Advances in Modern Information Technologies for Data Analysis in CRYO-EM and XFEL Experiments

Bobkov, Sergey; Teslyuk, Anton; Baymukhametov, Timur; Pichkur, Evgeny; Chesnokov, Yu. M.; Assalauova, Dameli; Poyda, Alexey; Novikov, Alexander M.; Zolotarev, S. I.; Ikonnikova, K. A.; Velikhov, Vasily; Vartanyants, Ivan A.; Vasiliev, A. L.; Ilyin, V. A.

doi:10.1134/s1063774520060085

Cited by 2 publications

(5 citation statements)

References 46 publications

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“…To account for the specifics of the experiment, additional steps are included in the data analysis procedure (Rose et al, 2018;Bobkov et al, 2020;Assalauova et al, 2020). First, due to the fact that only a small fraction of the images contain diffraction patterns, empty images are filtered out from the analysis.…”

Section: Data Analysis Pipeline Of Spi Experiments To Obtain the Spat...mentioning

confidence: 99%

“…Processing the experimental SPI data according to the pipeline allowed for a significant improvement in the structure recovery (Rose et al, 2018;Assalauova et al, 2020). Software was developed to implement all the stages of the pipeline, and a platform for automated data processing of SPI XFEL experiments was created (Bobkov et al, 2020). The platform includes containerized software that is integrated into a software pipeline, which provides automatic processing from experimental data to structure reconstruction.…”

Section: Data Analysis Pipeline Of Spi Experiments To Obtain the Spat...mentioning

confidence: 99%

“…Docker (Merkel, 2014)], as well as in computing clusters run by Kubernetes (https://kubernetes.io/). Information about the developed software (Bobkov et al, 2020) and the automatic data-processing platform is publicly available (https:// gitlab.com/spi_xfel).…”

Section: Data Analysis Pipeline Of Spi Experiments To Obtain the Spat...mentioning

confidence: 99%

“…Various experimental conditions are discussed in detail including the incident photon flux on the sample and the sample-to-detector distance. This work also presents a general data analysis pipeline and the existing structure reconstruction platform (Bobkov et al, 2020). In this structure determination, a priori knowledge of the simulated virus orientations was used and the experimental background signal was neglected.…”

Section: Introductionmentioning

confidence: 99%

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The structure of tick-borne encephalitis virus determined at X-ray free-electron lasers. Simulations

Assalauova

Vartanyants

2023

J Synchrotron Radiat

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The study of virus structures by X-ray free-electron lasers (XFELs) has attracted increased attention in recent decades. Such experiments are based on the collection of 2D diffraction patterns measured at the detector following the application of femtosecond X-ray pulses to biological samples. To prepare an experiment at the European XFEL, the diffraction data for the tick-borne encephalitis virus (TBEV) was simulated with different parameters and the optimal values were identified. Following the necessary steps of a well established data-processing pipeline, the structure of TBEV was obtained. In the structure determination presented, a priori knowledge of the simulated virus orientations was used. The efficiency of the proposed pipeline was demonstrated.

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Section: Data Analysis Pipeline Of Spi Experiments To Obtain the Spat...mentioning

confidence: 99%

Section: Data Analysis Pipeline Of Spi Experiments To Obtain the Spat...mentioning

confidence: 99%

Section: Data Analysis Pipeline Of Spi Experiments To Obtain the Spat...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The structure of tick-borne encephalitis virus determined at X-ray free-electron lasers. Simulations

Assalauova

Vartanyants

2023

J Synchrotron Radiat

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“…The experimental data sets used in this publication are publicly available: https://www.cxidb.org/id-156.html. They were preprocessed (background correction, center estimation) as described by Bobkov et al (2020) using the code available at https://gitlab.com/spi_xfel (see spi_processing section). For convenience, the preprocessed data are also available at https://zenodo.org/record/6451444 (Assalauova et al, 2022).…”

Section: Data and Code Availabilitymentioning

confidence: 99%

Classification of diffraction patterns using a convolutional neural network in single-particle-imaging experiments performed at X-ray free-electron lasers

Assalauova¹,

Ignatenko²,

Isensee³

et al. 2022

J Appl Cryst

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Single particle imaging (SPI) at X-ray free-electron lasers is particularly well suited to determining the 3D structure of particles at room temperature. For a successful reconstruction, diffraction patterns originating from a single hit must be isolated from a large number of acquired patterns. It is proposed that this task could be formulated as an image-classification problem and solved using convolutional neural network (CNN) architectures. Two CNN configurations are developed: one that maximizes the F1 score and one that emphasizes high recall. The CNNs are also combined with expectation-maximization (EM) selection as well as size filtering. It is observed that the CNN selections have lower contrast in power spectral density functions relative to the EM selection used in previous work. However, the reconstruction of the CNN-based selections gives similar results. Introducing CNNs into SPI experiments allows the reconstruction pipeline to be streamlined, enables researchers to classify patterns on the fly, and, as a consequence, enables them to tightly control the duration of their experiments. Incorporating non-standard artificial-intelligence-based solutions into an existing SPI analysis workflow may be beneficial for the future development of SPI experiments.

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Advances in Modern Information Technologies for Data Analysis in CRYO-EM and XFEL Experiments

Cited by 2 publications

References 46 publications

The structure of tick-borne encephalitis virus determined at X-ray free-electron lasers. Simulations

The structure of tick-borne encephalitis virus determined at X-ray free-electron lasers. Simulations

Classification of diffraction patterns using a convolutional neural network in single-particle-imaging experiments performed at X-ray free-electron lasers

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