Machine learning-based real-time object locator/evaluator for cryo-EM data collection

In cryo-electron microscopy (cryo-EM) data collection, locating a target object is error-prone. Here, we present a machine learning-based approach with a real-time object locator named yoneoLocr using YOLO, a well-known object detection system. Implementation shows its effectiveness in rapidly and p...

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Bibliographic Details
Published inCommunications biology Vol. 4; no. 1; p. 1044
Main Authors Yonekura, Koji, Maki-Yonekura, Saori, Naitow, Hisashi, Hamaguchi, Tasuku, Takaba, Kiyofumi
Format Journal Article
LanguageEnglish
Published England Nature Publishing Group 07.09.2021
Nature Publishing Group UK
Nature Portfolio
Subjects
Algorithms
Automation
Biology
Cryoelectron Microscopy - instrumentation
Cryoelectron Microscopy - methods
Crystallography
Crystallography, X-Ray - instrumentation
Crystals
Data collection
Data Collection - instrumentation
Electron diffraction
Electron microscopy
Image Processing, Computer-Assisted - instrumentation
Image Processing, Computer-Assisted - methods
Learning algorithms
Machine Learning
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Summary:In cryo-electron microscopy (cryo-EM) data collection, locating a target object is error-prone. Here, we present a machine learning-based approach with a real-time object locator named yoneoLocr using YOLO, a well-known object detection system. Implementation shows its effectiveness in rapidly and precisely locating carbon holes in single particle cryo-EM and in locating crystals and evaluating electron diffraction (ED) patterns in automated cryo-electron crystallography (cryo-EX) data collection. The proposed approach will advance high-throughput and accurate data collection of images and diffraction patterns with minimal human operation.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:2399-3642
2399-3642
DOI:10.1038/s42003-021-02577-1