Characterizing chromatin folding coordinate and landscape with deep learning.

ABSTRACT: Genome organization is critical for setting up the spatial environment of gene transcription, and substantial progress has been made towards its high-resolution characterization. The underlying molecular mechanism for its establishment is much less understood. We applied a deep-learning approach, variational autoencoder (VAE), to analyze the fluctuation and heterogeneity of chromatin structures revealed by single-cell imaging and to identify a reaction coordinate for chromatin folding. This coordinate connects the seemingly random structures observed in individual cohesin-depleted cells as intermediate states along a folding pathway that leads to the formation of topologically associating domains (TAD). We showed that folding into wild-type-like structures remain energetically favorable in

SUBMITTER: Xie WJ 

PROVIDER: S-EPMC7544120 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature