Plant MethodsBioinformaticsPeer-reviewed journal article
GO Big or Go Home: A New Gene Ontology Subset that Improves Plant Gene Function Prediction
Abstract
BackgroundThe availability of gene function prediction datasets helps researchers to consider possible functions for uncharacterized genes for hypothesis generation, candidate gene prioritization, and many other applications. Many such datasets are based on the Gene Ontology (GO) function graph. For plants this can be problematic because the most specific GO terms available are often derived from the biology of non-plant taxa (e.g., functions specific to nerve function would not seem likely to map to plant biological processes given that plants lack nerves). To balance the need for functional specificity while limiting to functions relevant to plant biology, researchers often limit to the GO Slim plant subset, but, by design, that subset consists of very general terms and limits real utility for e.g., specific hypothesis generation. Worse yet, sometimes researchers choose to simply throw out terms if they are not relevant to plant biology (rather than traversing the GO graph to select the most specific term in that hierarchy that is compatible with plant biology). ResultsWe created GO Big, a Gene Ontology subset type, to improve the biological relevance of gene function predictions for taxon-specific biology applications. GO Big plant subsets retain maximal functional specificity for hypothesis generation while limiting to terms applicable to the biology of plants. In brief, we used a curatorial approach to generate two GO Big subsets, a general subset derived from terms with experimentally validated functions across Viridiplantae species, and a species-specific subset for maize (Zea mays ssp. mays). ConclusionAnnotating genes with assignments that better reflect the biology of a taxon can pave the way for more biologically accurate and testable hypotheses for genes of interest. The subsets produced here can help plant biologists limit genome-wide gene function prediction sets to functions possible for plant genes, and the process to generate GO Big subsets is described in detail to enable others to create GO Big subsets for additional taxon sets, including ones for protists, fungi, and other phylogenetic categories.
- Citations
- 3
- FWCI
- 4.16
- Field percentile
- top 9%
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Standardized genome-wide function prediction enables comparative functional genomics: a new application area for Gene Ontologies in plants
Leila Fattel, Dennis Psaroudakis, … Carolyn J. Lawrence‐Dill·GigaSciencePeer-reviewed·Jan 1, 2022
- Gene Ontology Meta Annotator for Plants (GOMAP)
Kokulapalan Wimalanathan, Carolyn J. Lawrence-Dill·bioRxiv·May 3, 2021
- JGI Plant Gene Atlas: An updateable transcriptome resource to improve structural annotations and functional gene descriptions across the plant kingdom
A. Sreedasyam, C. Plott, … E Cooper·bioRxiv·Sep 30, 2022
- GenFam: A web application and database for gene family-based classification and functional enrichment analysis
Renesh H. Bedre, Kranthi Kiran Mandadi·Plant DirectPeer-reviewed·Dec 1, 2019
- DeepGO-SE: Protein function prediction as Approximate Semantic Entailment
Maxat Kulmanov, Francisco J. Guzmán‐Vega, … Robert Hoehndorf·bioRxiv·Sep 26, 2023
- GOParsimony: parsimonious saltation across the Gene Ontology digraph for efficient functional annotation
Paul M Harrison·bioRxiv·Nov 4, 2026