Recall of test set regulatory sequence prediction for different cell lines.

Gkm-SVM models were trained on DHS datasets (positive) and corresponding negative sets of k-mer shuffled sequences (k = 2, k = 7) or genomic background sequences (tGC = 0.02) for A549 or MCF-7 cells; cell lines with two training datasets (A/B) each. Model performance was evaluated based on recall for hold-out sets (chromosome 8). There are seven different hold-out sets derived from different cell lines and we assess model generalization across cell-types. Best performance is observed for models trained on highly shuffled sequences (k = 2), model performance is reduced when trained on genomic background, while the performance of models trained on lightly shuffled sequences (k = 7) is considerably worse. Datasets are named according to S1 Table. Results for the CNN models (2conv2norm and 4conv2pool4norm) are available in S5 and S6 Tables, respectively.