Exploring Saccharomycotina Yeast Ecology Through an Ecological Ontology Framework

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

doi:10.1002/yea.3981

Exploring Saccharomycotina Yeast Ecology Through an Ecological Ontology Framework

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

^*Bijbehorende auteur voor dit werk

Onderzoeksoutput: Bijdrage aan wetenschappelijk tijdschrift/periodieke uitgave › Boek/film/artikelrecensie › Wetenschappelijk

Samenvatting

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.

Originele taal-2	Engels
Pagina's (van-tot)	615-628
Aantal pagina's	14
Tijdschrift	Yeast
Volume	41
Nummer van het tijdschrift	10
DOI's	https://doi.org/10.1002/yea.3981
Status	Gepubliceerd - okt. 2024

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10.1002/yea.3981

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title = "Exploring Saccharomycotina Yeast Ecology Through an Ecological Ontology Framework",

abstract = "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.",

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author = "Harrison, {Marie Claire} and Opulente, {Dana A.} and Wolters, {John F.} and Shen, {Xing Xing} and Xiaofan Zhou and Marizeth Groenewald and Hittinger, {Chris Todd} and Antonis Rokas and LaBella, {Abigail Leavitt}",

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year = "2024",

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doi = "10.1002/yea.3981",

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AU - Harrison, Marie Claire

AU - Opulente, Dana A.

AU - Wolters, John F.

AU - Shen, Xing Xing

AU - Zhou, Xiaofan

AU - Groenewald, Marizeth

AU - Hittinger, Chris Todd

AU - Rokas, Antonis

AU - LaBella, Abigail Leavitt

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N2 - 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.

AB - 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.

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