Summary: In recent years, there has been an increasing interest in bacteriophages, which has led to growing numbers of bacteriophage genomic sequences becoming available. Consequently, there is a need for a rapid and consistent genomic annotation tool dedicated for bacteriophages. Existing tools either are not designed specifically for bacteriophages or are web- and email-based and require significant manual curation, which makes their integration into bioinformatic pipelines challenging. Pharokka was created to provide a tool that annotates bacteriophage genomes easily, rapidly and consistently with standards compliant outputs. Moreover, Pharokka requires only two lines of code to install and use and takes under 5 min to run for an average 50-kb bacteriophage genome. Availability and implementation: Pharokka is implemented in Python and is available as a bioconda package using ‘conda install -c bioconda pharokka’. The source code is available on GitHub (https://github.com/gbouras13/pharokka). Pharokka has been tested on Linux-64 and MacOSX machines and on Windows using a Linux Virtual Machine.

Microorganisms found in natural environments are fundamental components of ecosystems and play vital roles in various ecological processes. Studying their genomes can provide valuable insights into the diversity, functionality, and evolution of microbial life, as well as their impacts on human health. Once the genetic material is extracted from environmental samples, it undergoes sequencing using technologies like whole genome sequencing (WGS). The raw sequence data is then analysed, and computational methods are applied to assemble the fragmented sequences and reconstruct the complete microbial genomes (Wick et al.,

It is now possible to assemble near-perfect bacterial genomes using Oxford Nanopore Technologies (ONT) long reads, but short-read polishing is usually required for perfection. However, the effect of short-read depth on polishing performance is not well understood. Here, we introduce Pypolca (with default and careful parameters) and Polypolish v0.6.0 (with a new careful parameter). We then show that: (1) all polishers other than Pypolca-careful, Polypolish-default and Polypolish-careful commonly introduce false-positive errors at low read depth; (2) most of the benefit of short-read polishing occurs by 25× depth; (3) Polypolish-careful almost never introduces false-positive errors at any depth; and (4) Pypolca-careful is the single most effective polisher. Overall, we recommend the following polishing strategies: Polypolish-careful alone when depth is very low (25×).

Improvements in the accuracy and availability of long-read sequencing mean that complete bacterial genomes are now routinely reconstructed using hybrid (i.e. short- and long-reads) assembly approaches. Complete genomes allow a deeper understanding of bacterial evolution and genomic variation beyond single nucleotide variants. They are also crucial for identifying plasmids, which often carry medically significant antimicrobial resistance genes. However, small plasmids are often missed or misassembled by long-read assembly algorithms. Here, we present Hybracter which allows for the fast, automatic and scalable recovery of near-perfect complete bacterial genomes using a long-read first assembly approach. Hybracter can be run either as a hybrid assembler or as a long-read only assembler. We compared Hybracter to existing automated hybrid and long-read only assembly tools using a diverse panel of samples of varying levels of long-read accuracy with manually curated ground truth reference genomes. We demonstrate that Hybracter as a hybrid assembler is more accurate and faster than the existing gold standard automated hybrid assembler Unicycler. We also show that Hybracter with long-reads only is the most accurate long-read only assembler and is comparable to hybrid methods in accurately recovering small plasmids.

Summary: With recent advances in sequencing technologies, it is now possible to obtain near-perfect complete bacterial chromosome assemblies cheaply and efficiently by combining a long-read-first assembly approach with short-read polishing. However, existing methods for assembling bacterial plasmids from long-read-first assemblies often misassemble or even miss bacterial plasmids entirely and accordingly require manual curation. Plassembler was developed to provide a tool that automatically assembles and outputs bacterial plasmids using a hybrid assembly approach. It achieves increased accuracy and computational efficiency compared to the existing gold standard tool Unicycler by removing chromosomal reads from the input read sets using a mapping approach. Availability and implementation: Plassembler is implemented in Python and is installable as a bioconda package using ‘conda install -c bioconda plassembler’. The source code is available on GitHub at https://github.com/gbouras13/plassembler. The full benchmarking pipeline can be found at https://github.com/gbouras13/plassembler_simulation_benchmarking, while the benchmarking input FASTQ and output files can be found at https://doi.org/10.5281/zenodo.7996690.

Public microbial genomes encode an immense record of biological diversity, evolution and molecular function, but much of this information remains difficult to reuse because raw sequencing data are not uniformly assembled, quality controlled, annotated or searchable at scale. Here we present AllTheBacteria, an open, community-built resource that transforms public bacterial short-read whole-genome sequencing reads into a uniformly processed discovery platform. The current analysed release contains 2,440,377 high-quality bacterial and archaeal genomes from 11,273 species, together with standardized taxonomic assignments, genome annotations, antimicrobial resistance calls, antiphage-defence annotations, protein structure predictions and AI-ready sequence tables. We show that this infrastructure enables applications that would otherwise be impractical, from global sequence search and outbreak contextualization to pangenome method development, antimicrobial resistance reservoir mapping and antiphage-defence ecology. As a stringent experimental demonstration, we mined 3,919,096 encrypted peptide fragments from AllTheBacteria proteomes using our deep learning model APEX 1.1, identifying 1,867 candidates with predicted antimicrobial activity. We synthesized 24 representative peptides and tested them against 20 clinically relevant bacterial strains, including antibiotic-resistant pathogens. Multiple peptides showed low-micromolar activity, membrane-responsive conformational transitions and selective envelope perturbation. A lead molecule, ATB20, reduced Acinetobacter baumannii burden in a murine skin abscess model with efficacy comparable to polymyxin B and no overt toxicity. Together, these results establish AllTheBacteria as both a foundational community resource for microbiology and a renewable engine for AI-guided antimicrobial discovery.