SUMMARY: Nanometa Live presents a user-friendly interface designed for real-time metagenomic data analysis and pathogen identification utilizing Oxford Nanopore Technologies' MinION and Flongle flow cells. It offers an efficient workflow and graphical interface for the visualization and interpretation of metagenomic data as it is being generated. Key features include automated BLAST validation, streamlined handling of custom Kraken2 databases, and a simplified graphical user interface for enhanced user experience. Nanometa Live is particularly notable for its capability to run without constant internet or server access once installed, setting it apart from similar tools. It provides a comprehensive view of taxonomic composition and facilitates the detection of user-defined pathogens or other species of interest, catering to both researchers and clinicians. AVAILABILITY AND IMPLEMENTATION: Nanometa Live has been implemented as a local web application using the Dash framework with Snakemake handling the data processing. The source code is freely accessible on the GitHub repository at https://github.com/FOI-Bioinformatics/nanometa_live and it is easily installable using Bioconda. It includes containerization support via Docker and Singularity, ensuring ease of use, reproducibility, and portability.

SUMMARY: The Flexible Taxonomy Database framework provides a method for modification and merging official and custom taxonomic databases to create improved databases. Using such databases will increase accuracy and precision of existing methods to classify sequence reads. AVAILABILITY AND IMPLEMENTATION: Source code is freely available at https://github.com/FOI-Bioinformatics/flextaxd and installable through Bioconda.

SUMMARY: Advances in typing methodologies have recently reformed the field of molecular epidemiology of pathogens. The falling cost of sequencing technologies is creating a deluge of whole genome sequencing data that burdens bioinformatics resources and tool development. In particular, single nucleotide polymorphisms in core genomes of pathogens are recognized as the most important markers for inferring genetic relationships because they are evolutionarily stable and amenable to high-throughput detection methods. Sequence data will provide an excellent opportunity to extend our understanding of infectious disease when the challenge of extracting knowledge from available sequence resources is met. Here, we present an efficient and user-friendly genotype classification pipeline, CanSNPer, based on an easily expandable database of predefined canonical single nucleotide polymorphisms. AVAILABILITY AND IMPLEMENTATION: All documentation and Python-based source code for the CanSNPer are freely available at http://github.com/adrlar/CanSNPer.