Title: Generating and designing DNA with deep generative models,
Abstract: We propose generative neural network methods to generate DNA sequences and
tune them to have desired properties. We present three approaches: creating
synthetic DNA sequences using a generative adversarial network; a DNA-based
variant of the activation maximization ("deep dream") design method; and a
joint procedure which combines these two approaches together. We show that
these tools capture important structures of the data and, when applied to
designing probes for protein binding microarrays, allow us to generate new
sequences whose properties are estimated to be superior to those found in the
training data. We believe that these results open the door for applying deep
generative models to advance genomics research.