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PMID: 16549025 Published · epublish English Comparative Study Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.

An application of statistics to comparative metagenomics.

BMC bioinformatics ·Vol. 7 ·2006-03-20 ·Pages 162

Rodriguez-Brito B, Rohwer F, Edwards RA

Abstract

Metagenomics, sequence analyses of genomic DNA isolated directly from the environments, can be used to identify organisms and model community dynamics of a particular ecosystem. Metagenomics also has the potential to identify significantly different metabolic potential in different environments. Here we use a statistical method to compare curated subsystems, to predict the physiology, metabolism, and ecology from metagenomes. This approach can be used to identify those subsystems that are significantly different between metagenome sequences. Subsystems that were overrepresented in the Sargasso Sea and Acid Mine Drainage metagenome when compared to non-redundant databases were identified. The methodology described herein applies statistics to the comparisons of metabolic potential in metagenomes. This analysis reveals those subsystems that are more, or less, represented in the different environments that are compared. These differences in metabolic potential lead to several testable hypotheses about physiology and metabolism of microbes from these ecosystems.

MeSH Terms
Algorithms Computer Simulation Data Interpretation, Statistical Genomics/methods Models, Biological Models, Statistical Proteome/metabolism Sequence Analysis, DNA/methods Signal Transduction/physiology
Chemicals
Proteome
Authors & Affiliations
3 authors, click to expand affiliations / ORCID
Rodriguez-Brito Beltran
Computational Science Research Center, San Diego State University, San Diego, USA. [email protected]
Rohwer Forest
Edwards Robert A
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Article Info
Journal
BMC bioinformatics
Abbr.
BMC Bioinformatics
ISSN
1471-2105
DOI
10.1186/1471-2105-7-162
Email
[email protected]
Published
2006-03-20
Epub
2006-00-20
Pages
162
Language
English
Region
England
NLM ID
100965194
PMCID
PMC1473205
Subset
IM
External Links
PubMed source DOI: 10.1186/1471-2105-7-162 PMC Full Text View journal details
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