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Updated: May 9, 2026

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics

Published on: June 17, 2012

Genomic language model-based genomic prediction in plant breeding.

Ganesan Alagarasan¹, Huihui Li², Yunbi Xu³

¹Centre for Crop and Food Innovation, WA State Agricultural Biotechnology Centre, Food Futures Institute, Murdoch University, Murdoch, Western Australia 6150, Australia.

Trends in Plant Science

|May 7, 2026

Summary

Genomic prediction accuracy is plateauing. Genomic language models (GLMs) offer a new approach by using DNA sequence information, moving beyond traditional markers for better genomic selection.

Keywords:

DNA sequence grammar genomic language model genomic prediction molecular marker

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Genomic prediction using molecular markers has improved genomic selection.
Prediction accuracy often plateaus despite increased marker density and refined methods.
This saturation limits the utility of current genomic information.

Purpose of the Study:

To explore genomic language models (GLMs) as a novel framework for genomic prediction.
To investigate the potential of GLMs in capturing biologically meaningful DNA sequence grammar.
To address the limitations of traditional marker-based approaches in genomic prediction.

Main Methods:

Utilizing genomic language models (GLMs) to process sequence-based genomic information.
Comparing GLM performance against traditional marker-based genomic prediction methods.
Evaluating the biological expressivity of genomic representations.

Main Results:

Genomic language models (GLMs) provide a new framework for genomic prediction.
GLMs can incorporate richer sequence-based information, capturing DNA sequence grammar.
This approach has the potential to overcome the plateau in prediction accuracy.

Conclusions:

The future of genomic prediction relies on the biological expressivity of genomic representations, not just algorithmic refinement.
Genomic language models (GLMs) offer a principled method to expand this representational frontier.
GLMs represent a promising advancement for improving genomic selection accuracy.