Biology subjects

Muench, P. C.

Publications and source records attributed to Muench, P. C..

4 recordsLinked to original sources

bioRxiv2026

SeqDesk: a sequencing-facility management system for standards-compliant and FAIR (meta)data submission

Achieving FAIR compliance requires both standardized metadata and infrastructure for data deposition, yet in practice a large fraction of sequencing studies is still published without the persistent, standards-compliant metadata that reuse depends on. Collecting MIxS-compliant metadata is complex: environment-specific checklists can contain hundreds of fields, and the effort is magnified when metadata is assembled retrospectively at publication time rather than captured throughout the project. We developed SeqDesk, an open-source data management system for sequencing facilities that is designed so that FAIR-compliant public data is produced as the natural output of routine operations. Its current scope is microbial sequencing data, covering metagenomes as well as isolate genomes, for which it supports the corresponding MIxS checklists. SeqDesk gives a sequencing facility a configurable order-and-tracking system for sequencing projects, captures and validates MIxS-compliant metadata aligned with ENA checklists at project initiation, runs bioinformatics analyses through Nextflow pipelines, and brokers submission to the European Nucleotide Archive, all within the institution's own infrastructure. By embedding standards-compliant metadata capture into the sequencing-facility workflow rather than bolting it on at submission, SeqDesk shortens the path from sample to reusable public data. The underlying checklist model is generic, so support can be extended to further data types and metadata standards beyond the microbial domain. SeqDesk is free and open source under the Apache 2.0 licence and available at https://seqdesk.org, with a live demonstration at https://seqdesk.org/#demo.

bioinformatics↗

bioRxiv2025

Comparative Assessment of Large Language Models for Microbial Phenotype Annotation

Large language models (LLMs) are increasingly used to extract knowledge from text, yet their coverage and reliability in biology remain unclear. Microbial phenotypes are especially important to assess, as comprehensive data remain sparse except for well-studied organisms and they underpin our understanding of microbial characteristics, functional roles, and applications. Here, we systematically assessed the biological knowledge encoded in publicly available LLMs for structured phenotype annotation of microbial species. We evaluated the performance of over 50 LLMs, including state-of-the-art models such as Claude Sonnet 4 and the GPT-5 family of models. Across phenotypes, LLMs reached accurate assignments for many species, but performance varied widely by model and trait, and no single model dominated. Model self-reported confidence is informative, with higher confidence aligning with higher accuracy, and can be used to prioritize phenotype assignment, effectively distinguishing between high-and low-confidence inferences. Overall, our study outlines the utility and limitations of text-based LLMs for phenotype characterization in microbiology.

bioinformatics↗

bioRxiv2021

Exploring the interaction network of a synthetic gut bacterial community

A key challenge in microbiome research is to predict functionality from microbial community composition. As central microbiota functions are determined by bacterial community networks it is important to gain insight into the principles that govern bacteria-bacteria interactions. Here, we focused on growth and metabolic interactions of the Oligo-Mouse-Microbiota (OMM12) synthetic bacterial community, which is increasingly used as model system in gut microbiome research. Using a bottom-up approach, we uncovered the directionality of strain-strain interactions in mono- and pairwise co-culture experiments, as well as in community batch culture. Metabolomics analysis of spent culture supernatant of individual strains in combination with genome-informed pathway reconstruction provided insights into the metabolic potential of the individual community members. Thereby, we could show that the OMM12 interaction network is shaped by both, exploitative and interference competition in vitro. In particular, Enterococcus faecalis KB1 was identified as important driver of community composition by affecting the abundance of several other consortium members. Together, this study gives fundamental insight into key drivers and mechanistic basis of the OMM12 interaction network, which serves as knowledge base for future mechanistic studies.

microbiology↗

bioRxiv2017

Laboratory cultivation of acidophilic nanoorganisms. Physiological and bioinformatic dissection of a stable laboratory co-culture.

This study describes the laboratory cultivation of ARMAN (Archaeal Richmond Mine Acidophilic Nanoorganisms). After 2.5 years of successive transfers in an anoxic medium containing ferric sulfate as an electron acceptor, a consortium was attained that is comprised of two members of the order Thermoplasmatales, a member of a proposed ARMAN group, as well as a fungus. The 16S rRNA of one archaeon is only 91.6% identical to Thermogymnomonas acidicola as most closely related isolate. Hence, this organism is the first member of a new genus. The enrichment culture is dominated by this microorganism and the ARMAN. The third archaeon in the community seems to be present in minor quantities and has a 100% 16S rRNA identity to the recently isolated Cuniculiplasma divulgatum. The enriched ARMAN species is most probably incapable of sugar metabolism because the key genes for sugar catabolism and anabolism could not be identified in the metagenome. Metatranscriptomic analysis suggests that the TCA cycle funneled with amino acids is the main metabolic pathway used by the archaea of the community. Microscopic analysis revealed that growth of the ARMAN is supported by the formation of cell aggregates. These might enable cross feeding by other community members to the ARMAN.

microbiology↗