Standardized genome-wide function prediction enables comparative functional genomics: a new application area for Gene Ontologies in plants

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Abstract

Background: Genome-wide gene function annotations are useful for hypothesis generation and for prioritizing candidate genes potentially responsible for phenotypes of interest. We functionally annotated the genes of 18 crop plant genomes across 14 species using the GOMAP pipeline. Results: By comparison to existing GO annotation datasets, GOMAP-generated datasets cover more genes, contain more GO terms, and are similar in quality (based on precision and recall metrics using existing gold standards as the basis for comparison). From there, we sought to determine whether the datasets across multiple species could be used together to carry out comparative functional genomics analyses in plants. To test the idea and as a proof of concept, we created dendrograms of functional relatedness based on terms assigned for all 18 genomes. These dendrograms were compared to well-established species-level evolutionary phylogenies to determine whether trees derived were in agreement with known evolutionary relationships, which they largely are. Where discrepancies were observed, we determined branch support based on jackknifing then removed individual annotation sets by genome to identify the annotation sets causing unexpected relationships. Conclusions: GOMAP-derived functional annotations used together across multiple species generally retain sufficient biological signal to recover known phylogenetic relationships based on genome-wide functional similarities, indicating that comparative functional genomics across species based on GO data holds promise for generating novel hypotheses about comparative gene function and traits.

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Fattel, L., Psaroudakis, D., Yanarella, C. F., Chiteri, K. O., Dostalik, H. A., Joshi, P., … Lawrence-Dill, C. J. (2022). Standardized genome-wide function prediction enables comparative functional genomics: a new application area for Gene Ontologies in plants. GigaScience, 11. https://doi.org/10.1093/gigascience/giac023

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