# | Rank | Similarity | Title + Abs. | Year | PMID |
|---|---|---|---|---|---|
| 0 | 1 | 2 | 3 | 4 | 5 |
| 4354 | 0 | 1.0000 | ARDB--Antibiotic Resistance Genes Database. The treatment of infections is increasingly compromised by the ability of bacteria to develop resistance to antibiotics through mutations or through the acquisition of resistance genes. Antibiotic resistance genes also have the potential to be used for bio-terror purposes through genetically modified organisms. In order to facilitate the identification and characterization of these genes, we have created a manually curated database--the Antibiotic Resistance Genes Database (ARDB)--unifying most of the publicly available information on antibiotic resistance. Each gene and resistance type is annotated with rich information, including resistance profile, mechanism of action, ontology, COG and CDD annotations, as well as external links to sequence and protein databases. Our database also supports sequence similarity searches and implements an initial version of a tool for characterizing common mutations that confer antibiotic resistance. The information we provide can be used as compendium of antibiotic resistance factors as well as to identify the resistance genes of newly sequenced genes, genomes, or metagenomes. Currently, ARDB contains resistance information for 13,293 genes, 377 types, 257 antibiotics, 632 genomes, 933 species and 124 genera. ARDB is available at http://ardb.cbcb.umd.edu/. | 2009 | 18832362 |
| 4375 | 1 | 0.9996 | Evidence of a large novel gene pool associated with prokaryotic genomic islands. Microbial genes that are "novel" (no detectable homologs in other species) have become of increasing interest as environmental sampling suggests that there are many more such novel genes in yet-to-be-cultured microorganisms. By analyzing known microbial genomic islands and prophages, we developed criteria for systematic identification of putative genomic islands (clusters of genes of probable horizontal origin in a prokaryotic genome) in 63 prokaryotic genomes, and then characterized the distribution of novel genes and other features. All but a few of the genomes examined contained significantly higher proportions of novel genes in their predicted genomic islands compared with the rest of their genome (Paired t test = 4.43E-14 to 1.27E-18, depending on method). Moreover, the reverse observation (i.e., higher proportions of novel genes outside of islands) never reached statistical significance in any organism examined. We show that this higher proportion of novel genes in predicted genomic islands is not due to less accurate gene prediction in genomic island regions, but likely reflects a genuine increase in novel genes in these regions for both bacteria and archaea. This represents the first comprehensive analysis of novel genes in prokaryotic genomic islands and provides clues regarding the origin of novel genes. Our collective results imply that there are different gene pools associated with recently horizontally transmitted genomic regions versus regions that are primarily vertically inherited. Moreover, there are more novel genes within the gene pool associated with genomic islands. Since genomic islands are frequently associated with a particular microbial adaptation, such as antibiotic resistance, pathogen virulence, or metal resistance, this suggests that microbes may have access to a larger "arsenal" of novel genes for adaptation than previously thought. | 2005 | 16299586 |
| 4347 | 2 | 0.9996 | Going through phages: a computational approach to revealing the role of prophage in Staphylococcus aureus. Prophages have important roles in virulence, antibiotic resistance, and genome evolution in Staphylococcus aureus . Rapid growth in the number of sequenced S. aureus genomes allows for an investigation of prophage sequences at an unprecedented scale. We developed a novel computational pipeline for phage discovery and annotation. We combined PhiSpy, a phage discovery tool, with VGAS and PROKKA, genome annotation tools to detect and analyse prophage sequences in nearly 10 011 S . aureus genomes, discovering thousands of putative prophage sequences with genes encoding virulence factors and antibiotic resistance. To our knowledge, this is the first large-scale application of PhiSpy on a large-scale set of genomes (10 011 S . aureus ). Determining the presence of virulence and resistance encoding genes in prophage has implications for the potential transfer of these genes/functions to other bacteria via transduction and thus can provide insight into the evolution and spread of these genes/functions between bacterial strains. While the phage we have identified may be known, these phages were not necessarily known or characterized in S. aureus and the clustering and comparison we did for phage based on their gene content is novel. Moreover, the reporting of these genes with the S. aureus genomes is novel. | 2023 | 37424556 |
| 4624 | 3 | 0.9996 | Deciphering the distance to antibiotic resistance for the pneumococcus using genome sequencing data. Advances in genome sequencing technologies and genome-wide association studies (GWAS) have provided unprecedented insights into the molecular basis of microbial phenotypes and enabled the identification of the underlying genetic variants in real populations. However, utilization of genome sequencing in clinical phenotyping of bacteria is challenging due to the lack of reliable and accurate approaches. Here, we report a method for predicting microbial resistance patterns using genome sequencing data. We analyzed whole genome sequences of 1,680 Streptococcus pneumoniae isolates from four independent populations using GWAS and identified probable hotspots of genetic variation which correlate with phenotypes of resistance to essential classes of antibiotics. With the premise that accumulation of putative resistance-conferring SNPs, potentially in combination with specific resistance genes, precedes full resistance, we retrogressively surveyed the hotspot loci and quantified the number of SNPs and/or genes, which if accumulated would confer full resistance to an otherwise susceptible strain. We name this approach the 'distance to resistance'. It can be used to identify the creep towards complete antibiotics resistance in bacteria using genome sequencing. This approach serves as a basis for the development of future sequencing-based methods for predicting resistance profiles of bacterial strains in hospital microbiology and public health settings. | 2017 | 28205635 |
| 4663 | 4 | 0.9996 | Pan-genomics of Ochrobactrum species from clinical and environmental origins reveals distinct populations and possible links. Ochrobactrum genus is comprised of soil-dwelling Gram-negative bacteria mainly reported for bioremediation of toxic compounds. Since last few years, mainly two species of this genus, O. intermedium and O. anthropi were documented for causing infections mostly in the immunocompromised patients. Despite such ubiquitous presence, study of adaptation in various niches is still lacking. Thus, to gain insights into the niche adaptation strategies, pan-genome analysis was carried out by comparing 67 genome sequences belonging to Ochrobactrum species. Pan-genome analysis revealed it is an open pan-genome indicative of the continuously evolving nature of the genus. The presence/absence of gene clusters also illustrated the unique presence of antibiotic efflux transporter genes and type IV secretion system genes in the clinical strains while the genes of solvent resistance and exporter pumps in the environmental strains. A phylogenomic investigation based on 75 core genes depicted better and robust phylogenetic resolution and topology than the 16S rRNA gene. To support the pan-genome analysis, individual genomes were also investigated for the mobile genetic elements (MGE), antibiotic resistance genes (ARG), metal resistance genes (MRG) and virulence factors (VF). The analysis revealed the presence of MGE, ARG, and MRG in all the strains which play an important role in the species evolution which is in agreement with the pan-genome analysis. The average nucleotide identity (ANI) based on the genetic relatedness between the Ochrobactrum species indicated a distinction between individual species. Interestingly, the ANI tool was able to classify the Ochrobactrum genomes to the species level which were assigned till the genus level on the NCBI database. | 2020 | 32428556 |
| 5114 | 5 | 0.9996 | Datasets for benchmarking antimicrobial resistance genes in bacterial metagenomic and whole genome sequencing. Whole genome sequencing (WGS) is a key tool in identifying and characterising disease-associated bacteria across clinical, agricultural, and environmental contexts. One increasingly common use of genomic and metagenomic sequencing is in identifying the type and range of antimicrobial resistance (AMR) genes present in bacterial isolates in order to make predictions regarding their AMR phenotype. However, there are a large number of alternative bioinformatics software and pipelines available, which can lead to dissimilar results. It is, therefore, vital that researchers carefully evaluate their genomic and metagenomic AMR analysis methods using a common dataset. To this end, as part of the Microbial Bioinformatics Hackathon and Workshop 2021, a 'gold standard' reference genomic and simulated metagenomic dataset was generated containing raw sequence reads mapped against their corresponding reference genome from a range of 174 potentially pathogenic bacteria. These datasets and their accompanying metadata are freely available for use in benchmarking studies of bacteria and their antimicrobial resistance genes and will help improve tool development for the identification of AMR genes in complex samples. | 2022 | 35705638 |
| 5111 | 6 | 0.9995 | Antimicrobial Resistance Prediction for Gram-Negative Bacteria via Game Theory-Based Feature Evaluation. The increasing prevalence of antimicrobial-resistant bacteria drives the need for advanced methods to identify antimicrobial-resistance (AMR) genes in bacterial pathogens. With the availability of whole genome sequences, best-hit methods can be used to identify AMR genes by differentiating unknown sequences with known AMR sequences in existing online repositories. Nevertheless, these methods may not perform well when identifying resistance genes with sequences having low sequence identity with known sequences. We present a machine learning approach that uses protein sequences, with sequence identity ranging between 10% and 90%, as an alternative to conventional DNA sequence alignment-based approaches to identify putative AMR genes in Gram-negative bacteria. By using game theory to choose which protein characteristics to use in our machine learning model, we can predict AMR protein sequences for Gram-negative bacteria with an accuracy ranging from 93% to 99%. In order to obtain similar classification results, identity thresholds as low as 53% were required when using BLASTp. | 2019 | 31597945 |
| 4631 | 7 | 0.9995 | Genome Analysis of an Enterococcal Prophage, Entfac.MY. BACKGROUND: Bacteriophages are bacterial parasites. Unlike lytic bacteriophages, lysogenic bacteriophages do not multiply immediately after entering the host cells and may integrate their genomes into the bacterial genomes as prophages. Prophages can include various phenotypic and genotypic effects on the host bacteria. Enterococcus spp. are Gram-positive bacteria that cause infections in humans and animals. In recent decades, these bacteria have become resistant to various antimicrobials, including vancomycin. The aim of this study was to analyze genome of an enterococcal prophage. METHODS: In this study, Enterococcus faecium EntfacYE was isolated from biological samples and its genome was analyzed using next-generation sequencing method. RESULTS: Overall, 254 prophage genes were identified in the bacterial genome. The prophage included 39 housekeeping, 41 replication and regulation, 80 structural and packaging, and 48 lysis genes. Moreover, 46 genes with unknown functions were identified. All genes were annotated in DNA Data Bank of Japan. CONCLUSION: In general, most prophage genes were linked to packaging and structure (31.5%) gene group. However, genes with unknown functions included a high proportion (18.11%), which indicated necessity of further analyses. Genomic analysis of the prophages can be effective in better understanding of their roles in development of bacterial resistance to antibiotics. Moreover, identification and study of prophages can help researchers develop genetic engineering tools and novel infection therapies. | 2022 | 36061127 |
| 4388 | 8 | 0.9995 | Detection of Genes Related to Antibiotic Resistance in Leptospira. Leptospirosis is a disease caused by the bacteria of the Leptospira genus, which can usually be acquired by humans through contact with urine from infected animals; it is also possible for this urine to contaminate soils and bodies of water. The disease can have deadly consequences in some extreme cases. Fortunately, until now, patients with leptospirosis have responded adequately to treatment with doxycycline and azithromycin, and no cases of antibiotic resistance have been reported. However, with the extensive use of such medications, more bacteria, such as Staphylococci and Enterococci, are becoming resistant. The purpose of this study is to determine the presence of genes related to antibiotic resistance in the Leptospira genus using bioinformatic tools, which have not been undertaken in the past. Whole genomes from the 69 described Leptospira species were downloaded from NCBI's GeneBank and analyzed using CARD (The Comprehensive Antibiotic Resistant Database) and RAST (Rapid Annotations using Subsystem Technology). After a detailed genomic search, 12 genes associated with four mechanisms were found: resistance to beta-lactamases, vancomycin, aminoglycoside adenylyltransferases, as well as multiple drug efflux pumps. Some of these genes are highly polymorphic among different species, and some of them are present in multiple copies in the same species. In conclusion, this study provides evidence of the presence of genes related to antibiotic resistance in the genomes of some species of the genus Leptospira, and it is the starting point for future experimental evaluation to determine whether these genes are transcriptionally active in some species and serovars. | 2024 | 39330892 |
| 5113 | 9 | 0.9995 | 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 |
| 4358 | 10 | 0.9995 | Genomic profiling of pediococcus acidilactici BCB1H and identification of its key features for Biotechnological innovation, food technology and medicine. Lactic acid bacteria has been extensively used in food industry because of widespread properties and Pediococcus is among one of them. This study aims to conduct a comprehensive genomic analysis of Pediococcus acidilactici strain BCB1H to elucidate its genetic composition, functional elements, and potential biotechnological applications. The objectives include identifying key genomic features such as coding sequences, tRNA and rRNA genes, antibiotic resistance genes, and secondary metabolite biosynthetic gene clusters, which will highlight the adaptability and potential of P. acidilactici strain BCB1H for use in a variety of industrial and therapeutic applications. P. acidilactici strain BCB1H was analyzed using whole-genome sequencing, which used advanced sequencing technologies to obtain comprehensive genomic data. Key genomic features, such as coding sequences, tRNA and rRNA genes, antibiotic resistance genes, and secondary metabolite biosynthetic gene clusters, were identified through bioinformatics analyses. The genomic analysis of P. acidilactici strain BCB1H revealed a genome size of approximately 1.92 million base pairs with a GC content of 42.4%. The annotation identified 1,895 genes across 192 subsystems, highlighting the metabolic pathways and functional categories. Notably, specialty genes associated with carbohydrate metabolism, stress response, pathogenicity, and amino acid synthesis were identified, underscoring the versatility and potential applications in food technology and medicine. These findings shed light on the genetic makeup and functional potential of P. acidilactici strain BCB1H, highlighting its flexibility and industrial importance. The genetic traits discovered suggest its prospective use in probiotics, food preservation, and biotechnological advancements. | 2025 | 39971970 |
| 4380 | 11 | 0.9995 | Comparative genome analysis of ciprofloxacin-resistant Pseudomonas aeruginosa reveals genes within newly identified high variability regions associated with drug resistance development. The alarming rise of ciprofloxacin-resistant Pseudomonas aeruginosa has been reported in several clinical studies. Though the mutation of resistance genes and their role in drug resistance has been researched, the process by which the bacterium acquires high-level resistance is still not well understood. How does the genomic evolution of P. aeruginosa affect resistance development? Could the exposure of antibiotics to the bacteria enrich genomic variants that lead to the development of resistance, and if so, how are these variants distributed through the genome? To answer these questions, we performed 454 pyrosequencing and a whole genome analysis both before and after exposure to ciprofloxacin. The comparative sequence data revealed 93 unique resistance strain variation sites, which included a mutation in the DNA gyrase subunit A gene. We generated variation-distribution maps comparing the wild and resistant types, and isolated 19 candidates from three discrete resistance-associated high variability regions that had available transposon mutants, to perform a ciprofloxacin exposure assay. Of these region candidates with transposon disruptions, 79% (15/19) showed a reduction in the ability to gain high-level resistance, suggesting that genes within these high variability regions might enrich for certain functions associated with resistance development. | 2013 | 23808957 |
| 4629 | 12 | 0.9995 | Screening and in silico characterization of prophages in Helicobacter pylori clinical strains. The increase of antibiotic resistance calls for alternatives to control Helicobacter pylori, a Gram-negative bacterium associated with various gastric diseases. Bacteriophages (phages) can be highly effective in the treatment of pathogenic bacteria. Here, we developed a method to identify prophages in H. pylori genomes aiming at their future use in therapy. A polymerase chain reaction (PCR)-based technique tested five primer pairs on 74 clinical H. pylori strains. After the PCR screening, 14 strains most likely to carry prophages were fully sequenced. After that, a more holistic approach was taken by studying the complete genome of the strains. This study allowed us to identify 12 intact prophage sequences, which were then characterized concerning their morphology, virulence, and antibiotic-resistance genes. To understand the variability of prophages, a phylogenetic analysis using the sequences of all H. pylori phages reported to date was performed. Overall, we increased the efficiency of identifying complete prophages to 54.1 %. Genes with homology to potential virulence factors were identified in some new prophages. Phylogenetic analysis revealed a close relationship among H. pylori-phages, although there are phages with different geographical origins. This study provides a deeper understanding of H. pylori-phages, providing valuable insights into their potential use in therapy. | 2025 | 39368610 |
| 4455 | 13 | 0.9995 | A novel method to discover fluoroquinolone antibiotic resistance (qnr) genes in fragmented nucleotide sequences. BACKGROUND: Broad-spectrum fluoroquinolone antibiotics are central in modern health care and are used to treat and prevent a wide range of bacterial infections. The recently discovered qnr genes provide a mechanism of resistance with the potential to rapidly spread between bacteria using horizontal gene transfer. As for many antibiotic resistance genes present in pathogens today, qnr genes are hypothesized to originate from environmental bacteria. The vast amount of data generated by shotgun metagenomics can therefore be used to explore the diversity of qnr genes in more detail. RESULTS: In this paper we describe a new method to identify qnr genes in nucleotide sequence data. We show, using cross-validation, that the method has a high statistical power of correctly classifying sequences from novel classes of qnr genes, even for fragments as short as 100 nucleotides. Based on sequences from public repositories, the method was able to identify all previously reported plasmid-mediated qnr genes. In addition, several fragments from novel putative qnr genes were identified in metagenomes. The method was also able to annotate 39 chromosomal variants of which 11 have previously not been reported in literature. CONCLUSIONS: The method described in this paper significantly improves the sensitivity and specificity of identification and annotation of qnr genes in nucleotide sequence data. The predicted novel putative qnr genes in the metagenomic data support the hypothesis of a large and uncharacterized diversity within this family of resistance genes in environmental bacterial communities. An implementation of the method is freely available at http://bioinformatics.math.chalmers.se/qnr/. | 2012 | 23231464 |
| 4377 | 14 | 0.9995 | Pathogenicity and other genomic islands in plant pathogenic bacteria. SUMMARY Pathogenicity islands (PAIs) were first described in uropathogenic E. coli. They are now defined as regions of DNA that contain virulence genes and are present in the genome of pathogenic strains, but absent from or only rarely present in non-pathogenic variants of the same or related strains. Other features include a variable G+C content, distinct boundaries from the rest of the genome and the presence of genes related to mobile elements such as insertion sequences, integrases and transposases. Although PAIs have now been described in a wide range of both plant and animal pathogens it has become evident that the general features of PAIs are displayed by a number of regions of DNA with functions other than pathogenicity, such as symbiosis and antibiotic resistance, and the general term genomic islands has been adopted. This review will describe a range of genomic islands in plant pathogenic bacteria including those that carry effector genes, phytotoxins and the type III protein secretion cluster. The review will also consider some medically important bacteria in order to discuss the range, acquisition and stabilization of genomic islands. | 2003 | 20569400 |
| 4344 | 15 | 0.9995 | Phenetic Comparison of Prokaryotic Genomes Using k-mers. Bacterial genomics studies are getting more extensive and complex, requiring new ways to envision analyses. Using the Ray Surveyor software, we demonstrate that comparison of genomes based on their k-mer content allows reconstruction of phenetic trees without the need of prior data curation, such as core genome alignment of a species. We validated the methodology using simulated genomes and previously published phylogenomic studies of Streptococcus pneumoniae and Pseudomonas aeruginosa. We also investigated the relationship of specific genetic determinants with bacterial population structures. By comparing clusters from the complete genomic content of a genome population with clusters from specific functional categories of genes, we can determine how the population structures are correlated. Indeed, the strain clustering based on a subset of k-mers allows determination of its similarity with the whole genome clusters. We also applied this methodology on 42 species of bacteria to determine the correlational significance of five important bacterial genomic characteristics. For example, intrinsic resistance is more important in P. aeruginosa than in S. pneumoniae, and the former has increased correlation of its population structure with antibiotic resistance genes. The global view of the pangenome of bacteria also demonstrated the taxa-dependent interaction of population structure with antibiotic resistance, bacteriophage, plasmid, and mobile element k-mer data sets. | 2017 | 28957508 |
| 9405 | 16 | 0.9995 | Functional Metagenomic Screening for Antimicrobial Resistance in the Oral Microbiome. A large proportion of bacteria, from a multitude of environments, are not yet able to be grown in the laboratory, and therefore microbiological and molecular biological investigations of these bacteria are challenging. A way to circumvent this challenge is to analyze the metagenome, the entire collection of DNA molecules that can be isolated from a particular environment or sample. This collection of DNA molecules can be sequenced and assembled to determine what is present and infer functional potential, or used as a PCR template to detect known target DNA and potentially unknown regions of DNA nearby those targets; however assigning functions to new or conserved hypothetical, functionally cryptic, genes is difficult. Functional metagenomics allows researchers to determine which genes are responsible for selectable phenotypes, such as resistance to antimicrobials and metabolic capabilities, without the prerequisite needs to grow the bacteria containing those genes or to already know which genes are of interest. It is estimated that a third of the resident species of the human oral cavity is not yet cultivable and, together with the ease of sample acquisition, makes this metagenome particularly suited to functional metagenomic studies. Here we describe the methodology related to the collection of saliva samples, extraction of metagenomic DNA, construction of metagenomic libraries, as well as the description of functional assays that have previously led to the identification of new genes conferring antimicrobial resistance. | 2021 | 34410638 |
| 5112 | 17 | 0.9995 | 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 |
| 4450 | 18 | 0.9995 | Genomic insights into intrinsic and acquired drug resistance mechanisms in Achromobacter xylosoxidans. Achromobacter xylosoxidans is an opportunistic pathogen known to be resistant to a wide range of antibiotics; however, the knowledge about the drug resistance mechanisms is limited. We used a high-throughput sequencing approach to sequence the genomes of the A. xylosoxidans type strain ATCC 27061 and a clinical isolate, A. xylosoxidans X02736, and then we used different bioinformatics tools to analyze the drug resistance genes in these bacteria. We obtained the complete genome sequence for A. xylosoxidans ATCC 27061 and the draft sequence for X02736. We predicted a total of 50 drug resistance-associated genes in the type strain, including 5 genes for β-lactamases and 17 genes for efflux pump systems; these genes are also conserved among other A. xylosoxidans genomes. In the clinical isolate, except for the conserved resistance genes, we also identified several acquired resistance genes carried by a new transposon embedded in a novel integrative and conjugative element. Our study provides new insights into the intrinsic and acquired drug resistance mechanisms in A. xylosoxidans, which will be helpful for better understanding the physiology of A. xylosoxidans and the evolution of antibiotic resistance in this bacterium. | 2015 | 25487802 |
| 9879 | 19 | 0.9995 | IntegronFinder 2.0: Identification and Analysis of Integrons across Bacteria, with a Focus on Antibiotic Resistance in Klebsiella. Integrons are flexible gene-exchanging platforms that contain multiple cassettes encoding accessory genes whose order is shuffled by a specific integrase. Integrons embedded within mobile genetic elements often contain multiple antibiotic resistance genes that they spread among nosocomial pathogens and contribute to the current antibiotic resistance crisis. However, most integrons are presumably sedentary and encode a much broader diversity of functions. IntegronFinder is a widely used software to identify novel integrons in bacterial genomes, but has aged and lacks some useful functionalities to handle very large datasets of draft genomes or metagenomes. Here, we present IntegronFinder version 2. We have updated the code, improved its efficiency and usability, adapted the output to incomplete genome data, and added a few novel functions. We describe these changes and illustrate the relevance of the program by analyzing the distribution of integrons across more than 20,000 fully sequenced genomes. We also take full advantage of its novel capabilities to analyze close to 4000 Klebsiella pneumoniae genomes for the presence of integrons and antibiotic resistance genes within them. Our data show that K. pneumoniae has a large diversity of integrons and the largest mobile integron in our database of plasmids. The pangenome of these integrons contains a total of 165 different gene families with most of the largest families being related with resistance to numerous types of antibiotics. IntegronFinder is a free and open-source software available on multiple public platforms. | 2022 | 35456751 |