Paul F. Zierep

The paper introduces the Microbiology Galaxy Lab, a community-driven platform for reproducible and FAIR microbial data analysis, offering tools for various omics data types and use cases.

FAIRyMAGs provides a modular, FAIR-compliant Galaxy pipeline for generating and analyzing metagenome-assembled genomes, improving accessibility and reproducibility in genome-resolved metagenomics.

This study develops a machine learning framework to predict memory requirements for metagenomic assembly, improving efficiency in high-performance computing environments.

PathoGFAIR provides FAIR and adaptable metagenomics workflows for detecting and tracking pathogens from Nanopore sequencing data, achieving species-level detection in most tested samples.

This paper presents a method to predict epitope activity of non-peptidic compounds using machine learning and the IEDB database, achieving ROC-AUC values between 0.69-0.96.