Folker Meyer

Motivation: Antimicrobial resistance is a growing global threat, creating a need for rapid and accurate antimicrobial susceptibility testing. Current phenotypic antimicrobial susceptibility testing methods rely on prior isolation and cultivation, making them time-consuming. Whole genome sequencing combined with machine learning offers a faster and cost-effective alternative, but existing approaches are often limited in species coverage, antimicrobial scope, or data availability. Results: We developed stackPredAMR, a machine learning framework for predicting resistance to 18 antimicrobial agents in three clinically important bacterial species: Escherichia coli, Klebsiella pneumoniae, and Acinetobacter baumannii. The model uses antimicrobial resistance gene presence as input and incorporates cross-resistance patterns through a stacked architecture with two random forest layers. Benchmarking on more than 2500 publicly available whole genome sequencing datasets with linked phenotypic resistance data showed strong performance, achieving a median accuracy of 0.94, ROC AUC of 0.97, and F1-score of 0.91, outperforming previously published methods. stackPredAMR is freely available and designed to support future extension to additional species and antimicrobial agents. Availability and implementation: Source code and datasets (database-driven reference approach, sample lists, and input features) are available at WIN-KID repository (https://github.com/IKIM-Essen/WIN-KID/tree/v1.0.0.0) and the release page (https://github.com/IKIM-Essen/WIN-KID/releases/tag/v1.0.0.0).

Science benefits from rapid open data sharing, but current guidelines for data reuse were established two decades ago, when databases were several million times smaller than they are today. These guidelines are largely unfamiliar to the scientific community, and, owing to the rapid increase in biological data generated in the past decade, they are also outdated. As a result, there is a lack of community standards suited to the current landscape and inconsistent implementation of data sharing policies across institutions. Here we discuss current sequence data sharing policies and their benefits and drawbacks, and present a roadmap to establish guidelines for equitable sequence data reuse, developed in consultation with a data consortium of 167 microbiome scientists. We propose the use of a Data Reuse Information (DRI) tag for public sequence data, which will be associated with at least one Open Researcher and Contributor ID (ORCID) account. The machine-readable DRI tag indicates that the data creators prefer to be contacted before data reuse, and simultaneously provides data consumers with a mechanism to get in touch with the data creators. The DRI aims to facilitate and foster collaborations, and serve as a guideline that can be expanded to other data types.

Background Next-generation sequencing for microbial communities has become a standard technique. However, the computational analysis remains resource-intensive. With declining costs and growing adoption of sequencing-based methods in many fields, validated, fully automated, reproducible and flexible pipelines are increasingly essential in various scientific fields. Results We present RiboSnake, a validated, automated, reproducible QIIME2-based pipeline implemented in Snakemake for analysing 16S rRNA gene amplicon sequencing data. RiboSnake includes pre-packaged validated parameter sets optimized for different sample types, from environmental samples to patient data. The configuration packages can be easily adapted and shared, requiring minimal user input. Conclusion RiboSnake is a new alternative for researchers employing 16S rRNA gene amplicon sequencing and looking for a customizable and user-friendly pipeline for microbiome analyses with in vitro validated settings. By automating the analysis with validated parameters for diverse sample types, RiboSnake enhances existing methods significantly. The workflow repository can be found on GitHub (https://github.com/IKIM-Essen/RiboSnake).

Background: Next-generation sequencing for assaying microbial communities has become a standard technique in recent years. However, the initial investment required into in-silico analytics is still quite significant, especially for facilities not focused on bioinformatics. With the rapid decline in costs and growing adoption of sequencing-based methods in a number of fields, validated, fully automated, reproducible and yet flexible pipelines will play a greater role in various scientific fields in the future. Results: We present RiboSnake, a validated, automated, reproducible QIIME2-based analysis pipeline implemented in Snakemake for the computational analysis of 16S rRNA gene amplicon sequencing data. The pipeline comes with pre-packaged validated parameter sets, optimized for different sample types. The sets range from complex environmental samples to patient data. The configuration packages can be easily adapted and shared, requiring minimal user input. Conclusion: RiboSnake is a new alternative for researchers employing 16S rRNA gene amplicon sequencing and looking for a customizable and yet user-friendly pipeline for microbiome analysis with in-vitro validated settings. The complete analysis generated with a fully automated pipeline based on validated parameter sets for different sample types is a significant improvement to existing methods. The workflow repository can be found on GitHub ( https://github.com/IKIM-Essen/RiboSnake ).

Science benefits from rapid, open data sharing but samples for sequencing data are expensive for data creators to acquire and process. Current guidelines for data reuse were established two decades ago, when databases were several million times smaller, necessitating an update. This article presents a roadmap to establish best practices for sequence data reuse, developed in consultation with a data consortium of 167 microbiome scientists. It introduces a Data Reuse Information tag (DRI) for public sequencing data, which will be associated with at least one Open Researcher and Contributor ID (ORCID) account. The machine-readable DRI tag indicates that the data creators prefer to be contacted prior to data reuse, and simultaneously provides data consumers with a mechanism to get in touch with the data creators. Ideally, the DRI will facilitate and foster collaborations, and serve as a guideline that can be expanded to other data types.

Background: The MG-RAST API provides search capabilities and delivers organism and function data as well as raw or annotated sequence data via the web interface and its RESTful API. For casual users, however, RESTful APIs are hard to learn and work with. Results: We created the graphical MG-RAST API explorer to help researchers more easily build and export API queries; understand the data abstractions and indices available in MG-RAST; and use the results presented in-browser for exploration, development, and debugging. Conclusions: The API explorer lowers the barrier to entry for occasional or first-time MG-RAST API users.