Austin D.-E. Swafford

The authors reanalyze their pan-cancer microbiome findings using decontaminated data to address concerns about contamination, batch effects, and machine learning approaches.

This paper introduces OGUs, a metagenome analysis method that uses sequence alignment hits to reference genomes for community analysis, showing better performance in predicting biological traits than taxonomic or 16S rRNA methods.

EMPress provides an interactive tool for visualizing phylogenetic trees alongside multi-omic data, enabling exploratory analyses of microbiome and metabolome datasets.

This paper expands the interpretability of human metabolomes by using food-based annotations, increasing the fraction of molecular features understood 7-fold.

The paper uses metabolomics to correct metadata classifications in cheetah microbiome studies, revealing antibiotic transfer via grooming and improving understanding of medication effects.

MASST enables querying of public small-molecule tandem MS data to discover molecular relationships in metabolomics and natural product datasets.