BindVAE: Dirichlet variational autoencoders for de novo motif discovery from accessible chromatin
2022
Kshirsagar, Meghana | Yuan, Han | Ferres, Juan Lavista | Leslie, Christina
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. On the task of retrieving the motifs of expressed TFs in a given cell type, BindVAE is competitive with existing motif discovery approaches.
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