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Natl. Acad. Sci. U.S.A."],"published-print":{"date-parts":[[2024,4,30]]},"abstract":"<jats:p>\n                    How genomic differences contribute to phenotypic differences is a major question in biology. The recently characterized genomes, isolation environments, and qualitative patterns of growth on 122 sources and conditions of 1,154 strains from 1,049 fungal species (nearly all known) in the yeast subphylum Saccharomycotina provide a powerful, yet complex, dataset for addressing this question. We used a random forest algorithm trained on these genomic, metabolic, and environmental data to predict growth on several carbon sources with high accuracy. Known structural genes involved in assimilation of these sources and presence\/absence patterns of growth in other sources were important features contributing to prediction accuracy. By further examining growth on galactose, we found that it can be predicted with high accuracy from either genomic (92.2%) or growth data (82.6%) but not from isolation environment data (65.6%). Prediction accuracy was even higher (93.3%) when we combined genomic and growth data. After the\n                    <jats:italic>GAL<\/jats:italic>\n                    actose utilization genes, the most important feature for predicting growth on galactose was growth on galactitol, raising the hypothesis that several species in two orders, Serinales and Pichiales (containing the emerging pathogen\n                    <jats:italic>Candida auris<\/jats:italic>\n                    and the genus\n                    <jats:italic>Ogataea<\/jats:italic>\n                    , respectively), have an alternative galactose utilization pathway because they lack the\n                    <jats:italic>GAL<\/jats:italic>\n                    genes. Growth and biochemical assays confirmed that several of these species utilize galactose through an alternative oxidoreductive D-galactose pathway, rather than the canonical\n                    <jats:italic>GAL<\/jats:italic>\n                    pathway. Machine learning approaches are powerful for investigating the evolution of the yeast genotype\u2013phenotype map, and their application will uncover novel biology, even in well-studied traits.\n                  <\/jats:p>","DOI":"10.1073\/pnas.2315314121","type":"journal-article","created":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T12:51:31Z","timestamp":1714135891000},"update-policy":"https:\/\/doi.org\/10.1073\/pnas.cm10313","source":"Crossref","is-referenced-by-count":29,"title":["Machine learning enables identification of an alternative yeast galactose utilization pathway"],"prefix":"10.1073","volume":"121","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3013-9906","authenticated-orcid":false,"given":"Marie-Claire","family":"Harrison","sequence":"first","affiliation":[{"name":"Department of Biological Sciences and Evolutionary Studies Initiative, Vanderbilt University, Nashville, TN 37235"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emily J.","family":"Ubbelohde","sequence":"additional","affiliation":[{"name":"Laboratory of Genetics, Department of Energy (DOE) Great Lakes Bioenergy Research Center, Center for Genomic Science Innovation, J. 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