Michael Kuhn

FastaLake constructs compact metaproteomic databases from sequence resources to improve peptide identification without requiring matched metagenomic sequencing.

The paper surveys global prokaryotic carbon fixation pathways, revealing an oxygen-tolerant rTCA cycle in surface ocean Campylobacterota lineages.

The paper catalogs diazotroph genomes and analyzes their distribution across environments, identifying freshwater and sedimentary ecosystems as key nitrogen-fixation reservoirs.

The paper examines latitudinal diversity gradients in the ocean microbiome, finding they are not universal but vary by taxa and environmental factors.

The paper introduces microntology, a controlled vocabulary for microbial habitats, and provides annotations for over 300,000 metagenomic samples.

The paper analyzes planetary microbiome structure and gene flow across habitats using metagenomic data, identifying generalist microbes as key drivers of gene transfer.

Metalog manually curates and harmonises contextual metadata for 110,733 metagenomic samples from humans, animals, ocean water and other environments, linked to taxonomic profiles and SPIRE genomes.

This study shows that fecal microbial load, not disease, is the main driver of gut microbiome changes and identifies it as a major confounder in microbiome studies.

The paper constructs a global microbial small ORFs catalogue and identifies a tool for annotating small proteins in microbial genomes.

The paper refines enterotypes in global fecal metagenomes using fuzzy clustering and introduces an Enterotype Dysbiosis Score to assess disease associations.

The paper uses machine learning to predict antimicrobial peptides from microbiome data, identifies novel peptides, and validates their antibacterial activity against drug-resistant pathogens.

The paper introduces SF-Matching, a machine learning method for metabolite identification from MS/MS spectra using structural features, achieving high accuracy when combined with CSI:FingerID.

The paper develops a method to map tissue-specific gene expression across species without homologous tissue definitions, finding conserved expression programs across diverse taxa.