Predicting prokaryotic ecological niches using genome sequence analysis.

Automated DNA sequencing technology is so rapid that analysis has become the rate-limiting step. Hundreds of prokaryotic genome sequences are publicly available, with new genomes uploaded at the rate of approximately 20 per month. As a result, this growing body of genome sequences will include micro...

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Main Authors: Garret Suen, Barry S Goldman, Roy D Welch
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2007-01-01
Series:PLoS ONE
Online Access:http://europepmc.org/articles/PMC1937020?pdf=render
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spelling doaj-bcfe467e79754f3a8f776ab37c4a28102020-11-25T01:53:28ZengPublic Library of Science (PLoS)PLoS ONE1932-62032007-01-0128e74310.1371/journal.pone.0000743Predicting prokaryotic ecological niches using genome sequence analysis.Garret SuenBarry S GoldmanRoy D WelchAutomated DNA sequencing technology is so rapid that analysis has become the rate-limiting step. Hundreds of prokaryotic genome sequences are publicly available, with new genomes uploaded at the rate of approximately 20 per month. As a result, this growing body of genome sequences will include microorganisms not previously identified, isolated, or observed. We hypothesize that evolutionary pressure exerted by an ecological niche selects for a similar genetic repertoire in those prokaryotes that occupy the same niche, and that this is due to both vertical and horizontal transmission. To test this, we have developed a novel method to classify prokaryotes, by calculating their Pfam protein domain distributions and clustering them with all other sequenced prokaryotic species. Clusters of organisms are visualized in two dimensions as 'mountains' on a topological map. When compared to a phylogenetic map constructed using 16S rRNA, this map more accurately clusters prokaryotes according to functional and environmental attributes. We demonstrate the ability of this map, which we term a "niche map", to cluster according to ecological niche both quantitatively and qualitatively, and propose that this method be used to associate uncharacterized prokaryotes with their ecological niche as a means of predicting their functional role directly from their genome sequence.http://europepmc.org/articles/PMC1937020?pdf=render
collection DOAJ
language English
format Article
sources DOAJ
author Garret Suen
Barry S Goldman
Roy D Welch
spellingShingle Garret Suen
Barry S Goldman
Roy D Welch
Predicting prokaryotic ecological niches using genome sequence analysis.
PLoS ONE
author_facet Garret Suen
Barry S Goldman
Roy D Welch
author_sort Garret Suen
title Predicting prokaryotic ecological niches using genome sequence analysis.
title_short Predicting prokaryotic ecological niches using genome sequence analysis.
title_full Predicting prokaryotic ecological niches using genome sequence analysis.
title_fullStr Predicting prokaryotic ecological niches using genome sequence analysis.
title_full_unstemmed Predicting prokaryotic ecological niches using genome sequence analysis.
title_sort predicting prokaryotic ecological niches using genome sequence analysis.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2007-01-01
description Automated DNA sequencing technology is so rapid that analysis has become the rate-limiting step. Hundreds of prokaryotic genome sequences are publicly available, with new genomes uploaded at the rate of approximately 20 per month. As a result, this growing body of genome sequences will include microorganisms not previously identified, isolated, or observed. We hypothesize that evolutionary pressure exerted by an ecological niche selects for a similar genetic repertoire in those prokaryotes that occupy the same niche, and that this is due to both vertical and horizontal transmission. To test this, we have developed a novel method to classify prokaryotes, by calculating their Pfam protein domain distributions and clustering them with all other sequenced prokaryotic species. Clusters of organisms are visualized in two dimensions as 'mountains' on a topological map. When compared to a phylogenetic map constructed using 16S rRNA, this map more accurately clusters prokaryotes according to functional and environmental attributes. We demonstrate the ability of this map, which we term a "niche map", to cluster according to ecological niche both quantitatively and qualitatively, and propose that this method be used to associate uncharacterized prokaryotes with their ecological niche as a means of predicting their functional role directly from their genome sequence.
url http://europepmc.org/articles/PMC1937020?pdf=render
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