ReviewJournal of molecular biology2023
Computational Methods Toward Unbiased Pattern Mining and Structure Determination in Cryo-Electron Tomography Data.
Review in Journal of molecular biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Localization of macromolecules in crowded cellular cryo-electron tomograms from extremely sparse labels.Briefings in bioinformatics · 2025Article
- PickET: An unsupervised method for localizing macromolecules in cryo-electron tomograms.Research square · 2025Article
- PickET: An unsupervised method for localizing macromolecules in cryo-electron tomograms.bioRxiv : the preprint server for biology · 2025Article
- TomoPicker: Annotation-Efficient Particle Picking in cryo-electron Tomograms.bioRxiv : the preprint server for biology · 2024Article
- Bridging structural biology and clinical research through in-tissue cryo-electron tomography.The EMBO journal · 2024Article
- Combining per-particle cryo-ET and cryo-EM single particle analysis to elucidate heterogeneous DNA-protein organization.Current opinion in structural biology · 2024Review
- Stepwise assembly and release of Tc toxins from Yersinia entomophaga.Nature microbiology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.