Operon prediction in Pyrococcus furiosus

Identification of operons in the hyperthermophilic archaeon Pyrococcus furiosus represents an important step to understanding the regulatory mechanisms that enable the organism to adapt and thrive in extreme environments. We have predicted operons in P.furiosus by combining the results from three ex...

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Main Authors:	Tran, Thao T., Dam, Phuongan, Su, Zhengchang, Poole, Farris L., Adams, Michael W. W., Zhou, G. Tong, Xu, Ying
Format:	Online
Language:	English
Published:	Oxford University Press 2007
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1761436/

id	pubmed-1761436
recordtype	oai_dc
spelling	pubmed-17614362007-03-01 Operon prediction in Pyrococcus furiosus Tran, Thao T. Dam, Phuongan Su, Zhengchang Poole, Farris L. Adams, Michael W. W. Zhou, G. Tong Xu, Ying Computational Biology Identification of operons in the hyperthermophilic archaeon Pyrococcus furiosus represents an important step to understanding the regulatory mechanisms that enable the organism to adapt and thrive in extreme environments. We have predicted operons in P.furiosus by combining the results from three existing algorithms using a neural network (NN). These algorithms use intergenic distances, phylogenetic profiles, functional categories and gene-order conservation in their operon prediction. Our method takes as inputs the confidence scores of the three programs, and outputs a prediction of whether adjacent genes on the same strand belong to the same operon. In addition, we have applied Gene Ontology (GO) and KEGG pathway information to improve the accuracy of our algorithm. The parameters of this NN predictor are trained on a subset of all experimentally verified operon gene pairs of Bacillus subtilis. It subsequently achieved 86.5% prediction accuracy when applied to a subset of gene pairs for Escherichia coli, which is substantially better than any of the three prediction programs. Using this new algorithm, we predicted 470 operons in the P.furiosus genome. Of these, 349 were validated using DNA microarray data. Oxford University Press 2007-01 2006-12-05 /pmc/articles/PMC1761436/ /pubmed/17148478 http://dx.doi.org/10.1093/nar/gkl974 Text en © 2006 The Author(s) This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Tran, Thao T. Dam, Phuongan Su, Zhengchang Poole, Farris L. Adams, Michael W. W. Zhou, G. Tong Xu, Ying
spellingShingle	Tran, Thao T. Dam, Phuongan Su, Zhengchang Poole, Farris L. Adams, Michael W. W. Zhou, G. Tong Xu, Ying Operon prediction in Pyrococcus furiosus
author_facet	Tran, Thao T. Dam, Phuongan Su, Zhengchang Poole, Farris L. Adams, Michael W. W. Zhou, G. Tong Xu, Ying
author_sort	Tran, Thao T.
title	Operon prediction in Pyrococcus furiosus
title_short	Operon prediction in Pyrococcus furiosus
title_full	Operon prediction in Pyrococcus furiosus
title_fullStr	Operon prediction in Pyrococcus furiosus
title_full_unstemmed	Operon prediction in Pyrococcus furiosus
title_sort	operon prediction in pyrococcus furiosus
description	Identification of operons in the hyperthermophilic archaeon Pyrococcus furiosus represents an important step to understanding the regulatory mechanisms that enable the organism to adapt and thrive in extreme environments. We have predicted operons in P.furiosus by combining the results from three existing algorithms using a neural network (NN). These algorithms use intergenic distances, phylogenetic profiles, functional categories and gene-order conservation in their operon prediction. Our method takes as inputs the confidence scores of the three programs, and outputs a prediction of whether adjacent genes on the same strand belong to the same operon. In addition, we have applied Gene Ontology (GO) and KEGG pathway information to improve the accuracy of our algorithm. The parameters of this NN predictor are trained on a subset of all experimentally verified operon gene pairs of Bacillus subtilis. It subsequently achieved 86.5% prediction accuracy when applied to a subset of gene pairs for Escherichia coli, which is substantially better than any of the three prediction programs. Using this new algorithm, we predicted 470 operons in the P.furiosus genome. Of these, 349 were validated using DNA microarray data.
publisher	Oxford University Press
publishDate	2007
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1761436/
_version_	1611392508549398528

Operon prediction in Pyrococcus furiosus

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