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bioRxiv · 10.1101/677542

MicrographCleaner: a python package for cryo-EM micrograph segmentation using deep learning

Sanchez-Garcia, R.·Segura, J.·Maluenda, D.·Sorzano, C. O. S.·Carazo, J. M.
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Abstract

Cryo-EM Single Particle Analysis workflows require from 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, hurdling 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 which regions of micrographs are suitable for particle picking and which are not in an automatic fashion. 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 about 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.

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Sanchez-Garcia, R., Segura, J., Maluenda, D., Sorzano, C. O. S., Carazo, J. M.. 2019-06-21. MicrographCleaner: a python package for cryo-EM micrograph segmentation using deep learning. https://doi.org/10.1101/677542

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