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Hanson NW, Konwar KM, Hawley AK, et al. Metabolic pathways for the whole community. BMC Genomics. 2014;15:619doi: 10.1186/1471-2164-15-619.
Hanson, N. W., Konwar, K. M., Hawley, A. K., Altman, T., Karp, P. D., & Hallam, S. J. (2014). Metabolic pathways for the whole community. BMC genomics, 15619. https://doi.org/10.1186/1471-2164-15-619
Hanson, Niels W, et al. "Metabolic pathways for the whole community." BMC genomics vol. 15 (2014): 619. doi: https://doi.org/10.1186/1471-2164-15-619
Hanson NW, Konwar KM, Hawley AK, Altman T, Karp PD, Hallam SJ. Metabolic pathways for the whole community. BMC Genomics. 2014 Jul 22;15:619. doi: 10.1186/1471-2164-15-619. PMID: 25048541; PMCID: PMC4137073.
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BMC Genomics. 2014 Jul 22;15:619. doi: 10.1186/1471-2164-15-619.

Metabolic pathways for the whole community.

BMC genomics

Niels W Hanson, Kishori M Konwar, Alyse K Hawley, Tomer Altman, Peter D Karp, Steven J Hallam

Affiliations

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

PMID: 25048541 PMCID: PMC4137073 DOI: 10.1186/1471-2164-15-619

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.

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