular relationships, and the global organization of bacteria at the gene, pathway, and genome levels. | 2012 | 22517266 |
| 9553 | 10 | 0.9994 | A machine learning framework to predict antibiotic resistance traits and yet unknown genes underlying resistance to specific antibiotics in bacterial strains. Recently, the frequency of observing bacterial strains without known genetic components underlying phenotypic resistance to antibiotics has increased. There are several strains of bacteria lacking known resistance genes; however, they demonstrate resistance phenotype to drugs of that family. Although such strains are fewer compared to the overall population, they pose grave emerging threats to an already heavily challenged area of antimicrobial resistance (AMR), where death tolls have reached ~700 000 per year and a grim projection of ~10 million deaths per year by 2050 looms. Considering the fact that development of novel antibiotics is not keeping pace with the emergence and dissemination of resistance, there is a pressing need to decipher yet unknown genetic mechanisms of resistance, which will enable developing strategies for the best use of available interventions and show the way for the development of new drugs. In this study, we present a machine learning framework to predict novel AMR factors that are potentially responsible for resistance to specific antimicrobial drugs. The machine learning framework utilizes whole-genome sequencing AMR genetic data and antimicrobial susceptibility testing phenotypic data to predict resistance phenotypes and rank AMR genes by their importance in discriminating the resistance from the susceptible phenotypes. In summary, we present here a bioinformatics framework for training machine learning models, evaluating their performances, selecting the best performing model(s) and finally predicting the most important AMR loci for the resistance involved. | 2021 | 34015806 |
| 9880 | 11 | 0.9994 | Plasmid Classification in an Era of Whole-Genome Sequencing: Application in Studies of Antibiotic Resistance Epidemiology. Plasmids are extra-chromosomal genetic elements ubiquitous in bacteria, and commonly transmissible between host cells. Their genomes include variable repertoires of 'accessory genes,' such as antibiotic resistance genes, as well as 'backbone' loci which are largely conserved within plasmid families, and often involved in key plasmid-specific functions (e.g., replication, stable inheritance, mobility). Classifying plasmids into different types according to their phylogenetic relatedness provides insight into the epidemiology of plasmid-mediated antibiotic resistance. Current typing schemes exploit backbone loci associated with replication (replicon typing), or plasmid mobility (MOB typing). Conventional PCR-based methods for plasmid typing remain widely used. With the emergence of whole-genome sequencing (WGS), large datasets can be analyzed using in silico plasmid typing methods. However, short reads from popular high-throughput sequencers can be challenging to assemble, so complete plasmid sequences may not be accurately reconstructed. Therefore, localizing resistance genes to specific plasmids may be difficult, limiting epidemiological insight. Long-read sequencing will become increasingly popular as costs decline, especially when resolving accurate plasmid structures is the primary goal. This review discusses the application of plasmid classification in WGS-based studies of antibiotic resistance epidemiology; novel in silico plasmid analysis tools are highlighted. Due to the diverse and plastic nature of plasmid genomes, current typing schemes do not classify all plasmids, and identifying conserved, phylogenetically concordant genes for subtyping and phylogenetics is challenging. Analyzing plasmids as nodes in a network that represents gene-sharing relationships between plasmids provides a complementary way to assess plasmid diversity, and allows inferences about horizontal gene transfer to be made. | 2017 | 28232822 |
| 9674 | 12 | 0.9994 | Global epistasis in plasmid-mediated antimicrobial resistance. Antimicrobial resistance (AMR) in bacteria is a major public health threat and conjugative plasmids play a key role in the dissemination of AMR genes among bacterial pathogens. Interestingly, the association between AMR plasmids and pathogens is not random and certain associations spread successfully at a global scale. The burst of genome sequencing has increased the resolution of epidemiological programs, broadening our understanding of plasmid distribution in bacterial populations. Despite the immense value of these studies, our ability to predict future plasmid-bacteria associations remains limited. Numerous empirical studies have recently reported systematic patterns in genetic interactions that enable predictability, in a phenomenon known as global epistasis. In this perspective, we argue that global epistasis patterns hold the potential to predict interactions between plasmids and bacterial genomes, thereby facilitating the prediction of future successful associations. To assess the validity of this idea, we use previously published data to identify global epistasis patterns in clinically relevant plasmid-bacteria associations. Furthermore, using simple mechanistic models of antibiotic resistance, we illustrate how global epistasis patterns may allow us to generate new hypotheses on the mechanisms associated with successful plasmid-bacteria associations. Collectively, we aim at illustrating the relevance of exploring global epistasis in the context of plasmid biology. | 2024 | 38409539 |
| 5099 | 13 | 0.9994 | A machine learning-based strategy to elucidate the identification of antibiotic resistance in bacteria. Microorganisms, crucial for environmental equilibrium, could be destructive, resulting in detrimental pathophysiology to the human host. Moreover, with the emergence of antibiotic resistance (ABR), the microbial communities pose the century's largest public health challenges in terms of effective treatment strategies. Furthermore, given the large diversity and number of known bacterial strains, describing treatment choices for infected patients using experimental methodologies is time-consuming. An alternative technique, gaining popularity as sequencing prices fall and technology advances, is to use bacterial genotype rather than phenotype to determine ABR. Complementing machine learning into clinical practice provides a data-driven platform for categorization and interpretation of bacterial datasets. In the present study, k-mers were generated from nucleotide sequences of pathogenic bacteria resistant to antibiotics. Subsequently, they were clustered into groups of bacteria sharing similar genomic features using the Affinity propagation algorithm with a Silhouette coefficient of 0.82. Thereafter, a prediction model based on Random Forest algorithm was developed to explore the prediction capability of the k-mers. It yielded an overall specificity of 0.99 and a sensitivity of 0.98. Additionally, the genes and ABR drivers related to the k-mers were identified to explore their biological relevance. Furthermore, a multilayer perceptron model with a hamming loss of 0.05 was built to classify the bacterial strains into resistant and non-resistant strains against various antibiotics. Segregating pathogenic bacteria based on genomic similarities could be a valuable approach for assessing the severity of diseases caused by new bacterial strains. Utilization of this strategy could aid in enhancing our understanding of ABR patterns, paving the way for more informed and effective treatment options. | 2024 | 39816256 |
| 9557 | 14 | 0.9994 | Antimicrobial Resistance Profile by Metagenomic and Metatranscriptomic Approach in Clinical Practice: Opportunity and Challenge. The burden of bacterial resistance to antibiotics affects several key sectors in the world, including healthcare, the government, and the economic sector. Resistant bacterial infection is associated with prolonged hospital stays, direct costs, and costs due to loss of productivity, which will cause policy makers to adjust their policies. Current widely performed procedures for the identification of antibiotic-resistant bacteria rely on culture-based methodology. However, some resistance determinants, such as free-floating DNA of resistance genes, are outside the bacterial genome, which could be potentially transferred under antibiotic exposure. Metagenomic and metatranscriptomic approaches to profiling antibiotic resistance offer several advantages to overcome the limitations of the culture-based approach. These methodologies enhance the probability of detecting resistance determinant genes inside and outside the bacterial genome and novel resistance genes yet pose inherent challenges in availability, validity, expert usability, and cost. Despite these challenges, such molecular-based and bioinformatics technologies offer an exquisite advantage in improving clinicians' diagnoses and the management of resistant infectious diseases in humans. This review provides a comprehensive overview of next-generation sequencing technologies, metagenomics, and metatranscriptomics in assessing antimicrobial resistance profiles. | 2022 | 35625299 |
| 5101 | 15 | 0.9993 | Identification of Key Features Pivotal to the Characteristics and Functions of Gut Bacteria Taxa through Machine Learning Methods. BACKGROUND: Gut bacteria critically influence digestion, facilitate the breakdown of complex food substances, aid in essential nutrient synthesis, and contribute to immune system balance. However, current knowledge regarding intestinal bacteria remains insufficient. OBJECTIVE: This study aims to discover essential differences for different intestinal bacteria. METHODS: This study was conducted by investigating a total of 1478 gut bacterial samples comprising 235 Actinobacteria, 447 Bacteroidetes, and 796 Firmicutes, by utilizing sophisticated machine learning algorithms. By building on the dataset provided by Chen et al., we engaged sophisticated machine learning techniques to further investigate and analyze the gut bacterial samples. Each sample in the dataset was described by 993 unique features associated with gut bacteria, including 342 features annotated by the Antibiotic Resistance Genes Database, Comprehensive Antibiotic Research Database, Kyoto Encyclopedia of Genes and Genomes, and Virulence Factors of Pathogenic Bacteria. We employed incremental feature selection methods within a computational framework to identify the optimal features for classification. RESULTS: Eleven feature ranking algorithms selected several key features as pivotal to the characteristics and functions of gut bacteria. These features appear to facilitate the identification of specific gut bacterial species. Additionally, we established quantitative rules for identifying Actinobacteria, Bacteroidetes, and Firmicutes. CONCLUSION: This research underscores the significant potential of machine learning in studying gut microbes and enhances our understanding of the multifaceted roles of gut bacteria. | 2025 | 40671232 |
| 9552 | 16 | 0.9993 | Addressing antibiotic resistance: computational answers to a biological problem? The increasing prevalence of infections caused by antibiotic-resistant bacteria is a global healthcare crisis. Understanding the spread of resistance is predicated on the surveillance of antibiotic resistance genes within an environment. Bioinformatics and artificial intelligence (AI) methods applied to metagenomic sequencing data offer the capacity to detect known and infer yet-unknown resistance mechanisms, and predict future outbreaks of antibiotic-resistant infections. Machine learning methods, in particular, could revive the waning antibiotic discovery pipeline by helping to predict the molecular structure and function of antibiotic resistance compounds, and optimising their interactions with target proteins. Consequently, AI has the capacity to play a central role in guiding antibiotic stewardship and future clinical decision-making around antibiotic resistance. | 2023 | 37031568 |
| 9679 | 17 | 0.9993 | Integrons as the potential targets for combating multidrug resistance in Enterobacteriaceae using CRISPR- Cas9 technique. The emergence of multi-drug resistance (MDR) to pan-drug resistance (PDR) in Enterobacteriaceae has made treatment extremely challenging. Genetic mutations and horizontal gene transfer (HGT) through mobile genetic elements (MGEs) were frequently associated mechanisms of drug resistance in pathogens. However, transposons, plasmids, and integrons transfer MDR genes in bacterium via HGT much faster. Integrons are dsDNA segment that plays a crucial role in the adaptation and evolution of bacteria. They contain multiple gene cassettes that code for antibiotic resistance determinants that are expressed by a single promoter (Pc). Integrons are the cause of drug resistance in Enterobacteriaceae. Although alternatives to antibiotics such as bacteriophages, phage proteins, antimicrobial peptides, and natural compounds have been widely used to treat MDR infections, there have been limited efforts to reverse the antibiotic resistance ability of bacteria. Thus, silencing the genes harboured on MGEs achieved by Gene Editing Techniques (GETs) might prevent the spread of MDR. One such GETs, which has a simple design, good repeatability, low cost, and high efficiency, is CRISPR- Cas9 system. Thus, this review is a first of the kind that focuses on utilizing the structure of an integron to make it an ideal target for GETs like CRISPR- Cas9 systems. | 2023 | 37410611 |
| 9559 | 18 | 0.9993 | CRISPR-Cas Systems in the Fight Against Antimicrobial Resistance: Current Status, Potentials, and Future Directions. BACKGROUND: Antimicrobial resistance (AMR) is a critical global health concern that threatens the efficacy of existing antibiotics and poses significant challenges to public health and the economy worldwide. This review explores the potential of CRISPR-Cas systems as a novel approach to combating AMR and examines current applications, limitations, and prospects. METHODS: A comprehensive literature search was conducted across multiple databases, including PubMed, Google Scholar, Scopus, and Web of Science, covering publications published from 2014 to August 2024. This review focuses on CRISPR-Cas technologies and their applications in AMR. RESULTS: CRISPR-Cas systems have demonstrated efficacy in combating antimicrobial resistance by targeting and eliminating antibiotic-resistance genes. For example, studies have shown that CRISPR-Cas9 can effectively target and eliminate colistin resistance genes in MCR-1 plasmids, restoring susceptibility to carbapenems in bacteria such as E. coli and Klebsiella pneumoniae. Further molecular findings highlight the impact of CRISPR-Cas systems on various bacterial species, such as Enterococcus faecalis, in which CRISPR systems play a crucial role in preventing the acquisition of resistance genes. The effectiveness of CRISPR-Cas in targeting these genes varies due to differences in CRISPR locus formation among bacterial species. For instance, variations in CRISPR loci influence the targeting of resistance genes in E. faecalis, and CRISPR-Cas9 successfully reduces resistance by targeting genes such as tetM and ermB. CONCLUSION: CRISPR-Cas systems are promising for fighting AMR by targeting and eliminating antibiotic-resistant genes, as demonstrated by the effective targeting of colistin resistance genes on MCR-1 plasmids and their similar activities. However, the effectiveness of CRISPR-Cas is affected by variations in the CRISPR loci among bacterial species. Challenges persist, such as optimizing delivery methods and addressing off-target effects to ensure the safety and precision of CRISPR-Cas systems in clinical settings. | 2024 | 39619730 |
| 5112 | 19 | 0.9993 | Genome-Based Prediction of Bacterial Antibiotic Resistance. Clinical microbiology has long relied on growing bacteria in culture to determine antimicrobial susceptibility profiles, but the use of whole-genome sequencing for antibiotic susceptibility testing (WGS-AST) is now a powerful alternative. This review discusses the technologies that made this possible and presents results from recent studies to predict resistance based on genome sequences. We examine differences between calling antibiotic resistance profiles by the simple presence or absence of previously known genes and single-nucleotide polymorphisms (SNPs) against approaches that deploy machine learning and statistical models. Often, the limitations to genome-based prediction arise from limitations of accuracy of culture-based AST in addition to an incomplete knowledge of the genetic basis of resistance. However, we need to maintain phenotypic testing even as genome-based prediction becomes more widespread to ensure that the results do not diverge over time. We argue that standardization of WGS-AST by challenge with consistently phenotyped strain sets of defined genetic diversity is necessary to compare the efficacy of methods of prediction of antibiotic resistance based on genome sequences. | 2019 | 30381421 |