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By releasing the results of this study to the academic community, we hope to facilitate the comparison of competing exon- and transcript-level expression measures, alternative splicing detection, and selection of methods suitable for mapping exon array measures to the wealth of previously generated microarray data.
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For this reason, parallel methods for detecting and measuring alternative splicing are necessary.
First, we measured alternative splicing levels in genes from both species after transcript number normalization.
With these genes we also have the possibility to measure alternative splicing patterns.
The advent of exon and tiling microarrays means that researchers now have the capacity to experimentally measure alternative splicing on a genome wide level.
Moreover, it is entirely possible that the Affymetrix and AB platforms can be measuring alternative splice forms of the same gene.
Thus, in this work, we used the Splicing Index to measure the alternative splicing patterns in 3' gene expression microarrays.
See Additional file 1 for references To measure significant alternative splicing changes associated with primary brain tumors, we compared genome-wide exon expression levels of 24 grade IV glioblastoma (GBM) and 12 nontumor brain samples using the Human Exon Array 1.0 ST (Affymetrix, Santa Clara, CA) [ 29].
> -wrap-foot> Traditional microarray measurements, which ignore alternative splicing, are not able to fully measure variability of gene expression.
Recently, Stalteri and Harrison published a case study using a mouse gene "Surf4" and determined that some sibling probe sets on the mouse moe430a array with inconsistent measures were to detect alternative splicing (poly(A) sites) or errors [ 17].
Simply counting the number of transcripts assembled from RNA-seq data is one way to measure the extent of alternative splicing.
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