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Metabolic pathways for the whole community
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 oper...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4137073/ https://www.ncbi.nlm.nih.gov/pubmed/25048541 http://dx.doi.org/10.1186/1471-2164-15-619 |
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author | Hanson, Niels W Konwar, Kishori M Hawley, Alyse K Altman, Tomer Karp, Peter D Hallam, Steven J |
author_facet | Hanson, Niels W Konwar, Kishori M Hawley, Alyse K Altman, Tomer Karp, Peter D Hallam, Steven J |
author_sort | Hanson, Niels W |
collection | PubMed |
description | 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2164-15-619) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4137073 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-41370732014-08-19 Metabolic pathways for the whole community Hanson, Niels W Konwar, Kishori M Hawley, Alyse K Altman, Tomer Karp, Peter D Hallam, Steven J BMC Genomics Methodology Article 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2164-15-619) contains supplementary material, which is available to authorized users. BioMed Central 2014-07-22 /pmc/articles/PMC4137073/ /pubmed/25048541 http://dx.doi.org/10.1186/1471-2164-15-619 Text en © Hanson et al.; licensee BioMed Central Ltd. 2014 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Methodology Article Hanson, Niels W Konwar, Kishori M Hawley, Alyse K Altman, Tomer Karp, Peter D Hallam, Steven J Metabolic pathways for the whole community |
title | Metabolic pathways for the whole community |
title_full | Metabolic pathways for the whole community |
title_fullStr | Metabolic pathways for the whole community |
title_full_unstemmed | Metabolic pathways for the whole community |
title_short | Metabolic pathways for the whole community |
title_sort | metabolic pathways for the whole community |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4137073/ https://www.ncbi.nlm.nih.gov/pubmed/25048541 http://dx.doi.org/10.1186/1471-2164-15-619 |
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