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Updated: Jan 31, 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

Combining multiple functional annotation tools increases coverage of metabolic annotation.

Marc Griesemer¹, Jeffrey A Kimbrel¹, Carol E Zhou²

¹Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, CA, 94551, USA.

|December 21, 2018

Summary

This summary is machine-generated.

Comprehensive reannotation of bacterial genomes using multiple tools significantly expands metabolic network reconstruction. This approach improves functional annotation coverage, especially for non-model organisms and complex metabolic pathways.

Keywords:

Enzyme prediction Functional annotation Genome annotation Metabolic modeling Transport prediction

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mirMachine: A One-Stop Shop for Plant miRNA Annotation

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Published on: May 1, 2021

Area of Science:

Microbial genomics
Systems biology
Bioinformatics

Background:

Genome-scale metabolic modeling relies on functional gene annotations, which are often incomplete (30-50% unannotated).
Incomplete annotations limit understanding of microbial metabolic capabilities, particularly for pathways involved in complex community interactions.
This knowledge gap hinders accurate metabolic network reconstruction and analysis.

Purpose of the Study:

To comprehensively reannotate bacterial genomes to improve metabolic network reconstruction.
To assess the impact of integrating multiple annotation tools on genome coverage and metabolic network size.
To enhance the understanding of metabolic pathways in non-model organisms.

Main Methods:

Reannotation of 27 bacterial reference genomes.
Focus on enzyme (EC number) and membrane transporter annotations.
Utilized multiple databases and tools: KEGG, RAST, EFICAz, BRENDA, and TransportDB.

Main Results:

Combining annotation tools increased metabolic network reconstruction by adding an average of 40% more EC numbers.
Substrate-specific transporters increased 3-8 fold, and metabolic genes increased by 37%.
Improvements were more significant for bacterial species phylogenetically distant from model organisms like E. coli.

Conclusions:

Metabolic annotations are frequently incomplete and inconsistent across different tools.
Integrating multiple functional annotation tools substantially enhances genome coverage and metabolic network size.
This integrated approach is particularly beneficial for non-model organisms and understanding non-core metabolic pathways.