3 Guide to Jobs and Common Parameters

  1. 3.1 Workflows
  2. 3.2 Import
  3. 3.3 Motion Correction
  4. 3.4 CTF Estimation
  5. 3.5 Exposure Curation
  6. 3.6 Particle Picking
  7. 3.7 Particle Curation
  8. 3.8 3D Reconstruction
  9. 3.9 Refinement
  10. 3.10 Post-processing
  11. 3.11 Utilities
  12. 3.12 Local Refinement Beta

3.1 Workflows

Basic Workflow

Description: Generates an automatic workflow from Import Movies to Refinement. All Jobs in the workflow are automatically queued and will run as soon as the previous job is finished.

Input:

  • One or more raw movies in .mrc, .mrc.bz2 or .tiff format

Common Parameters:

  • You should specify the particle diameter A, Extraction box size, Refinement symmetry if applicable, and Refinement box size (pix) from the Basic Workflow Job Builder

Output:

  • Refined 3D map
  • Half-maps
  • Mask used in refinement
  • Mask used in FSC calculation
  • Plots, including orientation distributions and FSC curves
  • Particle alignments and assignments into classes

Notes: Interactive jobs in the Basic Workflow (e.g., Select 2D classes) will assume 'Waiting' status when ready.

Limitations:

  • Unless you edit the parameters of the Queued Ab-Initio Reconstruction and Refinement jobs, the Basic Workflow will proceed with the assumption that you are interested in one class/working with a homogeneous dataset

Common Next Steps:

  • Additional Basic Workflows, with adjusted parameters
  • Manual iteration

3.2 Import

Import Movies

Description: Import one or more raw movies for processing.

Input:

  • .mrc, .mrc.bz2 or .tiff

Common Parameters:

  • Movies data path: Click on the folder icon to browse or paste the path specifying the location where the movies are stored. To select multiple files, enter a wildcard expression in the browse bar, e.g., *.mrc, which will select all matching file types in the subfolder.
  • Gain reference path:
  • It is best to specify the Raw pixel size (A), Accelerating voltage (kV), Spherical aberration (mm) and Total exposure dose (e/A^2) if known

Output:

  • Imported movies

Common Next Steps:

  • Full frame motion correction to generate micrographs from which particles can be picked manually or using template-based automatic picking

Import Micrographs

Description: Import one or more micrographs for processing.

Input:

  • .mrc

Common Parameters:

  • It is best to specify the Raw pixel size (A), Accelerating voltage (kV), Spherical aberration (mm) and Total exposure dose (e/A^2) if known

Output:

  • Imported micrographs

Common Next Steps:

  • Particle picking

Import Particle Stack

Description: Import a stack of particles with metadata and CTF parameters.

Input:

  • .star, .mrc, .txt, .par, and/or .emx

Common Parameters:

  • It is best to specify the Raw pixel size (A), Accelerating voltage (kV), Spherical aberration (mm) and Total exposure dose (e/A^2) if known

Output:

  • Imported particles

Common Next Steps:

  • 2D Classification to sort and classify particles and remove "junk" particles
  • If the particle stack is already "clean", ab-initio reconstruction can be used to generate one or more initial models which can then be refined

Import 3D Volumes

Description: Import one or more 3D volumes, e.g., half-maps, sharpened or unsharpened maps, local resolution maps, and masks.

Input:

  • .mrc

Common Parameters:

  • Select Type of volume being imported from the drop-down menu: half-map, sharpened map, local resolution map, or mask.
  • You may wish to specify the Pixel size (A) if known

Output:

  • Imported 3D volume

Notes:

  • It is important to select the correct volume type, since you can only use the imported volume in input slots with the correct type.

Limitations:

Common Next Steps:

  • Homogeneous or heterogeneous refinement, using the imported 3D volume as a starting point for one or more classes respectively

Import Templates

Description: Import one or more templates.

Input:

  • .mrc

Common Parameters:

  • Pixel size (A)

Output:

  • Imported Templates

Common Next Steps:

  • Template-based automatic picking

3.3 Motion Correction

Patch-based Motion Correction New

Description: Fast, auto-tuning patch-based local anisotropic motion correction, together with global full-frame motion correction. No need for particle locations.

Input:

  • Raw movies in .mrc, .tiff or mrc.bz2 format

Common Parameters:

  • Only process this many movies: Selects the first n movies to process, instead of processing the entire set. Helpful when working with a set of movies for the first time, to better understand the data quality.

Output:

  • Non-dose weighted and dose-weighted micrographs

Common Next Steps:

  • Patch-based CTF Estimation [New]

Full-frame motion correction

Description: Estimate and correct for full-frame motion (e.g., stage drift) from movie data. Two versions are available - the first runs on a single GPU, while the multi option uses multiple available GPUs to parallelize motion correction.

Input:

  • Raw movies in .mrc or .tiff format

Common Parameters:

  • Only process this many movies: Selects the first n movies to process, instead of processing the entire set. Helpful when working with a set of movies for the first time, to better understand the data quality.

Output:

  • Aligned/motion-corrected micrographs
  • Rigid motion trajectory estimates

Common Next Steps:

  • CTF Estimation

Local motion correction

Description: Estimate and correct for anisotropic local beam-induced motion (per-particle) from movie data and a pre-estimated full-frame motion trajectory. Two versions are available - the first runs on a single GPU, while the multi option uses multiple available GPUs to parallelize processing.

Input:

  • Raw movies
  • Rigid motion trajectory estimates
  • Particle locations

Common Parameters:

  • Extraction box size (pix): This box size should be at least twice the particle width

Output:

  • Local motion-corrected particles
  • Local motion trajectories

Common Next Steps:

  • 2D Classification

MotionCor2 (Wrapper)

Description: An algorithm to correct anisotropic image motion at the single pixel level across the whole frame, suitable for both single particle and tomographic images. Iterative, patch-based motion detection is combined with spatial and temporal constraints and dose weighting.

Input:

  • Raw movies (with microscope parameters & gain reference files imported earlier)

Common Parameters:

  • Path to MotionCor2 executable: The absolute path to the MotionCor2 binary. Note: Run cryosparcm cli 'set_project_param_default(proect_uid, 'exec_param', path)' (where project_uid is your project number (e.g 'P12') and path is the absolute path to the MotionCor2 binary) to keep this parameter consistent across this project.

Output:

  • Global and local (patch-based) motion corrected dose weighted micrographs
  • Global and local (patch-based) motion corrected non-dose weighted micrographs (for CTF Estimation)
  • Global and local (patch-based) motion trajectories

MotionCor2 License Terms

CryoSPARC does not distribute MotionCor2 binaries. Please ensure you have your own copy of MotionCor2 installed under the terms of the MotionCor2 Non-Commercial Software License Agreement available at: https://docs.google.com/forms/d/e/1FAIpQLSfAQm5MA81qTx90W9JL6ClzSrM77tytsvyyHh1ZZWrFByhmfQ/formResponse. 
For-profit users must contact David Agard for licensing information prior to download. Structura Biotechnology Inc. makes no warranty regarding MotionCor2.

Common Next Steps:

  • CTF Estimation (ensure non-dose weighted micrographs are used)

3.4 CTF Estimation

Patch-based CTF Estimation New

Description: Fast, auto-tuning patch-based local CTF estimation. No prior knowledge about tilt, etc., needed. Supports local CTF estimation on difficult cases such as tilt data, deformed ice, etc.

Input:

  • Aligned/motion-corrected micrographs

Common Parameters:

  • Only process this many micrographs: Set a value if you wish to estimate CTF for only a subset of all micrographs, e.g., if you want to build a template for automatic particle picking.

Output:

  • Micrographs with CTF information

Common Next Steps:

  • Particle picking and/or extraction if particle locations are already known

CTFFIND (Wrapper)

Description: Estimate CTF parameters of micrographs using CTFFIND4 4.1.10. CryoSPARC v2 provides a wrapper to CTFFIND4: Alexis Rohou and Nikolaus Grigorieff. CTFFIND4: Fast and accurate defocus estimation from electron micrographs. Journal of Structural Biology, 192(2):216–221, November 2015. Please see the CTFFIND4 License Terms in the Notes.

Input:

  • Aligned/motion-corrected micrographs
  • Gain-corrected Movies

Common Parameters:

  • Only process this many micrographs: Set a value if you wish to estimate CTF for only a subset of all micrographs, e.g., if you want to build a template for automatic particle picking.

Output:

  • Micrographs/Movies with CTF information

Notes:

When using Movies as input, ensure movie frames are gain corrected (cryoSPARC will allow you to import a movie with no gain correction file). Movies connected to CTFFIND which were imported into cryoSPARC with a gain correction file will not work, as gain correction is not baked into the movies after being imported- they are calculated on the fly. Therefore, when you connect these movies to CTFFIND, it will fail to recognize the gain correction.

CTFFIND4 License Terms

The Janelia Research Campus Software License 1.2

Copyright (c) 2018, Howard Hughes Medical Institute, All rights reserved.


Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
2. 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.
3. Neither the name of the Howard Hughes Medical Institute nor the names of its 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, ANY IMPLIED WARRANTIES OF MERCHANTABILITY, NON-INFRINGEMENT, OR 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; REASONABLE ROYALTIES; 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.

Common Next Steps:

  • Particle picking

Gctf (Wrapper)

Description: Estimate CTF parameters of micrographs using Gctf 1.06. CryoSPARC v2 provides a wrapper to 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

Input:

  • Aligned/motion-corrected micrographs

Common Parameters:

  • Only process this many micrographs: Set a value if you wish to estimate CTF for only a subset of all micrographs, e.g., if you want to build a template for automatic particle picking.

Output:

  • Micrographs with CTF information

Common Next Steps:

  • Particle picking

3.5 Exposure Curation

Manually Curate Exposures

Description: Inspect and curate exposures by inspecting individually or via population statistics and thresholds, using motion trajectories, CTF fit values and scores, and particle counts. Detailed tutorial available here.

Input:

  • Exposures (micrographs or movies)
  • Particles (optional)

Common Parameters:

  • None (interactive job)

Output:

  • Subset of the input exposures

Common Next Steps

  • 2D Classification, using the particles associated with exposures that were retained
  • Particle picking

3.6 Particle Picking

Manual Picker

Description: Manually pick particles interactively from micrographs.

Input:

  • Aligned/motion-corrected micrographs

Common Parameters:

  • Once the job is running, you can input and adjust the Box size in the job inspector/stream log. We recommend a box size that is at least double the diameter of the particle.

Output:

  • Particles

Notes: When manually picking particles with the goal of creating a template for auto-picking, we recommend picking particles from multiple micrographs with a range of defocus and CTF fit values to ensure a comprehensive template.

Common Next Steps:

  • 2D Classification and Select 2D to generate templates for Template picking

Template Picker

Description: Auto-pick particles from micrographs using a template generated from 2D classification in cryoSPARC, or from a template imported from elsewhere.

Input:

  • One or more templates (generated in cryoSPARC or elsewhere)
  • Aligned/motion-corrected micrographs

Common Parameters:

  • Particle diameter in Angstrom:
  • Minimum distance between particles:

Output:

  • Particles

Common Next Steps:

  • Inspect picks

Inspect Picks

Description: Inspect and modify picks using various thresholds following auto-picking.

Input:

  • Previously picked particles
  • Aligned/motion-corrected micrographs

Common Parameters:

  • Once the job is running, you can adjust the lowpass filter, normalized cross correlation (NCC) and power threshold sliders to better view the picks and/or remove false positives.

Output:

  • Particle locations

Common Next Steps:

  • Extract particles from micrographs

Extract from Micrographs

Description: Extract picked particles from micrographs using a specified box size.

Input:

  • Aligned/motion-corrected micrographs
  • Particle locations

Common Parameters:

  • Extraction box size:
  • You can set the Number of GPUs parameter to 0 to run the job exclusively on the CPU.

Output:

  • Particles

Notes: Do not extract particles from micrographs if you plan to do local motion correction.

Downsample Particles

Description: You can use this job to downsample your particles and save them with smaller box sizes

Input:

  • Particles

Output:

  • Downsampled Particles

3.7 Particle Curation

2D Classification

Description: Classify particles into multiple 2D classes to facilitate stack cleaning and removal of junk particles. Also useful as a sanity check to investigate particle quality.

Input:

  • Particles

Common Parameters:

  • Number of 2D Classes: In a typical dataset comprising hundreds of thousands of particles, the Number of 2D Classes is typically set between 50 and 200, or even as high as 300 classes. In general, as the Number of 2D Classes increases, the likelihood of finding "junk" classes also increases because "good" classes will become visually more obvious. With too few classes, "junk" particles may be hidden within what otherwise looks like a "good" class.
  • Initial classification uncertainty factor: The Initial Classification Uncertainty Factor (ICUF) tries to capture the user's knowledge of the similarity in quality of particles within a dataset. When the ICUF is set to a value of 1, this reflects that "junk" particles look very different from good particles within the same dataset. On the other hand, a larger ICUF means that the "junk" may look very similar to good particles, and therefore the algorithm should at first be more uncertain about assigning particles to classes. Modifying this parameter instructs the optimization algorithm to search for 2D classes that are more similar (ICUF large) or less similar (ICUF small) to each other.
  • Depending on the results, in subsequent rounds of 2D Classification, you may wish to adjust the following to achieve better visual class averages:

    • Use a different noise model: Prepare a fresh 2D Classification experiment, and on the Parameters page, click on the Show Advanced Params for 2D Classification. Set Use white noise model to false. This will use a coloured noise model, which can help in tricky cases.
    • Marginalize over poses: Set Force Max over poses/shifts to false The algorithm will marginalize over poses which can help achieve better 2D classes especially for very small molecules.
    • Use clamp-solvent: If the classes appear to have a lot of unwanted artefacts in the background, you can use a special optimization method to ensure that all classes will have a blank background. Set Use clamp-solvent to solve 2D classes to true.

Output:

  • Class averages
  • Particles

Notes:

Limitations:

Common Next Steps:

  • Select 2D Classes

Select 2D Classes

Description: Interactively select 2D classes from the output of 2D classification in cryoSPARC.

Input:

  • Class averages
  • Particles

Common Parameters:

Output:

  • Selected class averages
  • Selected particles
  • Excluded class averages
  • Excluded particles

Notes:

Click on each "good" class to select it. You can use both the number of particles and the provided class resolution score to identify good classes of particles. There are several ways to sort the classes in ascending or descending order based on:

  • # of particles: The total number of particles in each class
  • Resolution: The relative resolution of all particles in the class (Å)
  • ECA: Effective Classes Assigned

Avoid selecting classes that contain only a partial particle or a non-particle junk image.

Limitations:

Common Next Steps:

  • Subsequent rounds of 2D Classification using the selected particles with varied number of classes to continue removing junk particles
  • Ab-initio reconstruction to generate one or more initial models from the selected particles

3.8 3D Reconstruction

Ab-initio Reconstruction

Description: Reconstruct a single (homogeneous) or multiple (heterogeneous) 3D maps from a set of particles, without any initial models or starting structures required. (Patent pending)

Input:

  • Particles
  • Initial model (optional)

Common Parameters:

  • Number of classes: The default number of classes is 1. Increase this value if you expect to find multiple conformations/states in your data, or if you are trying ab-initio reconstruction to identify junk particles.

Output:

  • 3D map(s)
  • Plots, including orientation distributions

Notes:

  • Use ab-initio reconstruction to reconstruct one or more 3D maps from a set of particles, without any initial model required. You can also use this job to identify junk particles from the raw particle stack or following 2D classification.
  • If no initial map is provided, it is possible to discover 3-D classes that are significantly different. If an initial map is provided, it will be used as the starting structure for all classes.
  • When performing an ab-initio reconstruction on a symmetric structure, we recommend not enforcing symmetry.
  • For more information on ab-initio reconstruction, please see Nature Methods.

Limitations: Ab-initio reconstruction can fail when data quality is poor or when viewing directions are missing or strongly biased. Highly symmetric structures can also pose challenges. It is not recommended to enforce symmetry during ab-initio reconstruction but can be helpful when symmetry is known in advance.

Common Next Steps:

  • Homogeneous or Heterogeneous Refinement

3D Variability Analysis New Beta

Description: Compute the principle modes of variability with a dataset of aligned particles

Input:

  • Particles
  • Mask

Common Parameters:

  • Number of modes to solve: The number of orthogonal principle modes (i.e. eigenvectors of the 3D covariance) to solve.
  • Filter Resolution (A): Resolution at which results are filtered. Typically set between 5 and 10.

Output:

  • Particles
  • Volume

Common Next Steps:

  • Running a 3D Variability Display job to generate a series of volumes representing frames of motion.

3D Variability Display New Beta

Description: Create various versions of a 3D variability result that can be used for display

Input:

  • Particles (from 3D Variability Analysis)
  • Volume (from 3D Variability Analysis)

Common Parameters:

  • Output mode: simple mode: output a simple linear "movie" of volumes along each dimension. Number of frames is the next param. cluster mode: fit clusters to reaction coordinates and output volumes and particles from each cluster. Number of clusters is the next param. intermediates mode - reconstruct multiple intermediate volumes along each variability dimension, useful for better visualizing non-linear changes. Number of intermediate frames is the next param. endpoints mode - split particles in two parts along each dimension and reconstruct, useful for creating initial volumes for classification/hetero refinement. Always two outputs per dimension.

Output:

  • A series of frames for each component of the variability detected in the dataset, each as a .zip file

Common Next Steps:

  • Visualization of results through UCSF Chimera.

3.9 Refinement

Homogeneous Refinement

Description: Rapidly refine a single homogeneous structure to high-resolution and validate using the gold-standard FSC. (US Patent No. 9,830,732)

Input:

  • Initial model
  • Particles
  • Mask (optional)

Common Parameters:

  • Refinement box size (voxels):
  • Symmetry: You may wish to enforce a particular type of symmetry during refinement. Some common examples are T2 (2-fold tetrahedral), O3 (3-fold octahedral), D7 (7-fold dihedral), I2 (2-fold icosahedral), and C1 (1-fold cyclic, meaning no symmetry).

Output:

  • Refined 3D map
  • Half-maps
  • Mask used in refinement
  • Mask used in FSC calculation
  • Gold-standard FSC curve
  • Plots, including orientation distribution

Notes:

  • This job is generally used to refine maps that were output from ab-initio reconstruction, using new optimized codepaths and GPU kernels. By default, this experiment uses dynamic masking to automatically generate a soft mask used during refinement. Thresholds and distances for this dynamic mask can be set, or a user-provided mask can be specified instead, if necessary.

Limitations: The initial model provided must be on the correct grey scale. Outputs from ab-initio reconstruction in cryoSPARC meet this requirement.

Common Next Steps:

  • Download and inspect map

Heterogeneous Refinement (3D Classification)

Description: Heterogeneous Refinement simultaneously classifies particles and refines structures from n initial structures, usually obtained following an Ab-Initio Reconstruction. This facilitates the ability to look for small differences between structures which may not be obvious at low resolutions, and also to re-classify particles to aid in sorting. (US Patent No. 9,830,732)

Input:

  • Initial models
  • Particles

Common Parameters:

  • Refinement box size (voxels):
  • Symmetry: You may wish to enforce a particular type of symmetry during refinement. Common types of symmetry include T (tetrahedral), O (octahedral), I (icosahedral), D (dihedral) and C (cyclic).

Output:

  • Refined 3D maps
  • Half-maps
  • Mask used in refinement
  • Mask used in FSC calculation
  • Gold-standard FSC curve
  • Plots, including orientation distribution

Notes: This task enables simultaneously sorting particles and identifying classes and is particularly useful in cases where identified classes look very similar. This task can also be used as a method to remove “junk” particles.

Limitations: The initial models provided must be on the correct grey scale. Outputs from ab-initio reconstruction in cryoSPARC meet this requirement.

Common Next Steps:

  • Download and inspect map

Non-uniform Refinement Beta

Description: Apply non-uniform refinement to achieve higher resolution and map quality, especially for membrane proteins. Non-uniform refinement iteratively accounts for regions of a structure that have disordered or flexible density causing local loss of resolution. Accounting for these regions and dynamically estimating their locations can significantly improve resolution in other regions as well as overall map quality by impacting the alignment of particles and reducing the tendency for refinement algorithms to over-fit disordered regions. (Patent pending)

Input:

  • Initial model
  • Particles
  • Mask (optional)

Common Parameters:

  • Refinement box size (voxels):
  • Symmetry: You may wish to enforce a particular type of symmetry during refinement. Common types of symmetry include T (tetrahedral), O (octahedral), I (icosahedral), and C (cyclic).

Output:

  • Refined 3D maps
  • Half-maps
  • Mask used in refinement
  • Mask used in FSC calculation
  • Gold-standard FSC curve
  • Plots, including orientation distribution

Common Next Steps:

  • Download and inspect map

3.10 Post-processing

Local Resolution Estimation

Description: Compute a local resolution map from the output of a refinement job.

Input:

  • Half maps of a refined volume, usually from a Homogenous/Heterogenous/NU-refinement job

Common Parameters:

  • Annealing Factor should be 0 for local resolution, increasing this to 1 will give the global resolution

Output:

  • map_locres contains the resolution of each voxel of the original structure

Notes:

  • To accurately view map_locres, you can use the Surface Color tool in UCSF Chimera to color the input map by the local resolution map. This is the best way to visualize the resolution at different parts of the structure.

Local Filtering

Description: Locally filter a refined map using a local resolution map.

Align 3D Maps

Description: Use this tool to align two or more 3D maps to a common reference in order to better analyze maps obtained from different experiments. The maps need not come from the same type of job, e.g., you could align one coarse resolution map obtained from Ab-Initio reconstruction with a higher resolution map obtained from Refinement.

ResLog Analysis

Description: This feature generates plots that show how the resolution of a given structure increases as more particles are added to the reconstruction. This insight may be useful in determining whether a higher-resolution result is possible with more particles, or if fewer particles are needed to achieve the same resolution.

ThreeDFSC (Wrapper)

Description: You can use this tool, developed by the NYSBC to visualize directional FSCs of your structure. Please see the ThreeDFSC License Terms in the Notes.

Output:

  • The histogram of FSCs will be outputted on the streamlog
  • Follow the instructions in the streamlog to access the full outputs of the ThreeDFSC software package through Chimera.

Notes:

MIT License

Copyright (c) 2017  New York Structural Biology Center, Salk Institute for Biological Studies Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Limitations:

  • The full functionality of ThreeDFSC is not currently implemented in this wrapper. The remaining functions will be added in future updates.

3.11 Utilities

Volume Tools

Description: You can use this tool to resample, or flip a volume.

Input:

  • A map volume

Output:

  • An edited version of the input map volume

Sharpening Tools

Description: You can use this tool to sharpen a volume following refinement.

Input:

  • A map volume

Output:

  • A sharpened map

3.12 Local Refinement Beta

For a detailed overview of local refinement and particle subtraction in cryoSPARC v2.2, please refer to this post and a detailed case study.

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