Ariana Peck, Yue Yu, Mohammadreza Paraan, Dari Kimanius, Utz H. Ermel, Joshua Hutchings, Jonathan Schwartz, Daniel Serwas, Hannah Siems, Norbert S. Hill, Mallak Ali, Julia Peukes, Garrett Greenan, Shu‐Hsien Sheu, Elizabeth Montabana, Bridget Carragher, Clinton S. Potter, David A. Agard, Shawn Zheng
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.