CryoSPARC (Cryo-EM Single Particle Ab-Initio Reconstruction and Classification) is a state of the art HPC software solution for complete processing of single-particle cryo-electron microscopy (cryo-EM) data. CryoSPARC is useful for solving cryo-EM structures of membrane proteins, viruses, complexes, flexible molecules, small particles, phase plate data and negative stain data.
CryoSPARC Live is a software platform that enables:
Real-time cryo-EM data quality assessment
Decision making based on 2D and 3D results during live data collection
An expedited, streamlined workflow for processing previously collected data
Direct seamless interoperation with cryoSPARC for advanced processing
Real-time cryo-EM data quality assessment and decision making during live data collection, as well as an expedited, streamlined workflow for processing already available data. See: About cryoSPARC Live
Ultra-fast end-to-end processing of raw cryo-EM data and reconstruction of 3D maps, ready for ingestion into model building software
Optimized algorithms and GPU acceleration at all stages, from pre-processing through particle picking, 2D particle classification, 3D ab-initio structure determination, high resolution refinement, and heterogeneity analysis
Specialized and unique tools for therapeutically relevant targets, membrane proteins, continuously flexible structures
Interactive, visual and iterative experimentation for even the most complex workflows
New manuscripts describing the expanded workflow in cryoSPARC and in cryoSPARC Live are in preparation. Currently, please cite the following papers as appropriate:
General cryoSPARC/cryoSPARC Live use, including preprocessing, 2D classification, Ab-initio reconstruction, Refinement: Punjani, A., Rubinstein, J.L., Fleet, D.J. & Brubaker, M.A. cryoSPARC: algorithms for rapid unsupervised cryo-EM structure determination. Nature Methods 14, 290-296 (2017).
CTF refinement and aberration correction: Zivanov, J., Nakane, T. & Scheres, S. H. W. Estimation of high-order aberrations and anisotropic magnification from cryo-EM data sets in RELION-3.1. IUCrJ 7, 253-267 (2020).
MotionCor2: Shawn Q. Zheng, Eugene Palovcak, Jean-Paul Armache, Yifan Cheng and David A. Agard (2016) Anisotropic Correction of Beam-induced Motion for Improved Single-particle Electron Cryo-microscopy, Nature Methods, submitted. BioArxiv: http://biorxiv.org/content/early/2016/07/04/061960
Gctf: Gctf: Jack (Kai) Zhang. Zhang, K. (2016). Gctf : Real-time CTF determination and correction. Journal of Structural Biology, 193(1), 1-12. https://doi.org/10.1016/j.jsb.2015.11.003
Topaz: Bepler, T., Morin, A., Rapp, M. et al. Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs. Nat Methods 16, 1153–1160 (2019) doi:10.1038/s41592-019-0575-8 and Bepler, T., Noble, A.J., Berger, B. Topaz-Denoise: general deep denoising models for cryoEM. bioRxiv 838920 (2019) doi: https://doi.org/10.1101/838920
cuDNN: libcudnn.so.8 is distributed with cryoSPARC as of v3.2, pursuant to the terms of NVIDIA's Software License Agreement (SLA) for cuDNN: https://docs.nvidia.com/deeplearning/cudnn/sla/index.html
scikit-cuda: A modified version of scikit-cuda is included with cryosparc_compute as of v3.2, pursuant to the scikit-cuda license terms: https://scikit-cuda.readthedocs.io/en/latest/
Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. Neither the name of Lev E. Givon nor the names of any contributors may be used to endorse or promote products derived from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS” AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
Hundreds of structural studies have used cryoSPARC for cryo-EM data processing:
CryoSPARC was originally a research project with origins at the University of Toronto in 2014. As of 2016, all research and development for cryoSPARC is done by Structura Biotechnology Inc., a scientific software startup based in Toronto, Canada. By combining our expertise in image processing, algorithm development and professional software engineering, we aim to keep cryoSPARC at the forefront of software for cryo-EM. To that end, we are constantly working on new algorithms and software features which we release on an ongoing basis. CryoSPARC's GPU-accelerated code is written entirely from scratch in-house, with exception of certain wrappers to third party tools that are clearly indicated in the documentation. Many of the algorithms in cryoSPARC are novel developments for cryo-EM image processing and links to publications can be found throughout this documentation.
CryoSPARC v3.0 was released on December 9, 2020 and has been followed by subsequent version v3.1 and v3.2. For release notes, see: https://cryosparc.com/updates
CryoSPARC v2.0 (released August 17, 2018) was followed by a number of new releases up to v2.15.0 (released May 13, 2020).
CryoSPARC v0.2.1 was the first public version of cryoSPARC (released February 7, 2017) and was followed by a number of new releases up to v0.6.5 (released January 12, 2018).