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Performance and Scalability of Discriminative Metrics for Comparative Gene Identification in 12 Drosophila Genomes

Figure 1

Overall discovery power of discriminative metrics using 12 genomes.

(A) ROC curves showing sensitivity and specificity of each metric on classifying 10,722 known exons and 39,181 random non-coding regions. Comparative methods tended to outperform single-sequence metrics, with the exception of a baseline sequence conservation metric. CSF and the dN/dS test achieved near-perfect specificity, while RFC achieved high sensitivity. (B) Summary error statistics for each metric computed from the ROC curves. Minimum Average Error (MAE) is the minimum average of the false negative rate and false positive rate. Area Above the Curve (AAC) is the area above the ROC curve in the unit square. (C) MAE and AAC error statistics for each metric when the dataset is partitioned into several sequence length categories. All metrics tended to perform better on longer sequences than on shorter sequences. Comparative methods strongly outperformed single-sequence metrics on short sequences (60–240 nt). Inset: relative size of each sequence length category.

Figure 1