Carolyn J. Lawrence‐Dill

The paper introduces GO Big, a new Gene Ontology subset for plants that improves gene function prediction by focusing on taxon-specific biological terms.

This paper demonstrates using spoken phenotypic descriptions in GWAS to identify genomic regions associated with maize plant height, corroborating known associations and finding novel candidate genes.

The paper uses GOMAP to annotate gene functions across 18 crop plant genomes, showing that these annotations can recover known phylogenetic relationships, supporting comparative functional genomics.

GOMAP improves genome-scale GO annotation for plants by enhancing gene annotation quality and quantity in maize and other species.

The paper demonstrates the use of natural language processing to analyze plant phenotype descriptions, generating similarity networks and providing a web tool for querying gene-phenotype associations.

GenomeQC evaluates genome assembly and gene annotation quality by integrating multiple metrics and allows benchmarking against reference genomes.

The paper translates plant phenotype descriptions into structured formats using computational methods and shows they perform comparably to manual curation for identifying genetic associations.

This paper provides a high-coverage, high-confidence GO annotation for maize genes, outperforming existing annotations in accuracy using a mixed-method pipeline.

The paper introduces a method to assess the significance of differences in gene expression among predefined gene groups, applying it to evaluate heat stress responses in maize.