Figure 3.

Figure 3.

DeepMEL classifies melanoma enhancers and predicts important TF binding motifs. (A) Cell-topic heatmap of cisTopic applied to 339,099 ATAC-seq regions across the 16 human melanoma lines, colored by normalized topic scores. (029*) MM029_R2. (B) Example regions of a MEL-specific (topic 4) region near MIA and MES-specific (topic 7) regions upstream of SERPINE1. (C) Schematic overview of DeepMEL. Twenty-four topics or sets of coaccessible regions were used as input for training of a multiclass multilabel neural network. (D,E) Receiver operating characteristic curve (D) and precision recall curve (E) for DeepMEL on training, test, and shuffled data of topic 4 and topic 7 regions. (F) Top enriched filters learned by DeepMEL to classify regions as MEL (topic 4) or MES (topic 7). Normalized filter importance is shown per filter. (G) Example of a MEL-predicted enhancer near IRF4. (First and second rows) DeepExplainer view of the forward and reverse strand, with the height of the nucleotides indicating the importance for prediction of the MEL enhancer. (Third row) In vitro effect of point mutations on enhancer activity as measured by MPRA (Kircher et al. 2019). Colors represent the nucleotide to which the wild-type nucleotide is mutated. (Fourth row) In silico effect of point mutations as predicted by DeepMEL. (H) Correlation between the in vitro mutational effects on the IRF4 enhancer and the in silico mutagenesis predictions. (I) Performance of variant effect prediction of DeepMEL using topics (black bar, model used in this paper) or using ATAC-seq signal (white bar), and several previously tested models on the IRF4 enhancer case (Kircher et al. 2019).