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Open Access Highly Accessed Methodology article

Metabolic pathways for the whole community

Niels W Hanson1, Kishori M Konwar2, Alyse K Hawley2, Tomer Altman3, Peter D Karp4 and Steven J Hallam12*

Author Affiliations

1 Graduate Program in Bioinformatics, University of British Columbia, Genome Sciences Centre, 100-570 West 7th Avenue, Vancouver, British Columbia V5Z 4S6, Canada

2 Department of Microbiology & Immunology, University of British Columbia, 2552-2350 Health Sciences Mall, Vancouver, British Columbia V6T 1Z3, Canada

3 Biomedical Informatics Training Program, Stanford University, MSOB, 1265 Welch Road, X-215 MC 5479, Stanford, CA 94305-5479, USA

4 Bioinformatics Research Group, SRI International, 333 Ravenswood Avenue, Menlo Park, CA 94025-3493, USA

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BMC Genomics 2014, 15:619  doi:10.1186/1471-2164-15-619

Published: 22 July 2014

Abstract

Background

A convergence of high-throughput sequencing and computational power is transforming biology into information science. Despite these technological advances, converting bits and bytes of sequence information into meaningful insights remains a challenging enterprise. Biological systems operate on multiple hierarchical levels from genomes to biomes. Holistic understanding of biological systems requires agile software tools that permit comparative analyses across multiple information levels (DNA, RNA, protein, and metabolites) to identify emergent properties, diagnose system states, or predict responses to environmental change.

Results

Here we adopt the MetaPathways annotation and analysis pipeline and Pathway Tools to construct environmental pathway/genome databases (ePGDBs) that describe microbial community metabolism using MetaCyc, a highly curated database of metabolic pathways and components covering all domains of life. We evaluate Pathway Tools’ performance on three datasets with different complexity and coding potential, including simulated metagenomes, a symbiotic system, and the Hawaii Ocean Time-series. We define accuracy and sensitivity relationships between read length, coverage and pathway recovery and evaluate the impact of taxonomic pruning on ePGDB construction and interpretation. Resulting ePGDBs provide interactive metabolic maps, predict emergent metabolic pathways associated with biosynthesis and energy production and differentiate between genomic potential and phenotypic expression across defined environmental gradients.

Conclusions

This multi-tiered analysis provides the user community with specific operating guidelines, performance metrics and prediction hazards for more reliable ePGDB construction and interpretation. Moreover, it demonstrates the power of Pathway Tools in predicting metabolic interactions in natural and engineered ecosystems.