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Genome Annotation and Assembly

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.

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Related Experiment Video

Updated: Jan 13, 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

Application of Large Language Models for Annotating Genes into Reactome Pathways.

Guanming Wu¹, Lisa Matthews², Nathan Boyer³

¹Division of Oncological Sciences, Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239, United States.

Biorxiv : the Preprint Server for Biology

|January 7, 2026

Summary

This summary is machine-generated.

Large language models (LLMs) assist in curating Reactome, a biological pathway knowledgebase, by predicting gene functions and extracting literature evidence. This AI-driven approach enhances efficiency and supports manual curation efforts.

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Last Updated: Jan 13, 2026

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Area of Science:

Bioinformatics
Computational Biology
Artificial Intelligence in Life Sciences

Background:

Reactome is a comprehensive, manually curated biological pathway knowledgebase.
Manual curation is labor-intensive and struggles to keep pace with biomedical literature growth.
Large Language Models (LLMs) and Artificial Intelligence (AI) offer potential solutions for bioinformatics resource development.

Purpose of the Study:

To explore the adoption of LLM/AI technologies for Reactome manual curation.
To develop and validate an LLM workflow to assist curators in gene annotation and pathway refinement.
To assess the utility of AI-generated summaries and extracted relationships for improving Reactome content.

Main Methods:

Developed an LLM workflow to predict gene-pathway associations and identify supporting literature.
Generated text summaries of potential molecular mechanisms and extracted functional relationships from full-text papers.
Validated workflow outputs using semantic similarity comparisons with existing Reactome annotations and manual curator evaluation.
Enhanced the workflow with protein-protein interaction data for reaction-based annotation.

Main Results:

The LLM workflow demonstrated significant enrichment of high-similarity matches between generated summaries and Reactome annotations.
Manual evaluation indicated that over half of the workflow's outputs were useful for supporting curation tasks.
An enhanced workflow incorporating protein-protein interaction data improved reaction-based annotation.

Conclusions:

Initial adoption of LLM/AI technologies shows encouraging results for Reactome curation.
The developed workflow provides a practical framework for integrating AI-assisted methods into the Reactome curation pipeline.
The strategies employed may be broadly applicable to other community knowledgebases.