UC Santa Cruz

Recent advances in genomic sequencing have resulted in several thousands of full genomes of pseudomonads, a genera of bacteria important in many science areas rang- ing from biogeochemical cycling in the environment to bacterial pneumonia in humans. With these high-quality data sets, combined with tens of thousands of somewhat lower quality metagenomically assembled genomes, we create a generative model for pseu- domonad genomes. We present a Generative Adversarial Network (GAN) model that generates gene family presence absence lists as a representation of a novel genome. We also demonstrate that the discriminator of this model can be used as a binary classifier to identify incorrect genomes with missing content. In the future, our desired model can be used to generate genomes within a given set of parameters such as, “Generate a genome that is root associated, drought resistant, salt tolerant that will produce this natural product”.