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

[Display omitted] •Cryo-ET reveals intracellular structures in situ and can form visual atlases to map the molecular sociology of a cell.•Analysis software often offer template matching or manual annotation, but these cannot robustly determine structures de novo.•Recent de novo structure pattern mining efforts focus on template-free particle picking and unbiased structure determination.•Design choices and data augmentation can increase the accessibility and performance of computational methods.•Standardized benchmarks are critical to objectively validate new methods and assess the current state of these technologies. Cryo-electron tomography can uniquely probe the native cellular environment for macromolecular structures. Tomograms feature complex data with densities of diverse, densely crowded macromolecular complexes, 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 complexes 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 discovery a possibility. Perspectives emphasizing the importance of features for user-friendliness and accessibility are also presented.