Abstract: The measurement of small molecule metabolites on a large scale offers the opportunity for a more complete understanding of cellular metabolism. We developed a high-throughput method to quantify primary amine-containing metabolites in the yeast Saccharomyces cerevisiae by the use of capillary electrophoresis in combination with fluorescent derivatization of cell extracts. We measured amino acid levels in the yeast deletion collection, a set of ~5000 strains each lacking a single gene, and developed a computational pipeline for data analysis. Amino acid peak assignments were validated by mass spectrometry, and the overall approach by the result that expected pathway intermediates accumulate in mutants of the arginine biosynthetic pathway. Global analysis of the deletion collection was then carried out using clustering methods. We grouped strains based on their metabolite profiles, revealing clusters of mutants enriched for genes encoding mitochondrial proteins, urea cycle enzymes, and vacuolar ATPase functions. One of the most striking profiles, common among several strains lacking ribosomal protein genes, accumulated lysine and a lysine-related metabolite. Mutations in the homologous ribosomal protein genes in the human result in Diamond-Blackfan anemia, demonstrating that metabolite data may have potential value in understanding disease pathology. This approach establishes metabolite profiling as capable of characterizing genes in a large collection of genetic variants.