Speaker
Description
Cryo-EM workflows require from tens of thousands of high-quality particle projections to unveil the three-dimensional structure of macromolecules. Current methods for automatic particle-picking tend to suffer from high false-positive rates, hurdling the reconstruction process. Usually, the failures of one particle-picking algorithm are typically not the failures of another. Therefore, a smart consensus over the output of different particle-picking algorithms, named DeepConsensus is presented in this work. DeepConsensus is based on a deep-convolutional neural network that is trained on a semi-automatically generated data set, resulting in a set of particles with a lower false-positive ratio than the initial sets[1]. However, one common false-positives source for most of the particle-picking algorithms is the presence of carbon and different types of high-contrast contaminations. In order to avoid those areas affected with this kind of contaminants, we have developed a deep-learning approach named MicrographCleaner, designed to discriminate the regions of micrographs suitable for particle picking from those labeled as contaminated[2]. MicrographCleaner implements a U-net-like model trained on a manually curated dataset, compiled from over five hundred micrographs.
[1] Sanchez-Garcia, Ruben et al. IUCrJ vol. 5,Pt 6 854-865. 30 Oct. 2018, doi:10.1107/S2052252518014392
[2] Sanchez-Garcia, Ruben et al. Submitted at BioInformatics, available in BioRxiv doi: https://doi.org/10.1101/677542
