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


METHODS
ARH is implemented in R and provided in the Supplementary Material.
Rasche
Axel
A
Department of Vertebrate Genomics, Max-Planck-Institute for Molecular Genetics, Ihnestr. 63-73, D-14195 Berlin, Germany. rasche@molgen.mpg.de
Herwig
Ralf
R
D000465:Algorithms; D017398:Alternative Splicing / Q000235:genetics*; D002874:Chromosome Mapping / Q000379:methods*; D004247:DNA / Q000235:genetics*; D019277:Entropy; D005091:Exons / Q000235:genetics*; D016678:Genome / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D022821:RNA Splice Sites / Q000235:genetics*
D022821:RNA Splice Sites; D004247:DNA