DeepG4: A deep learning approach to predict cell-type specific active G-quadruplex regions
Fig 3
Prediction performance of DeepG4 to predict active G4 regions (regions where G4s form both in vitro and in vivo).
A) Prediction performance of DeepG4. The model was trained and evaluated using HaCaT cell data. Predictions were evaluated on the testing set of sequences (same experiment as training set), but also on an independent set of sequences (from a different ChIP-seq experiment). Receiver operating characteristic (ROC) curve and area under the ROC curve (AUROC) were plotted. B) Genome browser of HaCaT-trained DeepG4 predictions and G4 ChIP-seq around KRAS gene in K562 cells. C) Genome browser of HaCaT-trained DeepG4 predictions and G4 ChIP-seq around C5orf34 gene in K562 cells. D) Prediction performance of DeepG4 trained using HaCaT data and evaluated on other cell lines. E) Genome-wide prediction performance of DeepG4 trained using HaCaT data and evaluated on other cell lines. Predictions are computed for every 200-b bins of the genome. Area Under the Precision-Recall curve is plotted (AUPR). F) Prediction performance of DeepG4* trained using HaCaT data and evaluated on other cell lines. DeepG4* is identical to DeepG4 except that chromatin accessibility is not used as input. G) Genome-wide prediction performance of DeepG4* trained using HaCaT data and evaluated on other cell lines. H) Comparison of DeepG4 and DeepG4* prediction performances, in terms of accuracy and false discovery rate (FDR) metrics. I) Comparison of DeepG4 and DeepG4* genome-wide prediction performances, in terms of accuracy and false discovery rate (FDR) metrics. J) Comparison of DeepG4 and DeepG4* promoter prediction performances, in terms of AUPR, accuracy and false discovery rate (FDR) metrics.