Extended Data Fig. 1: Performance comparison with different outlier detection methods and different differential splicing cutoffs.

a, Distribution of the area under the precision-recall curve across GTEx tissues (n = 49) of different prediction methods (SpliceAI, SpliceAI using SpliceMap annotation, SpliceAI using SpliceMap annotation along with quantitative reference levels of splicing, MMSplice using GENCODE annotation, MMSplice using SpliceMap annotation, MMSplice using SpliceMap annotation along with quantitative reference levels of splicing, and the integrative model AbSplice-DNA) taking as ground truth 3 different aberrant splicing callers: FRASER, LeafcutterMD and SPOT. A gene was considered aberrantly spliced if it contained at least one significant splicing outlier reported by the aberrant splicing caller without applying any additional replication or rare variant filter (Extended Data Fig. 4a for FRASER). Center line, median; box limits, first and third quartiles; whiskers span all data within 1.5 interquartile ranges of the lower and upper quartiles. P values were computed using the paired one-sided Wilcoxon test. b, Precision-recall curves comparing the overall prediction performance on all GTEx tissues of the same models as in a, using FRASER as the outlier caller and the rare variant filter in Extended Data Fig. 4c with 250 bp together with different differential splicing cutoffs, namely |ΔΨ| = 0.1, 0.2, 0.3.