Denoising Autoencoder Trained on Simulation-Derived Structures for Noise Reduction in Chromatin Scanning Transmission Electron Microscopy.

ABSTRACT: Scanning transmission electron microscopy tomography with ChromEM staining (ChromSTEM), has allowed for the three-dimensional study of genome organization. By leveraging convolutional neural networks and molecular dynamics simulations, we have developed a denoising autoencoder (DAE) capable of postprocessing experimental ChromSTEM images to provide nucleosome-level resolution. Our DAE is trained on synthetic images generated from simulations of the chromatin fiber using the 1-cylinder per nucleosome (1CPN) model of chromatin. We find that our DAE is capable of removing noise commonly found in high-angle annular dark field (HAADF) STEM experiments and is able to learn structural features driven by the physics of chromatin folding. The DAE outperforms other well-known denoising algorithms wi

SUBMITTER: Alvarado W 

PROVIDER: S-EPMC10311656 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature