Christian Schudoma

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 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 uses machine learning to meta-analyze gut microbiome data from Parkinson's disease patients, identifying microbial pathways linked to disease and potential diagnostic applications.

This study identifies C16-hydroxylated triterpenoids as key anti-inflammatory compounds in Calendula officinalis and reconstructs their biosynthetic pathway for production.

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

The paper analyzes genomic and phylogenetic data of Salmonella Typhimurium and monophasic variants from Colombia to understand their population structure, evolution, and antibiotic resistance.