Evidence map›Paper›PMID 41906130›Full record

ArticlePlant methods2026

Go Big or go home: a new gene ontology subset that improves plant gene function prediction.

Leila Fattel, Carolyn J Lawrence-Dill

Abstract read
In one paragraph

Article in Plant methods, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Sex-specific ethylene responses drive floral sexual plasticity in Cannabis sativa.The Plant journal : for cell and molecular biology · 2026
    Article
4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Leila FattelInterdepartmental Genetics and Genomics, Iowa State University, Ames, IA, 50011, USA.
Carolyn J Lawrence-DillDepartment of Soil and Crop Sciences, Colorado State University, Fort Collins, CO, 80523, USA. carolyn.lawrence-dill@colostate.edu.

Funding

National Institute of Food and Agriculture 2021-67021-35329National Science Foundation 2021-67021-35329
6 · The paper itself

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.

Indexed as

AnnotationGene functionGene ontologyPlants

Identifiers

PMID41906130
PMCPMC13154503

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.