Hostname: page-component-586b7cd67f-rcrh6 Total loading time: 0 Render date: 2024-11-29T01:44:14.336Z Has data issue: false hasContentIssue false

Application of a SQL Database for Automated Image Acquisition and Analysis for CRYOEM

Published online by Cambridge University Press:  02 July 2020

D. Fellmann
Affiliation:
Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801
B. Carragher
Affiliation:
Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801
C.S. Potter
Affiliation:
Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801
J. Pulokas
Affiliation:
Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801
Get access

Abstract

We have been developing a software system, called Leginon, for the control and acquisition of images from a transmission electron microscope. This software allows for the automatic acquisition of images under low dose conditions from a specimen embedded in vitreous ice. Thousands of images are collected during each session at the microscope. These images are collected at various magnifications and under a variety of conditions. For example, images are collected of the entire grid, individual grid squares, individual holes within the square, and several high magnification images are acquired for one to several targets within each hole (figure 1). in addition, for each identified hole an image is acquired at the low dose focus position. Power spectra are also calculated for each of the high magnification images as well as for the focus images. For managing these images we are using a relational database management system. We have also developed a bench of tools to store, access, and display the data as well as tools for extracting information from the database.

A database is a structured collection of data in which the information is classified by categories. Each category is then stored in a table and these tables are linked together by defined relations. Data may be combined from several tables on request. We have been using MySQL as our relational database management system. This allows us both to manage the images and to refer each image to its real context. in other words, through the database, we can keep track of information related to the microscope settings (e.g. defocus, electron dose, magnification, goniometer coordinates etc.), the type of image, and overall information about the specimen and the experiment.

Type
Instrument Automation (Organized by W. Deruijter and C. Potter)
Copyright
Copyright © Microscopy Society of America 2001

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Potter, C.S., et al. (1999) Ultramicroscopy, 77, 153161.CrossRefGoogle Scholar
2.Carragher, et al. (2000) JSB. 132, 3345.Google Scholar
4. This research by the National Science Foundation (DBI-9730056, DBI-9904547) and the National Institutes of Health (GM61939-01).Google Scholar