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At least 19 recordsLinked to original sources
PubMed2025

Autocycler: long-read consensus assembly for bacterial genomes.

MOTIVATION: Long-read sequencing enables complete bacterial genome assemblies, but individual assemblers are imperfect and often produce sequence-level and structural errors. Consensus assembly using Trycycler can improve accuracy, but its lack of automation limits scalability. There is a need for an automated method to generate high-quality consensus bacterial genome assemblies from long-read data. RESULTS: We present Autocycler, a command-line tool for generating accurate bacterial genome assemblies by combining multiple alternative long-read assemblies of the same genome. Without requiring user input, Autocycler builds a compacted De Bruijn graph from the input assemblies, clusters and filters contigs, trims overlaps, and resolves consensus sequences by selecting the most common variant at each locus. It also supports manual curation when desired, allowing users to refine assemblies in challenging or important cases. In our evaluation using Oxford Nanopore Technologies reads from five bacterial isolates, Autocycler outperformed individual assemblers, automated pipelines, and other consensus tools, producing assemblies with lower error rates and improved structural accuracy. AVAILABILITY AND IMPLEMENTATION: Autocycler is implemented in Rust, open-source, and freely available at github.com/rrwick/Autocycler. It runs on Linux and macOS and is extensively documented.

Genome, Bacterial↗
PubMed2025

Whole-genome phenotype prediction with machine learning: open problems in bacterial genomics.

MOTIVATION: How can we identify causal genetic mechanisms governing bacterial traits? Initial efforts entrusting machine learning models to handle the task of predicting phenotype from genotype yield high accuracy scores. However, attempts to extract meaningful interpretations from the predictive models are found to be corrupted by falsely identified 'causal' features. Relying solely on pattern recognition and correlations is unreliable, significantly so in bacterial genomics settings where high-dimensionality and spurious associations are the norm. Though it is not yet clear whether we can overcome this hurdle, significant efforts are being made towards discovering potential high-risk bacterial genetic variants. In view of this, we set up open problems surrounding phenotype prediction from bacterial whole-genome datasets and extending those approaches to learning causal effects, and discuss challenges that impact the reliability of a machine's decision-making when faced with datasets of this nature. RESULTS: We identify major sources of non-injectivity in the formulation of the genotype-to-phenotype mapping function-linkage-disequilibrium, limited sampling, information loss in representations, unmeasured confounders and observational noise-and analyse their implications for machine learning applications. Using a collection of 4,140 Staphylococcus aureus isolates, we illustrate challenges surrounding the defined open problems. AVAILABILITY AND IMPLEMENTATION: Raw sequencing data are available from the European Nucleotide Archive (ENA) under project accessions ERP001012, PRJEB3174, PRJEB2655, PRJEB2756, and PRJEB2944. Assemblies and annotations were generated with the Sanger bacterial pipeline (https://github.com/sanger-pathogens/vr-codebase) and unitigs extracted using DBGWAS (https://gitlab.com/leoisl/dbgwas).

Machine Learning↗
PubMed2024

Principles of bacterial genome organization, a conformational point of view.

Bacterial chromosomes are large molecules that need to be highly compacted to fit inside the cells. Chromosome compaction must facilitate and maintain key biological processes such as gene expression and DNA transactions (replication, recombination, repair, and segregation). Chromosome and chromatin 3D-organization in bacteria has been a puzzle for decades. Chromosome conformation capture coupled to deep sequencing (Hi-C) in combination with other "omics" approaches has allowed dissection of the structural layers that shape bacterial chromosome organization, from DNA topology to global chromosome architecture. Here we review the latest findings using Hi-C and discuss the main features of bacterial genome folding.

Genome, Bacterial↗
PubMed2026

Conference report: the third Bacterial Genome Sequencing Pan-European Network conference.

The third Bacterial Genome Sequencing Pan-European Network conference, held in Engelberg, Switzerland (12-15 January 2026), brought together experts from six European countries to discuss the implementation of bacterial genome sequencing in clinical microbiology and public health. Key themes included regulatory frameworks (In Vitro Diagnostic Regulation, General Data Protection Regulation), standardization, quality control, data sharing, economic evaluation, and the integration of artificial intelligence and long-read sequencing into diagnostic workflows. Across presentations, panel discussions, and workshops, participants emphasized that successful implementation of genome sequencing requires more than technical capacity: it depends on robust validation, sustainable funding, interoperable data standards, ethical governance, and interdisciplinary collaboration. The meeting highlighted that sequencing should remain question-driven and clinically meaningful, balancing cost, turnaround time, and public health impact. Overall, the conference reinforced the need for coordinated European efforts to advance responsible, standardized, and sustainable genomic surveillance and diagnostics.

bacterial genome sequencing↗
PubMed2025

PDP-Miner: an AI/ML tool to detect prophage tail proteins with depolymerase domains across thousands of bacterial genomes.

MOTIVATION: Antibiotic resistance is predicted to become the leading cause of human mortality by 2050. Despite this, no other major antibiotic class has been approved for medical use since 1987. Nevertheless, phage tail proteins offer a promising alternative, given their depolymerase activity toward outer membrane polysaccharides. Several pathogenic bacteria harbor prophages, thus making these prophages' molecular target already known. RESULTS: We therefore developed a wrapper for an existing machine learning-based phage depolymerase prediction tool (Depolymerase-Predictor), called PDP-Miner, which annotates phage tail proteins ab initio, detects depolymerase activity within this candidate protein subset, and then performs post-hoc validation by annotating protein domains thereby allowing the user to investigate for protein domains indicative of depolymerase activity. This tool allowed identification of 10 high confidence phage depolymerase gene candidates across all 1294 Pseudomonas genomes available on the International Pseudomonas Consortium Database while also accurately reporting depolymerases in known phage genomes, similarly to other software like PhageDPO or DepoScope. AVAILABILITY AND IMPLEMENTATION: Source code, test datasets and documentation are freely available for download at http:///www.github.com/jeffgauthier/pdpminer. This software is free and open source under the GNU General Public License v3.0.

Prophages↗
PubMed2025

A Step-by-Step Guide to Sequencing and Assembly of Complete Bacterial Genomes Using the Oxford Nanopore MinION.

The Oxford Nanopore (ONT) MinION enables sequencing of longer DNA/RNA fragments compared to other sequencers, such as Illumina, etc. This nanopore method provides distinct advantages for generating complete genome assemblies from microorganisms. Specifically, the R9.4 flow cells used for MinION sequencing have much lower error rates compared with earlier versions of the ONT platform. Coupled with base calling using Dorado software, higher-quality long reads can now be generated for complete bacterial genome assembly. In this chapter, we describe a detailed MinION method to assemble a complete genome from a microorganism, polish the final assembly, and evaluate the genome quality using various software tools. Because of the low cost for MinION sequencing, this platform could be an asset for virtually any laboratory interested in generating complete genomes from microorganisms.

Genome, Bacterial↗
PubMed2026

Prediction of antimicrobial minimum inhibitory concentration from bacterial genomes using a scalable and interpretable machine learning approach.

Although machine learning models can predict antimicrobial susceptibility from bacterial whole genome sequencing (WGS), state-of-the-art approaches are computationally demanding or dependent on knowledge of genetic resistance determinants. Here, we describe an efficient data-driven approach to predicting minimum inhibitory concentration (MIC) by progressively extending and refining predictive genome segments, independent of prior knowledge of resistance determinants. Resultant models had high interpretability - known and potentially novel resistance determinants were captured. Using 762 clinical E. coli strains, 71.6% of predictions were within one dilution of the measured MIC. Models trained with this algorithm generalised better onto external data (F1 score = 0.85) compared with alternative models trained on annotated resistance determinants (F1 = 0.82) or k-mer counts (F1 = 0.74). Computational demands were low (RAM usage 23.6GB vs 38.8GB for k-mer model). These advantages represent an important advance in predicting antimicrobial susceptibility from WGS, with potential applications for clinical diagnostics, drug development, and surveillance.

Journal Article↗
PubMed2016

Design and synthesis of a minimal bacterial genome.

We used whole-genome design and complete chemical synthesis to minimize the 1079-kilobase pair synthetic genome of Mycoplasma mycoides JCVI-syn1.0. An initial design, based on collective knowledge of molecular biology combined with limited transposon mutagenesis data, failed to produce a viable cell. Improved transposon mutagenesis methods revealed a class of quasi-essential genes that are needed for robust growth, explaining the failure of our initial design. Three cycles of design, synthesis, and testing, with retention of quasi-essential genes, produced JCVI-syn3.0 (531 kilobase pairs, 473 genes), which has a genome smaller than that of any autonomously replicating cell found in nature. JCVI-syn3.0 retains almost all genes involved in the synthesis and processing of macromolecules. Unexpectedly, it also contains 149 genes with unknown biological functions. JCVI-syn3.0 is a versatile platform for investigating the core functions of life and for exploring whole-genome design.

Artificial Cells↗
PubMed2025

The efficacy of a filtered handheld far-ultraviolet disinfection device for decontamination of high-touch surfaces in healthcare settings: a genomic bacterial analysis.

BACKGROUND AND OBJECTIVES: Enhanced environmental disinfection is linked to reduced hospital-acquired infection rates. In this study, we aimed to evaluate the efficacy of an emerging disinfection technology, a filtered far-UV-C handheld (FFUHH) device, for reducing bacterial loads on high-touch surfaces in shared clinical workrooms, and to isolate, identify and characterize clinically significant environmental pathogens. METHODS: We compared samples from high-touch items (dictation device, mouse, armchair, desk, and keyboard) before and after FFUHH treatment. Samples were collected weekly: contact plates for colony counts and swabs before and after intervention on standardized adjacent areas for each surface, respectively. The swabs were enriched and cultured on selective media to isolate pathogens. Environmental samples, as well as clinical samples collected from patients during the study period, were validated using MALDI-TOF and whole genome sequencing. RESULTS: Among the 440 collected plates (220 before and 220 after treatment), the highest mean colony count pre-treatment was detected from armchairs, and the lowest from keyboards. The mean reduction of colony-forming units ranged 53% and 83% and was statistically significant (P < 0.05) across all surfaces except for the keyboard. We characterized multidrug-resistant Staphylococcus epidermidis ST5 and ST16 strains, a carbapenem-resistant Acinetobacter baumannii, and a Klebsiella pneumoniae genetically related to a clinical isolate with a rare sequence type not previously detected in our institution. CONCLUSION: The FFUHH effectively reduced the microbial burden on high-touch surfaces. It can offer an advantage for surface disinfection and an alternative to routinely used biocides.

Humans↗
PubMed2024

The promise of CRISPR-associated transposons for bacterial functional genomics.

CRISPR-associated transposons (CASTs) are naturally occurring amalgamations of CRISPR-Cas machinery and Tn7-like transposons that direct site-specific integration of transposon DNA via programmable guide RNAs. Although the mechanisms of CAST-based transposition have been well studied at the molecular and structural level, CASTs have yet to be broadly applied to bacterial genome engineering and systematic gene phenotyping (i.e. functional genomics) - likely due to their relatively recent discovery. Here, we describe the function and applications of CASTs, focusing on well-characterized systems, including the type I-F CAST from Vibrio cholerae (VcCAST) and type V-K CAST from Scytonema hofmanni (ShCAST). Further, we discuss the potentially transformative impact of targeted transposition on bacterial functional genomics by proposing genome-scale extensions of existing CAST tools.

DNA Transposable Elements↗
PubMed2026

In silico genome mining and characterization of putative horse feces-derived bacterial phytases as potential monogastric animal feed additive candidates.

Phytic acid exerts a significant antinutritional effect in poultry, swine, and fish, which can be mitigated by supplementing monogastric feeds with efficient microbial phytases. Accordingly, mining bacterial genomes for novel phytases represents a strategic computational approach to identifying candidates for improving monogastric animal nutrition. In this study, 162 bacterial genomes associated with horse feces were systematically mined using an in silico pipeline to identify and characterize putative phytases.A total of 69 non-redundant sequences were identified and classified as histidine acid phytase (HAPhy) or protein tyrosine phosphatase-like phytase (PTPLPhy). HAPhys were detected in the genomes of Escherichia coli, Klebsiella pneumoniae, Salmonella enterica, Acinetobacter baumannii, and Cutibacterium equinum, whereas PTPLPhys were found in K. pneumoniae, Limosilactobacillus reuteri, Pediococcus acidilactici, Bifidobacterium pseudolongum, and Prescottella equi. Principal component analysis identified glucose-1-phosphatase (CAJ1242485.1) and bifunctional acid phosphatase (NHR17779.1) as the HAPhy candidates exhibiting the most favorable predicted physicochemical properties for potential feed applications. Similarly, among the PTPLPhys, protein tyrosine phosphatase (UNQ40438.1) and a hypothetical protein (CAJ1246072.1) showed the most favorable computational profiles. Biosafety analysis identified potential virulence factors, indicating that sources should be screened prior to feed application. High-quality AlphaFold2 models were obtained for these phytases (90.9-97.2). Molecular docking analysis showed that NHR17779.1 exhibited the strongest binding to phytic acid, whereas CAJ1246072.1 demonstrated the weakest interaction. Overall, this study identifies the horse fecal microbiota as a diverse source of putative phytases that may serve as promising targets for genetic and protein engineering; however, further in vitro and in vivo studies are essential to validate the enzymatic activity and industrial efficacy of these computational candidates.

Bacterial phytase↗
PubMed2025

Systematically investigating and identifying bacteriocins in the human gut microbiome.

Human gut microbiota produces unmodified bacteriocins, natural antimicrobial peptides that protect against pathogens and regulate host physiology. However, current bioinformatic tools limit the comprehensive investigation of bacteriocins' biosynthesis, obstructing research into their biological functions. Here, we introduce IIBacFinder, a superior analysis pipeline for identifying unmodified class II bacteriocins. Through large-scale bioinformatic analysis and experimental validation, we demonstrate their widespread distribution across the bacterial kingdom, with most being habitat specific. Analyzing over 280,000 bacterial genomes, we reveal the diverse potential of human gut bacteria to produce these bacteriocins. Guided by meta-omics analysis, we synthesized 26 hypothetical bacteriocins from gut commensal species, with 16 showing antibacterial activities. Further ex vivo tests show minimal impact of narrow-spectrum bacteriocins on human fecal microbiota. Our study highlights the huge biosynthetic potential of unmodified bacteriocins in the human gut, paving the way for understanding their biological functions and health implications.

Humans↗
PubMed2026

Host life-history strategy is a critical determinant of virulent phage infection propensity.

Bacteriophages shape microbial communities through two major lifestyles: virulent (obligately lytic) and temperate (capable of lysogeny). Prevailing phage ecology frameworks focus on how environmental conditions, host density, and physiological state modulate infection modality. This perspective overlooks how host traits exert selective pressure on the distribution of virulent and temperate lifestyles across bacterial species, which limits understanding of phage ecology. To address this critical knowledge gap, we adopt a host-centric, trait-based perspective and use 5821 complete bacterial genomes to build a host life-history space predominantly defined by genome size, metabolic capacity, and growth rate potential. After mapping phage lifestyle association signals, prophage burden formed a continuous gradient across this space. Also, virulent phage association was positively correlated with prophage burden, revealing a nested structure of lifestyle signals. Functional trait analysis identified enrichment of resource-acquisition modules underlying both temperate and virulent associations. Overall, these findings indicate that phage lifestyle is significantly influenced by host life-history strategies, highlighting fast-growing, metabolically versatile hosts as favorable targets for virulent phage isolation and biocontrol applications.

Bacteriophages↗
PubMed2024

Mosquitoes provide a transmission route between possums and humans for Buruli ulcer in southeastern Australia.

Buruli ulcer, a chronic subcutaneous infection caused by Mycobacterium ulcerans, is increasing in prevalence in southeastern Australia. Possums are a local wildlife reservoir for M. ulcerans and, although mosquitoes have been implicated in transmission, it remains unclear how humans acquire infection. We conducted extensive field survey analyses of M. ulcerans prevalence among mosquitoes in the Mornington Peninsula region of southeastern Australia. PCR screening of trapped mosquitoes revealed a significant association between M. ulcerans and Aedes notoscriptus. Spatial scanning statistics revealed overlap between clusters of M. ulcerans-positive Ae. notoscriptus, M. ulcerans-positive possum excreta and Buruli ulcer cases, and metabarcoding analyses showed individual mosquitoes had fed on humans and possums. Bacterial genomic analysis confirmed shared single-nucleotide-polymorphism profiles for M. ulcerans detected in mosquitoes, possum excreta and humans. These findings indicate Ae. notoscriptus probably transmit M. ulcerans in southeastern Australia and highlight mosquito control as a Buruli ulcer prevention measure.

Animals↗
PubMed2026

cgDist: Nucleotide-level distance calculation from cgMLST allelic profiles.

Bacterial genomic surveillance requires balancing computational efficiency with genetic resolution for effective cluster investigation. cgMLST distance calculations treat all allelic differences as equivalent units, obscuring nucleotide-level variation. Furthermore, single nucleotide polymorphism-based pipelines provide finer resolution at substantially higher computational cost, which limits their routine deployment in surveillance laboratories. We present cgDist, an algorithm that calculates nucleotide-level distances directly from cgMLST allelic profiles, providing finer resolution than allele-count distances by leveraging within-allele nucleotide variation. The cache architecture stores alignment statistics, enabling distance calculation modes without computation and supporting both dataset-specific and schema-complete cache generation. This design enables incremental surveillance analysis, with performance benefits as laboratories accumulate alignment data. cgDist functions as a precision 'zoom lens' for the investigation of clusters identified through initial cgMLST screening. Rather than restructuring population relationships, this targeted approach concentrates enhanced resolution where it is most informative. The algorithm ensures that cgDist distances are greater than or equal to corresponding cgMLST distances, preserving epidemiological interpretability while adding genetic discrimination. By increasing resolution within identified clusters, cgDist may also support outbreak investigation, a potential application that remains to be evaluated on outbreak-derived data.

Algorithms↗
PubMed2025

Rapid assessment of clinical severity for salmonellosis cases via protein family domain analysis and machine learning.

Salmonella is a common pathogen, infecting more than a million people yearly. Rapid assessment of clinical case severity is essential for improving patient outcomes and optimizing healthcare resources. Advancements in genome sequencing technologies have enabled the analysis of bacterial genomes from many clinical cases, opening up new opportunities for precise and timely diagnosis. This study proposes a genome-based framework for identifying critical Salmonella cases before the onset of critical symptoms and facilitating early medical intervention. By leveraging protein family (Pfam) domains as the representation for genomic data, the complex genetic profiles of Salmonella cases are simplified into interpretable features. The severity levels of cases were investigated through rigorous data analysis, resulting in a set of 70 Pfam domains that could be potentially used as biomarkers. Machine Learning was employed to assess the predictive power of the curated Pfam biomarkers, achieving high accuracy (~93%) in sorting cases into critical, moderate, and mild categories. The results demonstrate the efficacy of the proposed approach. This framework highlights the potential of using bacterial genomic data in clinical decision-making, opening the window for timely personalized interventions for Salmonella infection management.

Domains of unknown function (DUFs)↗