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Microbes - bacteria, archaea, unicellular eukaryotes, and viruses - play an important role in human and environmental health. Growing awareness of this fact has led to a huge increase in microbiological research and applications in a variety of fields. Driven by technological advances that allow high-throughput molecular characterization of microbial species and communities, microbiological research now offers unparalleled opportunities to address current and emerging needs. As well as helping to address global health threats such as antimicrobial resistance and viral pandemics, it also has a key role to play in areas such as agriculture, waste management, water treatment, ecosystems remediation, and the diagnosis, treatment and prevention of various diseases. Reflecting this broad potential, billions of euros have been invested in microbiota research programs worldwide. Though run independently, many of these projects are closely related. However, Germany currently has no infrastructure to connect such projects or even compare their results. Thus, the potential synergy of data and expertise is being squandered. The goal of the NFDI4Microbiota consortium is to serve and connect this broad and heterogeneous research community by elevating the availability and quality of research results through dedicated training, and by facilitating the generation, management, interpretation, sharing, and reuse of microbial data. In doing so, we will also foster interdisciplinary interactions between researchers. NFDI4Microbiota will achieve this by creating a German microbial research network through training and community-building activities, and by creating a cloud-based system that will make the storage, integration and analysis of microbial data, especially omics data, consistent, reproducible, and accessible across all areas of life sciences. In addition to increasing the quality of microbial research in Germany, our training program will support widespread and proper usage of these services. Through this dual emphasis on education and services, NFDI4Microbiota will ensure that microbial research in Germany is synergistic and efficient, and thus excellent. By creating a central resource for German microbial research, NDFDI4Microbiota will establish a connecting hub for all NFDI consortia that work with microbiological data, including GHGA, NFDI4Biodiversity, NFDI4Agri and several others. NFDI4Microbiota will provide nonmicrobial specialists from these consortia with direct and easy access to the necessary expertise and infrastructure in microbial research in order to facilitate their daily work and enhance their research. The links forged through NFDI4Microbiota will not only increase the synergy between NFDI consortia, but also elevate the overall quality and relevance of microbial research in Germany. [ABSTRACT FROM AUTHOR]

Motivation: Using a single linear reference genome poses a limitation to exploring the full genomic diversity of a species. The release of a draft human pangenome underscores the increasing relevance of pangenomics to overcome these limitations. Pangenomes are commonly represented as graphs, which can represent billions of base pairs of sequence. Presently, there is a lack of scalable software able to perform key tasks on pangenomes, such as quantifying universally shared sequence across genomes (the core genome ) and measuring the extent of genomic variability as a function of sample size ( pangenome growth )., Results: We introduce Panacus (pangenome-abacus), a tool designed to rapidly perform these tasks and visualize the results in interactive plots. Panacus can process GFA files, the accepted standard for pangenome graphs, and is able to analyze a human pangenome graph with 110 million nodes in less than one hour., Availability: Panacus is implemented in Rust and is published as Open Source software under the MIT license. The source code and documentation are available at https://github.com/marschall-lab/panacus. Panacus can be installed via Bioconda at https://bioconda.github.io/recipes/panacus/README.html.

Computational pangenomics deals with the joint analysis of all genomic sequences of a species. It has already been successfully applied to various tasks in many research areas. Further advances in DNA sequencing technologies constantly let more and more genomic sequences become available for many species, leading to an increasing attractiveness of pangenomic studies. At the same time, larger datasets also pose new challenges for data structures and algorithms that are needed to handle the data. Efficient methods oftentimes make use of the concept of k-mers.Core detection is a common way of analyzing a pangenome. The pangenome's core is defined as the subset of genomic information shared among all individual members. Classically, it is not only determined on the abstract level of genes but can also be described on the sequence level.In this chapter, we provide an overview of k-mer-based methods in the context of pangenomics studies. We first revisit existing software solutions for k-mer counting and k-mer set representation. Afterward, we describe the usage of two k-mer-based approaches, Pangrowth and Corer, for pangenomic core detection., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

The comparison of large-scale genome structures across distinct species offers valuable insights into the species' phylogeny, genome organization, and gene associations. In this chapter, we review the family-free genome comparison tool FFGC that, relying on built-in interfaces with a sequence comparison tool (either BLAST+ or DIAMOND) and with an ILP solver (either CPLEX or Gurobi), provides several methods for analyses that do not require prior classification of genes across the studied genomes. Taking annotated genome sequences as input, FFGC is a complete workflow for genome comparison allowing not only the computation of measures of similarity and dissimilarity but also the inference of gene families, simultaneously based on sequence similarities and large-scale genomic features., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)