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<p>High-throughput data processing is necessary to realize the full potential of cryo-electron tomography and subtomogram averaging. The field’s fragmented software landscape remains a considerable hurdle to this end. Here we present AreTomoLive, an automated preprocessing pipeline composed of two GPU-accelerated packages. The first, AreTomo3, streamlines tomographic alignment and reconstruction, with new features to fully account for sample geometry and locally correct the contrast transfer function. The second package, DenoisET, leverages AreTomo3’s comprehensively corrected tomograms to retain more intermediate-resolution features during contrast enhancement. Collectively, this pipeline prioritizes automation to support data preprocessing concurrent with data collection at scale.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月25日

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  • 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 A. Greenan,
  • Shu-Hsien Sheu,
  • Elizabeth A. Montabana,
  • Bridget Carragher,
  • Clinton S. Potter,
  • David A. Agard,
  • Shawn Zheng

摘要

High-throughput data processing is necessary to realize the full potential of cryo-electron tomography and subtomogram averaging. The field’s fragmented software landscape remains a considerable hurdle to this end. Here we present AreTomoLive, an automated preprocessing pipeline composed of two GPU-accelerated packages. The first, AreTomo3, streamlines tomographic alignment and reconstruction, with new features to fully account for sample geometry and locally correct the contrast transfer function. The second package, DenoisET, leverages AreTomo3’s comprehensively corrected tomograms to retain more intermediate-resolution features during contrast enhancement. Collectively, this pipeline prioritizes automation to support data preprocessing concurrent with data collection at scale.