Computational Methods Toward Unbiased Pattern Mining and Structure Determination in Cryo-Electron Tomography Data

Journal of Molecular Biology(2023)

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Cryo-electron tomography can uniquely probe the native cellular environment for macromolecular struc-tures. Tomograms feature complex data with densities of diverse, densely crowded macromolecular com-plexes, low signal-to-noise, and artifacts such as the missing wedge effect. Post-processing of this data generally involves isolating regions or particles of interest from tomograms, organizing them into related groups, and rendering final structures through subtomogram averaging. Template-matching and reference-based structure determination are popular analysis methods but are vulnerable to biases and can often require significant user input. Most importantly, these approaches cannot identify novel com-plexes that reside within the imaged cellular environment. To reliably extract and resolve structures of interest, efficient and unbiased approaches are therefore of great value. This review highlights notable computational software and discusses how they contribute to making automated structural pattern discov-ery a possibility. Perspectives emphasizing the importance of features for user-friendliness and accessi-bility are also presented. (c) 2023 Elsevier Ltd. All rights reserved.
Cryo-ET,image pattern recognition,data mining,machine learning,cellular structural biology
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