Exploring Saccharomycotina Yeast Ecology Through an Ecological Ontology Framework

Harrison, Marie‐Claire; Opulente, Dana A.; Wolters, John F.; Shen, Xing‐Xing; Zhou, Xiaofan; Groenewald, Marizeth; Hittinger, Chris Todd; Rokas, Antonis; LaBella, Abigail Leavitt

doi:10.1002/yea.3981

Exploring Saccharomycotina Yeast Ecology Through an Ecological Ontology Framework

Journal Article · Wed Sep 18 00:00:00 EDT 2024 · Yeast

DOI:https://doi.org/10.1002/yea.3981· OSTI ID:2473025

Harrison, Marie‐Claire ^[1]; Opulente, Dana A. ^[2]; Wolters, John F. ^[3]; Shen, Xing‐Xing ^[4]; Zhou, Xiaofan ^[5]; Groenewald, Marizeth ^[6]; ^[3]; Rokas, Antonis ^[7]; ^[8]

Vanderbilt University, Nashville, TN (United States); University of North Carolina at Charlotte Department of Bioinformatics and Genomics
Villanova University, PA (United States); University of Wisconsin-Madison, WI (United States)
University of Wisconsin-Madison, WI (United States)
Zhejiang University, Hangzhou (China)
South China Agricultural University, Guangzhou (China)
Westerdijk Fungal Biodiversity Institute, Utrecht (The Netherlands)
Vanderbilt University, Nashville, TN (United States)
University of North Carolina at Charlotte, Kannapolis, NC (United States); University of North Carolina at Charlotte, Charlotte, NC (United States)

Yeasts in the subphylum Saccharomycotina are found across the globe in disparate ecosystems. A major aim of yeast research is to understand the diversity and evolution of ecological traits, such as carbon metabolic breadth, insect association, and cactophily. This includes studying aspects of ecological traits like genetic architecture or association with other phenotypic traits. Genomic resources in the Saccharomycotina have grown rapidly. Ecological data, however, are still limited for many species, especially those only known from species descriptions where usually only a limited number of strains are studied. Moreover, ecological information is recorded in natural language format limiting high throughput computational analysis. To address these limitations, we developed an ontological framework for the analysis of yeast ecology. A total of 1,088 yeast strains were added to the Ontology of Yeast Environments (OYE) and analyzed in a machine-learning framework to connect genotype to ecology. This framework is flexible and can be extended to additional isolates, species, or environmental sequencing data. Widespread adoption of OYE would greatly aid the study of macroecology in the Saccharomycotina subphylum.

View Accepted Manuscript (DOE)

Research Organization:: Great Lakes Bioenergy Research Center (GLBRC), Madison, WI (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER); National Science Foundation (NSF)

Grant/Contract Number:: SC0018409

OSTI ID:: 2473025

Journal Information:: Yeast, Journal Name: Yeast Journal Issue: 10 Vol. 41; ISSN 0749-503X

Publisher:: WileyCopyright Statement

Country of Publication:: United States

Language:: English

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