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Peer-reviewedCC BYNature Methods

AreTomoLive: automated reconstruction of comprehensively corrected and denoised cryo-electron tomograms in real time and at high throughput

  • Ariana Peck1
  • Yue Eric Yu1
  • Mohammadreza Paraan1
  • Dari Kimanius1
  • Utz H. Ermel1
  • Joshua Hutchings1
  • Jonathan Schwartz1
  • Daniel Serwas1
  • Hannah Siems1
  • Norbert S. Hill1
  • Mallak Ali1
  • Julia Peukes1
7 more authors
  • Garrett A. Greenan1
  • Shu‐Hsien Sheu1
  • Elizabeth A. Montabana1
  • Bridget O Carragher1
  • Clinton S. Potter1
  • David A. Agard1,2
  • Shawn Q. Zheng3
  1. 1Biohub, Redwood City, CA, USA
  2. 2Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
  3. 3Biohub, Redwood City, CA, USA. [email protected]
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Abstract

A high throughput processing pipeline that performs comprehensive corrections is needed to realize the full potential of cryo-electron tomography and subtomogram averaging. The field’s fragmented software landscape remains a significant hurdle to this end. Here we present AreTomoLive, an automated real-time pipeline composed of two GPU-accelerated packages. The first, AreTomo3, streamlines tomographic alignment and reconstruction, with new features to fully account for sample geometry, locally correct the contrast transfer function, and curate data for downstream tasks. The second package, DenoisET, is a new implementation of the machine learning algorithm Noise2Noise and runs in parallel with AreTomo3 to perform contrast enhancement. To reduce barriers to routine use, AreTomoLive prioritizes automation: AreTomo3 autonomously pauses and reactivates processing depending on the status of data collection, while DenoisET algorithmically determines when to transition from training to inference. AreTomoLive endeavors to advance cryoET for in situ structural analysis with its comprehensive corrections and full automation.

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Cite

BibTeXRIS
BibTeX
@article{peck2026aretomolive,
  title = {{AreTomoLive: automated reconstruction of comprehensively corrected and denoised cryo-electron tomograms in real time and at high throughput}},
  author = {Ariana Peck and Yue Eric Yu and Mohammadreza Paraan and Dari Kimanius and Utz H. Ermel and Joshua Hutchings and Jonathan Schwartz and Daniel Serwas and Hannah Siems and Norbert S. Hill and Mallak Ali and Julia Peukes and Garrett A. Greenan and Shu‐Hsien Sheu and Elizabeth A. Montabana and Bridget O Carragher and Clinton S. Potter and David A. Agard and Shawn Q. Zheng},
  journal = {Nature Methods},
  year = {2026},
  month = may,
  doi = {10.1038/s41592-026-03093-y},
  url = {https://doi.org/10.1038/s41592-026-03093-y},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - AreTomoLive: automated reconstruction of comprehensively corrected and denoised cryo-electron tomograms in real time and at high throughput
AU  - Ariana Peck
AU  - Yue Eric Yu
AU  - Mohammadreza Paraan
AU  - Dari Kimanius
AU  - Utz H. Ermel
AU  - Joshua Hutchings
AU  - Jonathan Schwartz
AU  - Daniel Serwas
AU  - Hannah Siems
AU  - Norbert S. Hill
AU  - Mallak Ali
AU  - Julia Peukes
AU  - Garrett A. Greenan
AU  - Shu‐Hsien Sheu
AU  - Elizabeth A. Montabana
AU  - Bridget O Carragher
AU  - Clinton S. Potter
AU  - David A. Agard
AU  - Shawn Q. Zheng
PY  - 2026
DA  - 2026/05/25
JO  - Nature Methods
DO  - 10.1038/s41592-026-03093-y
UR  - https://doi.org/10.1038/s41592-026-03093-y
AB  - A high throughput processing pipeline that performs comprehensive corrections is needed to realize the full potential of cryo-electron tomography and subtomogram averaging. The field’s fragmented software landscape remains a significant hurdle to this end. Here we present AreTomoLive, an automated real-time pipeline composed of two GPU-accelerated packages. The first, AreTomo3, streamlines tomographic alignment and reconstruction, with new features to fully account for sample geometry, locally correct the contrast transfer function, and curate data for downstream tasks. The second package, DenoisET, is a new implementation of the machine learning algorithm Noise2Noise and runs in parallel with AreTomo3 to perform contrast enhancement. To reduce barriers to routine use, AreTomoLive prioritizes automation: AreTomo3 autonomously pauses and reactivates processing depending on the status of data collection, while DenoisET algorithmically determines when to transition from training to inference. AreTomoLive endeavors to advance cryoET for in situ structural analysis with its comprehensive corrections and full automation.
SN  - 1548-7091
SN  - 1548-7105
ER  -