ICON: 3D reconstruction with ‘missing-information’ restoration in biological electron tomography

Deng, Yuchen; Yu, Changlin; Zhang, Yan; Wang, Shengliu; Zhang, Fa; Sun, Fei

doi:10.1016/j.jsb.2016.04.004

Cited by 84 publications

(84 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…2016). Here, the −0.29° tilt (the minimum tilt) projection was excluded as the omit-projection (“ground truth”) (Fig.…”

Section: Resultsmentioning

confidence: 99%

“…2016):Step 1Pre-processing. Align tilt series and correct contrast transfer function (CTF).Step 2Gray value adjustment.…”

Section: Methodsmentioning

confidence: 99%

“…proposed iterative compressed-sensing optimized NUFFT reconstruction (ICON) by combining CS and NUFFT together (Deng et al . 2016). With a validation procedure, ICON not only restores the missing information but also measures the fidelity of the information restoration.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Accelerating electron tomography reconstruction algorithm ICON with GPU

et al. 2017

Self Cite

View full text Add to dashboard Cite

Electron tomography (ET) plays an important role in studying in situ cell ultrastructure in three-dimensional space. Due to limited tilt angles, ET reconstruction always suffers from the “missing wedge” problem. With a validation procedure, iterative compressed-sensing optimized NUFFT reconstruction (ICON) demonstrates its power in the restoration of validated missing information for low SNR biological ET dataset. However, the huge computational demand has become a major problem for the application of ICON. In this work, we analyzed the framework of ICON and classified the operations of major steps of ICON reconstruction into three types. Accordingly, we designed parallel strategies and implemented them on graphics processing units (GPU) to generate a parallel program ICON-GPU. With high accuracy, ICON-GPU has a great acceleration compared to its CPU version, up to 83.7×, greatly relieving ICON’s dependence on computing resource.

show abstract

“…2016). Here, the −0.29° tilt (the minimum tilt) projection was excluded as the omit-projection (“ground truth”) (Fig.…”

Section: Resultsmentioning

confidence: 99%

“…2016):Step 1Pre-processing. Align tilt series and correct contrast transfer function (CTF).Step 2Gray value adjustment.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Accelerating electron tomography reconstruction algorithm ICON with GPU

et al. 2017

Self Cite

View full text Add to dashboard Cite

show abstract

“…To improve the contrast and quality of ET reconstruction, they developed three different reconstruction algorithms: ASART, 23 FIRT, 24 and ICON. 25 Notably, ICON combines both nonuniform fast Fourier transformation and compressed sensing techniques and significantly recovers the missing information in both the angular intervals and the missing wedge.…”

Section: Em Methodology and Technologymentioning

confidence: 99%

Biological cryo‐electron microscopy in China

2016

View full text Add to dashboard Cite

Cryo‐electron microscopy (cryo‐EM) plays an increasingly more important role in structural biology. With the construction of an arm of the Chinese National Protein Science Facility at Tsinghua University, biological cryo‐EM has entered a phase of rapid development in China. This article briefly reviews the history of biological cryo‐EM in China, describes its current status, comments on its impact on the various biological research fields, and presents future outlook.

show abstract

“…Variations of this method have also been demonstrated for cryoET specimens (Aganj et al 2007; Song et al 2012). Recently, the iterative compressed-sensing optimized non-uniform (ICON) reconstruction (Deng et al 2016) method demonstrated that CS can restore missing information in noisy cryoET data of both cells and isolated macromolecules, thereby minimizing missing wedge artifacts and yielding measurably better reconstructions than WBP.…”

Section: Challenges In Single Particle Tomographymentioning

confidence: 99%

The advent of structural biology in situ by single particle cryo-electron tomography

Galaz-Montoya

Ludtke

2017

Biophys Rep

View full text Add to dashboard Cite

Single particle tomography (SPT), also known as subtomogram averaging, is a powerful technique uniquely poised to address questions in structural biology that are not amenable to more traditional approaches like X-ray crystallography, nuclear magnetic resonance, and conventional cryoEM single particle analysis. Owing to its potential for in situ structural biology at subnanometer resolution, SPT has been gaining enormous momentum in the last five years and is becoming a prominent, widely used technique. This method can be applied to unambiguously determine the structures of macromolecular complexes that exhibit compositional and conformational heterogeneity, both in vitro and in situ. Here we review the development of SPT, highlighting its applications and identifying areas of ongoing development.

show abstract

ICON: 3D reconstruction with ‘missing-information’ restoration in biological electron tomography

Cited by 84 publications

References 40 publications

Accelerating electron tomography reconstruction algorithm ICON with GPU

Accelerating electron tomography reconstruction algorithm ICON with GPU

Biological cryo‐electron microscopy in China

The advent of structural biology in situ by single particle cryo-electron tomography

Contact Info

Product

Resources

About