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Investigating Evolutionary Phenotype...

Jackson, Laura Marie.

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Investigating Evolutionary Phenotypes Across Teleost Fishes Using Trait Ontologies.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Investigating Evolutionary Phenotypes Across Teleost Fishes Using Trait Ontologies./
作者:	Jackson, Laura Marie.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
面頁冊數:	313 p.
附註:	Source: Dissertations Abstracts International, Volume: 82-06, Section: B.
Contained By:	Dissertations Abstracts International82-06B.
標題:	Bioinformatics. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27672202
ISBN:	9798557001922

Investigating Evolutionary Phenotypes Across Teleost Fishes Using Trait Ontologies.
Jackson, Laura Marie.

Investigating Evolutionary Phenotypes Across Teleost Fishes Using Trait Ontologies. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 313 p.

Source: Dissertations Abstracts International, Volume: 82-06, Section: B.

Thesis (Ph.D.)--University of South Dakota, 2019.

This item must not be sold to any third party vendors.

Addressing any broad-scale question concerning the evolution of traits requires a comprehensive source of phenotypic data. Such large datasets must be readily extractable and computable, as manual aggregation from a dispersed literature is essentially intractable. This dissertation attempts to use publicly available synthetic datasets to investigate synthetic trait matrices and compare against a gold standard observational phenome dataset. The first objective attempts to resolve challenges associated with integrating large-scale phylogenetic trees with large morphological trait matrices using paired fin loss as a case study. This work required the development of automated methods for integration using a bioinformatics pipeline. Ontological data sources were used to generate synthetic matrices using a combined approach of data inference and taxonomic propagation, which showed how data can be extended exponentially and reduce overall missing data. The second objective used computationally derived data to develop a Full Teleostei Skeletal Phenome applicable across all teleosts. This work was the first attempt at developing a comprehensive list of skeletal features that encompass all 30,000+ teleost species. Using this newly developed standardized list, cleared and stained specimens were observed, and a fully populated comprehensive matrix was developed encompassing data for 370 terms across seven study species. The third objective compares the comprehensive dataset generated from direct specimens in the second objective to compare computationally derived datasets generated using inference and taxonomic propagation. This work aimed at quantifying ontology-derived data and uncovering gaps and conflicts among sources by comparing against a known gold standard dataset based on species observations. Using a comprehensive observed phenome collected in objective 2, this study identified the potential of using synthetically generated data by evaluating data gaps and conflicts. In summary, the computational frameworks and phenomic resources developed for this dissertation enables the study of skeletal phenomics across large taxonomic groups to study and quantify the variability among species. Overall, this is the first large-scale attempt, to our knowledge, at investigating morphological phenomes in evolutionary biology.

ISBN: 9798557001922Subjects--Topical Terms:

553671
Bioinformatics.
Subjects--Index Terms:

Bioinformatics pipeline

Investigating Evolutionary Phenotypes Across Teleost Fishes Using Trait Ontologies.
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520 $a Addressing any broad-scale question concerning the evolution of traits requires a comprehensive source of phenotypic data. Such large datasets must be readily extractable and computable, as manual aggregation from a dispersed literature is essentially intractable. This dissertation attempts to use publicly available synthetic datasets to investigate synthetic trait matrices and compare against a gold standard observational phenome dataset. The first objective attempts to resolve challenges associated with integrating large-scale phylogenetic trees with large morphological trait matrices using paired fin loss as a case study. This work required the development of automated methods for integration using a bioinformatics pipeline. Ontological data sources were used to generate synthetic matrices using a combined approach of data inference and taxonomic propagation, which showed how data can be extended exponentially and reduce overall missing data. The second objective used computationally derived data to develop a Full Teleostei Skeletal Phenome applicable across all teleosts. This work was the first attempt at developing a comprehensive list of skeletal features that encompass all 30,000+ teleost species. Using this newly developed standardized list, cleared and stained specimens were observed, and a fully populated comprehensive matrix was developed encompassing data for 370 terms across seven study species. The third objective compares the comprehensive dataset generated from direct specimens in the second objective to compare computationally derived datasets generated using inference and taxonomic propagation. This work aimed at quantifying ontology-derived data and uncovering gaps and conflicts among sources by comparing against a known gold standard dataset based on species observations. Using a comprehensive observed phenome collected in objective 2, this study identified the potential of using synthetically generated data by evaluating data gaps and conflicts. In summary, the computational frameworks and phenomic resources developed for this dissertation enables the study of skeletal phenomics across large taxonomic groups to study and quantify the variability among species. Overall, this is the first large-scale attempt, to our knowledge, at investigating morphological phenomes in evolutionary biology.
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