Contributed talk: Deep Motif Dashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks

Deep neural network (DNN) models have recently obtained state-of-the-art prediction accuracy for the transcription factor binding (TFBS) site classification task. However, it remains unclear how these approaches identify meaningful DNA sequence signals and give insights as to why TFs bind to certain locations. In this paper, we propose a toolkit called the Deep Motif Dashboard (DeMo Dashboard) which provides a suite of visualization strategies to extract motifs, or sequence patterns from deep neural network models for TFBS classification. We demonstrate how to visualize and understand three important DNN models using three visualization methods: saliency maps, temporal output scores, and class optimization. In addition to providing insights as to how each model makes its prediction, the visualization techniques indicate that CNN-RNN makes predictions by modeling both motifs as well as dependencies among them.