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Related Experiment Video

Updated: Jan 22, 2026

Author Spotlight: Improved Methods for Preparing Transverse Sections and Unrolled Whole Mounts of Maize Leaf Primordia for Fluorescence and Confocal Imaging

Author Spotlight: Improved Methods for Preparing Transverse Sections and Unrolled Whole Mounts of Maize Leaf Primordia for Fluorescence and Confocal Imaging

Published on: September 22, 2023

Maize GO Annotation-Methods, Evaluation, and Review (maize-GAMER).

Kokulapalan Wimalanathan^1,2, Iddo Friedberg^1,3, Carson M Andorf^4,5

¹Bioinformatics and Computational Biology Iowa State University Ames IA USA.

|June 28, 2019

Summary

We developed maize-GAMER, a new Gene Ontology (GO) annotation for all maize genes, significantly improving coverage and accuracy over existing datasets. This robust annotation enhances maize functional genomics research.

Keywords:

GO functional annotation gene ontology genomics maize

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Area of Science:

Plant Genomics
Bioinformatics
Functional Genomics

Background:

Existing maize Gene Ontology (GO) annotation sets from Phytozome and Gramene cover only 41% and 56% of protein-coding genes, respectively.
Accurate functional annotation is crucial for understanding gene function and advancing maize research.

Purpose of the Study:

To create a high-coverage, robust, and reproducible functional annotation of maize protein-coding genes using Gene Ontology (GO) terms.
To improve upon existing maize GO annotation sets in terms of both coverage and accuracy.

Main Methods:

Utilized sequence similarity and protein domain presence methods, alongside mixed-method pipelines from the Critical Assessment of Function Annotation (CAFA) challenge.
Developed the maize-GAMER (GO Annotation Method, Evaluation, and Review) project.
Validated annotations against a manually curated gold standard set of 1,619 genes.

Main Results:

Achieved 100% coverage of maize protein-coding genes with GO annotations.
The new maize-GAMER annotation set demonstrated measurably higher accuracy compared to Phytozome and Gramene annotations when evaluated against the gold standard.
Provided accessible annotation data through MaizeGDB and CyVerse.

Conclusions:

The maize-GAMER project successfully generated a comprehensive and accurate functional annotation for maize genes.
This enhanced annotation resource is expected to significantly benefit maize functional genomics and breeding efforts.
The developed methodology offers a robust approach for improving gene annotations in other plant species.