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Operon prediction in Pyrococcus furiosus.

Tran TT, Dam P, Su Z, Poole FL, Adams MW, Zhou GT, Xu Y - Nucleic Acids Res. (2006)

Bottom Line: We have predicted operons in P.furiosus by combining the results from three existing algorithms using a neural network (NN).Using this new algorithm, we predicted 470 operons in the P.furiosus genome.Of these, 349 were validated using DNA microarray data.

View Article: PubMed Central - PubMed

Affiliation: School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.

ABSTRACT
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.

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Venn diagram of overlap between gene pairs for operons predicted from the NN-based method, the ‘microarray evidence list’ and the ‘putative operon list’. Predicted operons from the NN-based method overlapping the ‘microarray evidence list’ and the ‘putative operon list’ represent strong candidates for further experimental studies.
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fig3: Venn diagram of overlap between gene pairs for operons predicted from the NN-based method, the ‘microarray evidence list’ and the ‘putative operon list’. Predicted operons from the NN-based method overlapping the ‘microarray evidence list’ and the ‘putative operon list’ represent strong candidates for further experimental studies.

Mentions: The predicted operons that overlap the ‘microarray evidence list’ are annotated and can be found at . The annotation for P.furiosus was obtained from GenBank and the TIGR-Comprehensive Microbial Resource (31). In addition, a subset of this list that overlaps the ‘putative operon list’ can be found in Supplementary Table S4. The number of overlapping gene pairs from these files are summarized in the Venn diagram as shown in Figure 3. The 646 (=489+157) gene pairs common to our predicted operons and the ‘microarray evidence list’ represent 349 unique operons. The 157 gene pairs that overlap all three lists form 98 operons. The novel operons in this set provide biologists a list of targets for further experimental studies.


Operon prediction in Pyrococcus furiosus.

Tran TT, Dam P, Su Z, Poole FL, Adams MW, Zhou GT, Xu Y - Nucleic Acids Res. (2006)

Venn diagram of overlap between gene pairs for operons predicted from the NN-based method, the ‘microarray evidence list’ and the ‘putative operon list’. Predicted operons from the NN-based method overlapping the ‘microarray evidence list’ and the ‘putative operon list’ represent strong candidates for further experimental studies.
© Copyright Policy - openaccess
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC1761436&req=5

fig3: Venn diagram of overlap between gene pairs for operons predicted from the NN-based method, the ‘microarray evidence list’ and the ‘putative operon list’. Predicted operons from the NN-based method overlapping the ‘microarray evidence list’ and the ‘putative operon list’ represent strong candidates for further experimental studies.
Mentions: The predicted operons that overlap the ‘microarray evidence list’ are annotated and can be found at . The annotation for P.furiosus was obtained from GenBank and the TIGR-Comprehensive Microbial Resource (31). In addition, a subset of this list that overlaps the ‘putative operon list’ can be found in Supplementary Table S4. The number of overlapping gene pairs from these files are summarized in the Venn diagram as shown in Figure 3. The 646 (=489+157) gene pairs common to our predicted operons and the ‘microarray evidence list’ represent 349 unique operons. The 157 gene pairs that overlap all three lists form 98 operons. The novel operons in this set provide biologists a list of targets for further experimental studies.

Bottom Line: We have predicted operons in P.furiosus by combining the results from three existing algorithms using a neural network (NN).Using this new algorithm, we predicted 470 operons in the P.furiosus genome.Of these, 349 were validated using DNA microarray data.

View Article: PubMed Central - PubMed

Affiliation: School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus