ArticleNature methods2023
TomoTwin: generalized 3D localization of macromolecules in cryo-electron tomograms with structural data mining.
Article in Nature methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.
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Who cites it
55 citing papers in PubMed, 87 citations in OpenAlex.
- MemBrain v2: an end-to-end tool for the analysis of membranes in cryo-electron tomography.Nature methods · 2026Article
- Graph identification of proteins in tomograms (GRIP-Tomo) 2.0: Topologically aware classification for proteins.Protein science : a publication of the Protein Society · 2026Article
- Lessons learned from a Kaggle challenge for particle picking in cryo-electron tomography.Nature methods · 2026Article
- Integrating in situ single-particle cryo-electron microscopy with cryo-electron tomography for high-resolution structural biology in native cellular contexts.Current opinion in structural biology · 2026Review
- Towards a systems-level view of the microtubule cytoskeleton and its functions in physiology and disease.Nature reviews. Molecular cell biology · 2026Review
- CoCryoViS: Collaborative online cryo-electron tomography visualization system.Protein science : a publication of the Protein Society · 2026Article
- Applications and prospects of cryo-electron tomography in drug discovery and understanding disease.Current opinion in structural biology · 2026Review
- TomoSwin3D: a Swin3D Transformer for the Identification and Classification of Macromolecules in 3D Cryo-ET Tomograms.bioRxiv : the preprint server for biology · 2026Article
- Cryo-FIB-enabled 3D imaging of biological specimens.Applied microscopy · 2026Review
- Fully genetically encoded low-molecular-weight protein tags with defined shapes for direct molecular identification by cryo-electron tomography.bioRxiv : the preprint server for biology · 2026Article
- MVGFormer: Multi-view perspective with graph-guided transformer for cryo-ET segmentation.Knowledge-based systems · 2026Article
- ExoSloNano: multimodal nanogold labels for identification of macromolecules in live cells and cryo-electron tomograms.Nature methods · 2026Article
- Mechanisms of COPII coat assembly and cargo recognition in the secretory pathway.Nature reviews. Molecular cell biology · 2025Review
- Article
- PickET: An unsupervised method for localizing macromolecules in cryo-electron tomograms.Research square · 2025Article
- cryoTIGER: deep-learning based tilt interpolation generator for enhanced reconstruction in cryo electron tomography.Communications biology · 2025Article
- Template Learning: Deep learning with domain randomization for particle picking in cryo-electron tomography.Nature communications · 2025Article
- SynapseNet: Deep learning for automatic synapse reconstruction.Molecular biology of the cell · 2025Article
- The synaptic vesicle cluster as a controller of pre- and postsynaptic structure and function.The Journal of physiology · 2025Review
- A realistic phantom dataset for benchmarking cryo-ET data annotation.Nature methods · 2025Article
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cryogenic-electron tomography enables the visualization of cellular environments in extreme detail, however, tools to analyze the full amount of information contained within these densely packed volumes are still needed. Detailed analysis of macromolecules through subtomogram averaging requires particles to first be localized within the tomogram volume, a task complicated by several factors including a low signal to noise ratio and crowding of the cellular space. Available methods for this task suffer either from being error prone or requiring manual annotation of training data. To assist in this crucial particle picking step, we present TomoTwin: an open source general picking model for cryogenic-electron tomograms based on deep metric learning. By embedding tomograms in an information-rich, high-dimensional space that separates macromolecules according to their three-dimensional structure, TomoTwin allows users to identify proteins in tomograms de novo without manually creating training data or retraining the network to locate new proteins.
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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.