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MetaCyc and AraCyc. Metabolic pathway databases for plant research
Peifen Zhang1, Hartmut Foerster, Christophe P Tissier
1The Arabidopsis Information Resource, Department of Plant Biology, Carnegie Institution of Washington, Stanford, California 94305, USA.
Plant Physiology
|May 13, 2005
Summary
MetaCyc and AraCyc databases now include over 60 new plant-specific metabolic pathways and enhanced data curation. These updates improve the prediction and analysis of plant metabolism in model organisms like Arabidopsis thaliana.
Area of Science:
- Metabolic databases
- Bioinformatics
- Plant science
Background:
- MetaCyc is a reference database for experimentally determined biochemical pathways.
- Pathway Tools software enables computational prediction of metabolic pathways from annotated genomes.
- AraCyc is a species-specific database for the model plant Arabidopsis thaliana, derived from MetaCyc.
Purpose of the Study:
- To enhance the MetaCyc database with plant-specific pathways.
- To improve the data quality and pathway coverage of the AraCyc database.
- To update pathway predictions using the latest functional annotations of Arabidopsis genes.
Main Methods:
- Addition and updating of over 60 plant-specific pathways in MetaCyc.
- Manual curation of 28 pathways from scientific literature for AraCyc.
- Updating pathway predictions in AraCyc with current gene annotations and evidence.
- Implementation of an evidence ontology for data quality.
- Expansion of the secondary metabolism node and enhancement of the cellular component ontology.
Main Results:
- AraCyc now contains 1,418 unique genes mapped to 204 pathways with 1,156 literature citations.
- The Omics Viewer tool facilitates visualization and analysis of user data on pathway maps.
- Recent enhancements include an evidence ontology, expanded secondary metabolism pathways, and improved cellular component ontology.
Conclusions:
- The updated MetaCyc and AraCyc databases provide a more comprehensive resource for plant metabolism research.
- Enhanced data quality and visualization tools facilitate deeper understanding of plant metabolic networks.
- These resources support computational prediction and experimental analysis of plant metabolic pathways.