Deep Consensus, a deep learning-based approach for particle pruning in cryo-electron microscopy.

ABSTRACT: Single-particle cryo-electron microscopy (cryo-EM) has recently become a mainstream technique for the structural determination of macromolecules. Typical cryo-EM workflows collect hundreds of thousands of single-particle projections from thousands of micrographs using particle-picking algorithms. However, the number of false positives selected by these algorithms is large, so that a number of different 'cleaning steps' are necessary to decrease the false-positive ratio. Most commonly employed techniques for the pruning of false-positive particles are time-consuming and require user intervention. In order to overcome these limitations, a deep learning-based algorithm named Deep Consensus is presented in this work. Deep Consensus works by computing a smart consensus over the ou

SUBMITTER: Sanchez-Garcia R 

PROVIDER: S-EPMC6211526 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature