Bioinformatics Advance Access published online on November 4, 2009
Bioinformatics, doi:10.1093/bioinformatics/btp626
ARH: Predicting Splice Variants from Genome-wide Data with Modified Entropy
Department of Vertebrate Genomics, Max-Planck-Institute for Molecular Genetics, Ihnestr. 63-73, D-14195 Berlin, Germany
*To whom correspondence should be addressed. Mr. Axel Rasche, E-mail: rasche{at}molgen.mpg.de
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Abstract |
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Motivation: Exon arrays allow the quantitative study of alternative splicing on a genome-wide scale. A variety of splicing prediction methods has been proposed for Affymetrix exon arrays mainly focusing on geometric correlation measures or analysis of variance. In this paper we introduce an information theoretic concept that is based on modification of the well-known entropy function.
Results: We have developed an alternative splicing robust prediction method based on entropy (ARH). We can show that this measure copes with bias inherent in the analysis of alternative splicing such as the dependency of prediction performance on the number of exons or variable exon expression. In order to judge the performance of ARH. we have compared it with eight existing splicing prediction methods using experimental benchmark data and demonstrate that ARH is a well-performing new method for the prediction of splice variants.
Availability and Implementation: ARH is implemented in R and provided in the supplementary material.
Contact: rasche{at}molgen.mpg.de
Supplementary Information: The supplementary material provides additional figures and tables, the R implementation of ARH, a basic implementation for the method comparison and the AEdb true positive set.
Associate Editor: Dr. Trey Ideker
Received on June 15, 2009; revised on August 21, 2009; accepted on October 7, 2009
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