Journal Article

ARH: predicting splice variants from genome-wide data with modified entropy

Axel Rasche and Ralf Herwig

in Bioinformatics

Volume 26, issue 1, pages 84-90
Published in print January 2010 | ISSN: 1367-4803
Published online November 2009 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btp626
ARH: predicting splice variants from genome-wide data with modified entropy

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Motivation: Exon arrays allow the quantitative study of alternative splicing (AS) 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 article, we introduce an information theoretic concept that is based on modification of the well-known entropy function.

Results: We have developed an AS robust prediction method based on entropy (ARH). We can show that this measure copes with bias inherent in the analysis of AS 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@molgen.mpg.de

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  5005 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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