MicrographCleaner: A python package for cryo-EM micrograph cleaning using deep learning.
J Struct Biol
; 210(3): 107498, 2020 06 01.
Article
en En
| MEDLINE
| ID: mdl-32276087
ABSTRACT
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 preprocessing. 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.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Microscopía por Crioelectrón
/
Aprendizaje Profundo
Idioma:
En
Revista:
J Struct Biol
Asunto de la revista:
BIOLOGIA MOLECULAR
Año:
2020
Tipo del documento:
Article