Here, we describe the third major release of RELION. CPU-based vector acceleration has been added in addition to GPU support, which provides flexibility in use of resources and avoids memory limitations. Reference-free autopicking with Laplacian-of-Gaussian filtering and execution of jobs from python allows non-interactive processing during acquisition, including 2D-classification, de novo model generation and 3D-classification. Per-particle refinement of CTF parameters and correction of estimated beam tilt provides higher resolution reconstructions when particles are at different heights in the ice, and/or comma-free alignment has not been optimal. Ewald sphere curvature correction improves resolution for large particles. We illustrate these developments with publicly available data sets: together with a Bayesian approach to beam-induced motion correction it leads to resolution improvements of 0.2–0.7 Å compared to previous RELION versions.

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Citation: Jasenko Zivanov, Takanori Nakane, Björn O Forsberg, Dari Kimanius, Wim JH Hagen, Erik Lindahl, Sjors HW Scheres, “New tools for automated high-resolution cryo-EM structure determination in RELION-3,” eLife 2018;7:e42166. doi: 10.7554/eLife.42166

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Citation: Sjors H.W. Scheres, "RELION: Implementation of a Bayesian approach to cryo-EM structure determination", Journal of Structural Biology, Volume 180, Issue 3, 2012, Pages 519-530, ISSN 1047-8477, https://doi.org/10.1016/j.jsb.2012.09.006

November 2017 Intel HPC Developers Conference, SC17, Denver

Structural biology is going through a revolution where cryo-electron microscopy (cryo-EM) now determine 3D structures from thousands of noisy images, but it relies on very large computations. This talk presents our work to accelerate the RELION program with x86 SIMD, Intel® Threading Building Blocks, and Intel® Math Kernel Library to provide outstanding performance.

Speakers: Erik Lindahl, Stockholm University; Charles Congdon, Intel Corporation

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Intel® SSF solutions together with optimized software provide an effective cryo-EM platform that will help scientists achieve scalable results faster, maximize the use of available infrastructure, and enable them to achieve new breakthroughs in 3D protein modeling.

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“Performance improvements of this magnitude change the computing paradigm,” said Lindahl. “Results are available in a fraction of the time. This means the application can now make efficient use of large amounts of system memory, which opens up new avenues of research into larger and more complex molecules.”

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