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Operon prediction in Pyrococcus furiosus

Journal Article · · Nucleic Acids Research
DOI:https://doi.org/10.1093/nar/gkl974· OSTI ID:1625415
 [1];  [2];  [2];  [2];  [2];  [3];  [2]
  1. Georgia Inst. of Technology, Atlanta, GA (United States). School of Electrical and Computer Engineering; DOE/OSTI
  2. Univ. of Georgia, Athens, GA (United States). Inst. of Bioinformatics. Dept. of Biochemistry and Molecular Biology
  3. Georgia Inst. of Technology, Atlanta, GA (United States). School of Electrical and Computer Engineering
+ Show Author Affiliations
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.
Research Organization:
Univ. of Georgia, Athens, GA (United States)
Sponsoring Organization:
National Institutes of Health (NIH); National Science Foundation (NSF); USDOE Office of Science (SC), Biological and Environmental Research (BER). Biological Systems Science Division
Grant/Contract Number:
FG05-95ER20175
OSTI ID:
1625415
Journal Information:
Nucleic Acids Research, Journal Name: Nucleic Acids Research Journal Issue: 1 Vol. 35; ISSN 0305-1048
Publisher:
Oxford University PressCopyright Statement
Country of Publication:
United States
Language:
English

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Cited By (7)

Miniaturized technology for protein and nucleic acid point-of-care testing journal November 2012
Binary particle swarm optimization for operon prediction journal April 2010
High accuracy operon prediction method based on STRING database scores journal April 2010
Transcriptome dynamics-based operon prediction in prokaryotes journal May 2014
Directional RNA-seq reveals highly complex condition-dependent transcriptomes in E. coli K12 through accurate full-length transcripts assembling journal January 2013
Cautions about the reliability of pairwise gene correlations based on expression data journal June 2015
Features for computational operon prediction in prokaryotes journal June 2012

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