https://3demmethods.i2pc.es/index.php?action=history&feed=atom&title=2020Sanchez_Cleaner 2026-05-24T20:15:39Z Revision history for this page on the wiki MediaWiki 1.44.2 https://3demmethods.i2pc.es/index.php?title=2020Sanchez_Cleaner&diff=3721&oldid=prev 2020-07-06T07:40:07Z

<p>Created page with &quot;== Citation == Sanchez-Garcia, R.; Segura, J.; Maluenda, D.; Sorzano, C. O. S., Carazo, J. M. MicrographCleaner: A python package for cryo-EM micrograph cleaning using deep...&quot;</p> <p><b>New page</b></p><div>== Citation ==<br /> <br /> Sanchez-Garcia, R.; Segura, J.; Maluenda, D.; Sorzano, C. O. S., Carazo, J. M. MicrographCleaner: A python package for cryo-EM micrograph cleaning using deep learning. J. Structural Biology, 2020, 210, 107498 <br /> <br /> == Abstract ==<br /> <br /> 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. <br /> <br /> == Keywords ==<br /> <br /> == Links ==<br /> <br /> https://www.sciencedirect.com/science/article/pii/S1047847720300642<br /> <br /> == Related software ==<br /> <br /> == Related methods ==<br /> <br /> == Comments ==</div>

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