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

Abstract

Determining the cell type-specific and genome-wide binding locations of transcription factors (TFs) is an important step towards decoding gene regulatory programs. Profiling by the assay for transposase-accessible chromatin using sequencing (ATAC-seq) reveals open chromatin sites that are potential binding sites for TFs but does not identify which TFs occupy a given site. 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. Our approach automatically learns distinct groups of k-mer patterns that correspond to cell type-specific in vivo binding signals. Latent factors found by BindVAE generally map to TFs that are expressed in the input cell type. BindVAE finds different TF binding sites in different cell types and can learn composite patterns for TFs involved in co-operative binding. BindVAE therefore provides a novel unsupervised approach to deconvolve the complex TF binding signals in chromatin accessible sites.