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Genomic splice site prediction algorithm based on nucleotide sequence pattern for RNA viruses

Paper ID Volume ID Publish Year Pages File Format Full-Text
15319 1403 2009 5 PDF Available
Title
Genomic splice site prediction algorithm based on nucleotide sequence pattern for RNA viruses
Abstract

Splice site prediction on an RNA virus has two potential difficulties seriously degrading the performance of most conventional splice site predictors. One is a limited number of strains available for a virus species and the other is the diversified sequence patterns around the splice sites caused by the high mutation frequency. To overcome these two difficulties, a new algorithm called Genomic Splice Site Prediction (GSSP) algorithm, was proposed for splice site prediction of RNA viruses. The key idea of the GSSP algorithm was to characterize the interdependency among the nucleotides and base positions based on the eigen-patterns. Identified by a sequence pattern mining technique, each eigen-pattern specified a unique composition of the base positions and the nucleotides occurring at the positions. To remedy the problem of insufficient training data due to the limited number of strains for an RNA virus, a cross-species strategy was employed in this study. The GSSP algorithm was shown to be effective and superior to two conventional methods in predicting the splice sites of five RNA species in the Orthomyxoviruses family. The sensitivity and specificity achieved by the GSSP algorithm was higher than 99 and 94%, respectively, for the donor sites, and was higher than 96 and 92%, respectively, for the acceptor sites. Supplementary data associated with this work are freely available for academic use at http://homepage.ntu.edu.tw/∼d91548013/.

Keywords
Splice site prediction; RNA virus; Eigen-pattern; Cross-species strategy; Orthomyxovirus
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Genomic splice site prediction algorithm based on nucleotide sequence pattern for RNA viruses
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Publisher
Database: Elsevier - ScienceDirect
Journal: Computational Biology and Chemistry - Volume 33, Issue 2, April 2009, Pages 171–175
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
Online Support
Any Questions? feel free to contact us