Related Experiment Video
Updated: Apr 26, 2026
07:26
Metabolic Pathway Confirmation and Discovery Through 13C-labeling of Proteinogenic Amino Acids
Published on: January 26, 2012
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Metabolic pathways for the whole community.
Niels W Hanson, Kishori M Konwar, Alyse K Hawley
1Graduate Program in Bioinformatics, University of British Columbia, Genome Sciences Centre, 100-570 West 7th Avenue, Vancouver, British Columbia V5Z 4S6, Canada. shallam@mail.ubc.ca.
BMC Genomics
|July 23, 2014
Summary
This study introduces a computational pipeline for analyzing microbial metabolism, creating environmental pathway/genome databases (ePGDBs). The tools offer insights into metabolic interactions and system states across diverse environments.
Area of Science:
- Computational Biology
- Metagenomics
- Systems Biology
Background:
- High-throughput sequencing and computational power are transforming biology into information science.
- Converting sequence data into biological insights is challenging due to the complexity of biological systems.
- Agile software tools are needed for comparative analyses across multiple biological information levels (DNA, RNA, protein, metabolites).
Purpose of the Study:
- To construct and evaluate environmental pathway/genome databases (ePGDBs) for microbial community metabolism.
- To assess the performance of the MetaPathways pipeline and Pathway Tools across datasets of varying complexity.
- To define relationships between sequencing parameters and pathway recovery for reliable database construction.
Main Methods:
- Utilized the MetaPathways annotation and analysis pipeline and Pathway Tools.
- Constructed ePGDBs using MetaCyc, a curated database of metabolic pathways.
- Evaluated performance on simulated metagenomes, a symbiotic system, and the Hawaii Ocean Time-series data.
- Analyzed the impact of read length, coverage, and taxonomic pruning on ePGDBs.
Main Results:
- ePGDBs provide interactive metabolic maps and predict emergent metabolic pathways for biosynthesis and energy production.
- Differentiated between genomic potential and phenotypic expression across environmental gradients.
- Established accuracy and sensitivity relationships between sequencing parameters and pathway recovery.
Conclusions:
- Provided operating guidelines, performance metrics, and prediction hazards for ePGDB construction and interpretation.
- Demonstrated the utility of Pathway Tools for predicting metabolic interactions in natural and engineered ecosystems.
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