Journal
JOURNAL OF STRUCTURAL BIOLOGY
Volume 210, Issue 3, Pages -Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jsb.2020.107498
Keywords
Cryo-EM; Deep learning; Micrographs; Cleaning; Carbon; Contaminants
Funding
- Spanish Ministry of Economy and Competitiveness [BIO2016-76400-R]
- Ministry of Education of Spain [S2017/BMD-3817]
- FPU fellowship
- Comunidad Autonoma de Madrid [S2017/BMD-3817]
- Instruct-ERIC, a Landmark ESFRI project
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Cryo-EM Single Particle Analysis workflows require tens of thousands of high-quality particle projections to unveil the three-dimensional structure of macromolecules. Conventional methods for automatic particle picking tend to suffer from high false-positive rates, hampering the reconstruction process. One common cause of this problem is the presence of carbon and different types of high-contrast contaminations. In order to overcome this limitation, we have developed MicrographCleaner, a deep learning package designed to discriminate, in an automated fashion, between regions of micrographs which are suitable for particle picking, and those which are not. MicrographCleaner implements a U-net-like deep learning model trained on a manually curated dataset compiled from over five hundred micrographs. The benchmarking, carried out on approximately one hundred independent micrographs, shows that MicrographCleaner is a very efficient approach for micrograph pre-processing. MicrographCleaner (micrograph_cleaner_em) package is available at PyPI and Anaconda Cloud and also as a Scipion/Xmipp protocol. Source code is available at https://github.com/rsanchezgarc/micrograph_cleaner_em.
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