Georg Zeller

The paper shows bacterial invasion and neutrophil immunosuppression drive adenoma progression in a porcine FAP model using single-cell RNA sequencing and spatial transcriptomics.

The paper introduces the Ocean Microbiomics Database (OMDB) to analyze microbial thermal niches and predict their response to ocean warming, showing richness declines faster than abundance.

The paper develops a geometric matching method to detect catalytic features in protein structures, showing it is more sensitive than sequence-based approaches for identifying homology between distantly related enzymes.

This study shows that different colorectal cancer genotypes create distinct microbial ecosystems and that microbiota dependence decreases as tumors progress.

The paper develops Cayman, a CAZyme profiler, to analyze gut microbial CAZyme repertoires and finds differences in substrate preferences between Western and non-Western metagenomes and CRC patients.

The paper develops tools for transposon mutagenesis in Bacteroidales to identify essential genes and functional elements across three gut bacteria.

The paper introduces Python libraries for computing average nucleotide identity, matching existing tools' results and integrating with BioPython for workflow use.

IGUA is a fast and scalable method for delineating gene cluster families in prokaryotic genomes, outperforming existing tools in speed without sacrificing accuracy.

CHAMOIS predicts metabolite properties from biosynthetic gene clusters using machine learning, achieving AUPRC > 0.5 for 120 chemical properties.

The paper surveys Ruminococcus gnavus distribution across host lifestyles and health, revealing higher prevalence in Westernized populations and genomic differences between healthy and Crohn's disease isolates.

This study shows that long-term fasting changes gut microbial composition and function, as well as the serum metabolome, with significant shifts in microbial species and metabolite levels.

This study reveals interindividual variability in gut microbial metabolism of immunosuppressive drugs mediated by specific bacterial species and enzymes.

The paper benchmarks differential abundance methods in microbiome studies, showing that only a few methods control false discoveries well and that confounding variables can lead to spurious associations.

The paper evaluates metagenomic software tools using realistic datasets, highlighting improvements in assembly and taxon profiling while identifying remaining challenges in virus/archaea detection and reproducibility.

MEFISTO enables spatio-temporal dimensionality reduction and pattern separation in multi-modal genomic data, revealing conserved and diverged developmental programs and microbiome heterogeneity.

A linear conditional random field over ordered protein-domain annotations finds biosynthetic gene clusters de novo more precisely than DeepBGC and runs over three times faster on one CPU.

The paper introduces mOTUs3, a tool for reference genome-independent taxonomic profiling of metagenomes, improving accuracy and coverage for underexplored microbiomes.

The paper introduces SIAMCAT, a machine-learning toolbox for metagenomics analysis, showing it can identify disease-specific and shared biomarkers across studies.