BindVAE: Dirichlet variational autoencoders for de novo motif discovery from accessible chromatin

Abstract

We present a novel unsupervised deep learning approach called BindVAE, based on Dirichlet variational autoencoders, for jointly decoding multiple TF binding signals from open chromatin regions. BindVAE can disentangle an input DNA sequence into distinct latent factors that encode cell-type specific in vivo binding signals for individual TFs, composite patterns for TFs involved in cooperative binding, and genomic context surrounding the binding sites. For the task of retrieving motifs of expressed TFs for a given cell type, we find that BindVAE has a higher precision, albeit lower recall, compared to other motif discovery approaches.