ArticleProtein science : a publication of the Protein Society2026
CoCryoViS: Collaborative online cryo-electron tomography visualization system.
Article in Protein science : a publication of the Protein Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
1 citing paper in PubMed.
- CoCryoViS: Collaborative online cryo-electron tomography visualization system.Protein science : a publication of the Protein Society · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
We present a collaborative, interactive, web-based visualization and processing system for Cryo-Electron Tomography (cryo-ET) data that supports the complete workflow, from raw tilt-series projections to final 3D segmentations. The system integrates tilt-series alignment, motion correction, tomographic reconstruction, manual and pseudo-annotation, deep model training, inference, and visualization, all accessible through a unified web-based interface. The backend manages computationally intensive tasks, including advanced tomographic reconstruction, pseudo-annotation generation, deep learning model training, and large-scale segmentation of new datasets. It features separate queues for CPU and GPU tasks, allowing users to prepare and manage large workloads in advance. The frontend supports lightweight tomographic reconstruction, manual data annotation, segmentation with pretrained models, interactive exploration of pipeline stages, and real-time monitoring of queued tasks. Key processing steps are available on both the client and server sides, providing flexibility for diverse computational environments, enabling visualization of externally hosted datasets, and supporting data privacy when required. The system is designed for collaborative workflows, enabling multiple users to share input data, annotations, and trained models within joint projects. Its modular architecture allows easy extension with additional stages on either the client or server side, making it adaptable to specialized pipelines within individual institutions. We demonstrate the system's capabilities on representative cryo-ET datasets, highlighting its utility for both individual researchers and distributed teams working on structural analysis of complex cellular environments. The initial release of the system is available at https://cocryovis.lgm.fri.uni-lj.si/ (Project repository: https://github.com/nanovis/cocryovis, mirror deploy: https://tomography.kaust.edu.sa/), and the entire system will become open source so that any visualization or domain researcher can further enhance its functionality.
Indexed as
Identifiers
What OpenQuestion holds
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.