Figure 2.

Prediction accuracy. (a) Differentiating alternative (n = 11,773) from constitutive (n = 9,638) exons. Detecting which exons are alternative (green) is significantly improved compared to a classifier that uses exon expression measurements from 33 experiments (cyan), and compared to the original classifier trained to detect only tissue-dependent cassette exons (red). Detection of exons that exhibit tissue-dependent splicing changes (blue, n = 659) is much more accurate. Numbers within each legend represent the area under the curve (AUC) (b) Identifying tissue-dependent splicing. Detecting tissue-dependent splicing changes (n = 865) from a random set of non-tissue-dependent exons (n = 4,000) achieves an overall accuracy of 89% AUC (black). Accuracy varies considerably between tissues and for detecting increased inclusion (solid line) or exclusion (dashed) in a tissue (c) Detection accuracy for an independent set of Mbnl1/2-dependent exons [14] (n = 461). Differentiating between Mbnl1/2-dependent exons and constitutive exons achieves 97% AUC. Accuracy in detecting Mbnl1/2-dependent exons from a random set of non-tissue-dependent exons (n = 2,000) is approximately 94% AUC for both brain (blue) and muscle (red).

Barash et al. Genome Biology 2013 14:R114   doi:10.1186/gb-2013-14-10-r114
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