Improving Gene Ontology resources for functional genomics applications in plants
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Date
2024-05
Authors
Fattel, Leila
Major Professor
Advisor
Lawrence-Dill, Carolyn J.
Hufford, Matthew
Yandeau-Nelson, Marna D.
Friedberg, Iddo
Eulenstein, Oliver
Committee Member
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Abstract
Gene function prediction helps researchers form hypotheses about possible functions of genes
that have not yet been experimentally characterized. Many annotation tools have been developed
that assign functional terms based on sequence similarity, domain presence, etc. These term
datasets are organized as controlled vocabularies, with the Gene Ontology (GO) directed acyclic
graph widely adopted. This work describes the generation of GO-based functional annotation
datasets for various plant species using the Gene Ontology Meta Annotator for Plants (GOMAP)
annotation tool, and efforts to improve functional genomics predictions for plants.
Accomplishments include creating genome-wide gene function predictions across crop species,
with a focus on producing predictions for multiple maize inbred lines and demonstrating that
comparisons across multiple species retain sufficient biological information for comparative
functional genomics. As an outcome of those efforts, it became evident that the use of the full set
of GO terms for annotation in plants caused issues. For example, some assigned functions were
not present in plants (e.g., terms related to nerves, blood cells, etc. were assigned). On the other
hand, existing plant-specific subsets were either too general for developing hypotheses about
specific gene functions (Plant Slim) or were not based on functional characterization of plant
genes (i.e., they were not evidence-based). To address this issue, plant-specific GO subsets were
created by including terms assigned to plant species based on experimental data. The products of
these efforts benefit plant biologists by providing gene function prediction sets for many crops, by
demonstrating how to use predicted gene functions across species, and by providing GO
subgraphs that can be used to limit genome-wide gene function prediction sets to functions
possible for plant genes. These products advance the use of predicted gene functions for plant
biological research, not only by enabling the creation of testable hypotheses for candidate genes
but by demonstrating ways to better use predicted gene functions for plant biological research.
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dissertation