Generating and designing DNA with deep generative models

TL;DR

This paper introduces deep generative neural network methods for creating and optimizing DNA sequences with specific properties, advancing the application of AI in genomics research.

Contribution

It presents three novel approaches for DNA sequence generation and design, combining GANs and activation maximization techniques, which improve the quality and properties of generated sequences.

Findings

Generated sequences capture key data structures

Sequences show improved properties over training data

Tools enable advanced DNA design for genomics

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.